Podcasts about ai winter

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Best podcasts about ai winter

Latent Space: The AI Engineer Podcast â€” CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

5 episodes with ai winter

Data Science at Home

4 episodes with ai winter

Software Engineering Radio - The Podcast for Professional Software Developers

3 episodes with ai winter

AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

3 episodes with ai winter

The Nonlinear Library

3 episodes with ai winter

Doppelgänger Tech Talk

3 episodes with ai winter

Data Transformers Podcast

2 episodes with ai winter

The Machine Learning Podcast

2 episodes with ai winter

XR for Business

2 episodes with ai winter

Latest podcast episodes about ai winter

SN 1027: Artificial Intelligence - The Status of Encrypted Client Hello

Security Now (MP3)

Play Episode Listen Later May 28, 2025 174:28

What the status of Encrypted Client Hello (ECH)? What radio technology would be best for remote inverter shutdown? Some DNS providers already block newly listed domains. Knowing when not to click a link can take true understanding. Why can losing a small portion of a power grid bring the rest down? Where are we in the "AI Hype Cycle" and is this the first? Speaking of hype: An AI system resorted to blackmail? Why are we so quick to imbue AI with awareness? ChatGPT's latest o3 model ignored the order to shutdown. Copilot may not be making Windows core code any better. Venice.AI is an unfiltered and unrestrained LLM Show Notes - https://www.grc.com/sn/SN-1027-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: outsystems.com/twit threatlocker.com for Security Now canary.tools/twit - use code: TWIT hoxhunt.com/securitynow 1password.com/securitynow

Play Episode Listen Later May 28, 2025 174:28 Transcription Available

SN 1027: Artificial Intelligence - The Status of Encrypted Client Hello

Security Now (Video LO)

Play Episode Listen Later May 28, 2025

Security Now 1027: Artificial Intelligence

All TWiT.tv Shows (Video LO)

Play Episode Listen Later May 28, 2025 174:28 Transcription Available

Security Now 1027: Artificial Intelligence

Radio Leo (Video HD)

Play Episode Listen Later May 28, 2025 174:28 Transcription Available

From College Teacher to Global AI Expert | Ep. 264

The Doers Nepal -Podcast

Play Episode Listen Later May 20, 2025 99:16

ai college global vice president teacher dangers cybersecurity joke nepal customer service big data wells fargo ml genai debugging real risk saurav ai winter

Episode 96: Trends in 2025

SalesX und Innovation

Play Episode Listen Later Jan 9, 2025 34:27

Patrick und Jörg nehmen einen Ausblick auf 2025 vor. Was sind die Erwartungen an 2025? Wird sich die Stagnation Deutschlands und z.B. die Kosteneinsparungen der Migros auf die Schweizer Unternehmen negativ auswirken? Wie gehen die Unternehmen mit der zu erwartende Stärkung des Schweizer Frankens um? Warum braucht es immer «Krisen» für Veränderungen? Gibt es das grosse Erwachen in Bezug auf den desolaten Zustand der IT-Infrastruktur mit Bezug auf die Kundendaten? Wie kann es gelingen IT und Vertrieb gleichzeitig zu optimieren, vor dem Hintergrund des steigenden Marktdrucks? Gibt es bald einen weiteren AI-Winter oder geht der Hype weiter? Wertvolle Erkenntnisse, wie immer aus der Sendeanstalt Beromünster.

hype gibt unternehmen erwartungen ausblick bezug hintergrund krisen zustand vertrieb erwachen migros kundendaten it infrastruktur schweizer unternehmen ai winter kosteneinsparungen

Latent.Space 2024 Year in Review

Latent Space: The AI Engineer Podcast â€” CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

Play Episode Listen Later Dec 31, 2024 111:07

Applications for the 2025 AI Engineer Summit are up, and you can save the date for AIE Singapore in April and AIE World's Fair 2025 in June.Happy new year, and thanks for 100 great episodes! Please let us know what you want to see/hear for the next 100!Full YouTube Episode with Slides/ChartsLike and subscribe and hit that bell to get notifs!Timestamps* 00:00 Welcome to the 100th Episode!* 00:19 Reflecting on the Journey* 00:47 AI Engineering: The Rise and Impact* 03:15 Latent Space Live and AI Conferences* 09:44 The Competitive AI Landscape* 21:45 Synthetic Data and Future Trends* 35:53 Creative Writing with AI* 36:12 Legal and Ethical Issues in AI* 38:18 The Data War: GPU Poor vs. GPU Rich* 39:12 The Rise of GPU Ultra Rich* 40:47 Emerging Trends in AI Models* 45:31 The Multi-Modality War* 01:05:31 The Future of AI Benchmarks* 01:13:17 Pionote and Frontier Models* 01:13:47 Niche Models and Base Models* 01:14:30 State Space Models and RWKB* 01:15:48 Inference Race and Price Wars* 01:22:16 Major AI Themes of the Year* 01:22:48 AI Rewind: January to March* 01:26:42 AI Rewind: April to June* 01:33:12 AI Rewind: July to September* 01:34:59 AI Rewind: October to December* 01:39:53 Year-End Reflections and PredictionsTranscript[00:00:00] Welcome to the 100th Episode![00:00:00] Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co host Swyx for the 100th time today.[00:00:12] swyx: Yay, um, and we're so glad that, yeah, you know, everyone has, uh, followed us in this journey. How do you feel about it? 100 episodes.[00:00:19] Alessio: Yeah, I know.[00:00:19] Reflecting on the Journey[00:00:19] Alessio: Almost two years that we've been doing this. We've had four different studios. Uh, we've had a lot of changes. You know, we used to do this lightning round. When we first started that we didn't like, and we tried to change the question. The answer[00:00:32] swyx: was cursor and perplexity.[00:00:34] Alessio: Yeah, I love mid journey. It's like, do you really not like anything else?[00:00:38] Alessio: Like what's, what's the unique thing? And I think, yeah, we, we've also had a lot more research driven content. You know, we had like 3DAO, we had, you know. Jeremy Howard, we had more folks like that.[00:00:47] AI Engineering: The Rise and Impact[00:00:47] Alessio: I think we want to do more of that too in the new year, like having, uh, some of the Gemini folks, both on the research and the applied side.[00:00:54] Alessio: Yeah, but it's been a ton of fun. I think we both started, I wouldn't say as a joke, we were kind of like, Oh, we [00:01:00] should do a podcast. And I think we kind of caught the right wave, obviously. And I think your rise of the AI engineer posts just kind of get people. Sombra to congregate, and then the AI engineer summit.[00:01:11] Alessio: And that's why when I look at our growth chart, it's kind of like a proxy for like the AI engineering industry as a whole, which is almost like, like, even if we don't do that much, we keep growing just because there's so many more AI engineers. So did you expect that growth or did you expect that would take longer for like the AI engineer thing to kind of like become, you know, everybody talks about it today.[00:01:32] swyx: So, the sign of that, that we have won is that Gartner puts it at the top of the hype curve right now. So Gartner has called the peak in AI engineering. I did not expect, um, to what level. I knew that I was correct when I called it because I did like two months of work going into that. But I didn't know, You know, how quickly it could happen, and obviously there's a chance that I could be wrong.[00:01:52] swyx: But I think, like, most people have come around to that concept. Hacker News hates it, which is a good sign. But there's enough people that have defined it, you know, GitHub, when [00:02:00] they launched GitHub Models, which is the Hugging Face clone, they put AI engineers in the banner, like, above the fold, like, in big So I think it's like kind of arrived as a meaningful and useful definition.[00:02:12] swyx: I think people are trying to figure out where the boundaries are. I think that was a lot of the quote unquote drama that happens behind the scenes at the World's Fair in June. Because I think there's a lot of doubt or questions about where ML engineering stops and AI engineering starts. That's a useful debate to be had.[00:02:29] swyx: In some sense, I actually anticipated that as well. So I intentionally did not. Put a firm definition there because most of the successful definitions are necessarily underspecified and it's actually useful to have different perspectives and you don't have to specify everything from the outset.[00:02:45] Alessio: Yeah, I was at um, AWS reInvent and the line to get into like the AI engineering talk, so to speak, which is, you know, applied AI and whatnot was like, there are like hundreds of people just in line to go in.[00:02:56] Alessio: I think that's kind of what enabled me. People, right? Which is what [00:03:00] you kind of talked about. It's like, Hey, look, you don't actually need a PhD, just, yeah, just use the model. And then maybe we'll talk about some of the blind spots that you get as an engineer with the earlier posts that we also had on on the sub stack.[00:03:11] Alessio: But yeah, it's been a heck of a heck of a two years.[00:03:14] swyx: Yeah.[00:03:15] Latent Space Live and AI Conferences[00:03:15] swyx: You know, I was, I was trying to view the conference as like, so NeurIPS is I think like 16, 17, 000 people. And the Latent Space Live event that we held there was 950 signups. I think. The AI world, the ML world is still very much research heavy. And that's as it should be because ML is very much in a research phase.[00:03:34] swyx: But as we move this entire field into production, I think that ratio inverts into becoming more engineering heavy. So at least I think engineering should be on the same level, even if it's never as prestigious, like it'll always be low status because at the end of the day, you're manipulating APIs or whatever.[00:03:51] swyx: But Yeah, wrapping GPTs, but there's going to be an increasing stack and an art to doing these, these things well. And I, you know, I [00:04:00] think that's what we're focusing on for the podcast, the conference and basically everything I do seems to make sense. And I think we'll, we'll talk about the trends here that apply.[00:04:09] swyx: It's, it's just very strange. So, like, there's a mix of, like, keeping on top of research while not being a researcher and then putting that research into production. So, like, people always ask me, like, why are you covering Neuralibs? Like, this is a ML research conference and I'm like, well, yeah, I mean, we're not going to, to like, understand everything Or reproduce every single paper, but the stuff that is being found here is going to make it through into production at some point, you hope.[00:04:32] swyx: And then actually like when I talk to the researchers, they actually get very excited because they're like, oh, you guys are actually caring about how this goes into production and that's what they really really want. The measure of success is previously just peer review, right? Getting 7s and 8s on their um, Academic review conferences and stuff like citations is one metric, but money is a better metric.[00:04:51] Alessio: Money is a better metric. Yeah, and there were about 2200 people on the live stream or something like that. Yeah, yeah. Hundred on the live stream. So [00:05:00] I try my best to moderate, but it was a lot spicier in person with Jonathan and, and Dylan. Yeah, that it was in the chat on YouTube.[00:05:06] swyx: I would say that I actually also created.[00:05:09] swyx: Layen Space Live in order to address flaws that are perceived in academic conferences. This is not NeurIPS specific, it's ICML, NeurIPS. Basically, it's very sort of oriented towards the PhD student, uh, market, job market, right? Like literally all, basically everyone's there to advertise their research and skills and get jobs.[00:05:28] swyx: And then obviously all the, the companies go there to hire them. And I think that's great for the individual researchers, but for people going there to get info is not great because you have to read between the lines, bring a ton of context in order to understand every single paper. So what is missing is effectively what I ended up doing, which is domain by domain, go through and recap the best of the year.[00:05:48] swyx: Survey the field. And there are, like NeurIPS had a, uh, I think ICML had a like a position paper track, NeurIPS added a benchmarks, uh, datasets track. These are ways in which to address that [00:06:00] issue. Uh, there's always workshops as well. Every, every conference has, you know, a last day of workshops and stuff that provide more of an overview.[00:06:06] swyx: But they're not specifically prompted to do so. And I think really, uh, Organizing a conference is just about getting good speakers and giving them the correct prompts. And then they will just go and do that thing and they do a very good job of it. So I think Sarah did a fantastic job with the startups prompt.[00:06:21] swyx: I can't list everybody, but we did best of 2024 in startups, vision, open models. Post transformers, synthetic data, small models, and agents. And then the last one was the, uh, and then we also did a quick one on reasoning with Nathan Lambert. And then the last one, obviously, was the debate that people were very hyped about.[00:06:39] swyx: It was very awkward. And I'm really, really thankful for John Franco, basically, who stepped up to challenge Dylan. Because Dylan was like, yeah, I'll do it. But He was pro scaling. And I think everyone who is like in AI is pro scaling, right? So you need somebody who's ready to publicly say, no, we've hit a wall.[00:06:57] swyx: So that means you're saying Sam Altman's wrong. [00:07:00] You're saying, um, you know, everyone else is wrong. It helps that this was the day before Ilya went on, went up on stage and then said pre training has hit a wall. And data has hit a wall. So actually Jonathan ended up winning, and then Ilya supported that statement, and then Noam Brown on the last day further supported that statement as well.[00:07:17] swyx: So it's kind of interesting that I think the consensus kind of going in was that we're not done scaling, like you should believe in a better lesson. And then, four straight days in a row, you had Sepp Hochreiter, who is the creator of the LSTM, along with everyone's favorite OG in AI, which is Juergen Schmidhuber.[00:07:34] swyx: He said that, um, we're pre trading inside a wall, or like, we've run into a different kind of wall. And then we have, you know John Frankel, Ilya, and then Noam Brown are all saying variations of the same thing, that we have hit some kind of wall in the status quo of what pre trained, scaling large pre trained models has looked like, and we need a new thing.[00:07:54] swyx: And obviously the new thing for people is some make, either people are calling it inference time compute or test time [00:08:00] compute. I think the collective terminology has been inference time, and I think that makes sense because test time, calling it test, meaning, has a very pre trained bias, meaning that the only reason for running inference at all is to test your model.[00:08:11] swyx: That is not true. Right. Yeah. So, so, I quite agree that. OpenAI seems to have adopted, or the community seems to have adopted this terminology of ITC instead of TTC. And that, that makes a lot of sense because like now we care about inference, even right down to compute optimality. Like I actually interviewed this author who recovered or reviewed the Chinchilla paper.[00:08:31] swyx: Chinchilla paper is compute optimal training, but what is not stated in there is it's pre trained compute optimal training. And once you start caring about inference, compute optimal training, you have a different scaling law. And in a way that we did not know last year.[00:08:45] Alessio: I wonder, because John is, he's also on the side of attention is all you need.[00:08:49] Alessio: Like he had the bet with Sasha. So I'm curious, like he doesn't believe in scaling, but he thinks the transformer, I wonder if he's still. So, so,[00:08:56] swyx: so he, obviously everything is nuanced and you know, I told him to play a character [00:09:00] for this debate, right? So he actually does. Yeah. He still, he still believes that we can scale more.[00:09:04] swyx: Uh, he just assumed the character to be very game for, for playing this debate. So even more kudos to him that he assumed a position that he didn't believe in and still won the debate.[00:09:16] Alessio: Get rekt, Dylan. Um, do you just want to quickly run through some of these things? Like, uh, Sarah's presentation, just the highlights.[00:09:24] swyx: Yeah, we can't go through everyone's slides, but I pulled out some things as a factor of, like, stuff that we were going to talk about. And we'll[00:09:30] Alessio: publish[00:09:31] swyx: the rest. Yeah, we'll publish on this feed the best of 2024 in those domains. And hopefully people can benefit from the work that our speakers have done.[00:09:39] swyx: But I think it's, uh, these are just good slides. And I've been, I've been looking for a sort of end of year recaps from, from people.[00:09:44] The Competitive AI Landscape[00:09:44] swyx: The field has progressed a lot. You know, I think the max ELO in 2023 on LMSys used to be 1200 for LMSys ELOs. And now everyone is at least at, uh, 1275 in their ELOs, and this is across Gemini, Chadjibuti, [00:10:00] Grok, O1.[00:10:01] swyx: ai, which with their E Large model, and Enthopic, of course. It's a very, very competitive race. There are multiple Frontier labs all racing, but there is a clear tier zero Frontier. And then there's like a tier one. It's like, I wish I had everything else. Tier zero is extremely competitive. It's effectively now three horse race between Gemini, uh, Anthropic and OpenAI.[00:10:21] swyx: I would say that people are still holding out a candle for XAI. XAI, I think, for some reason, because their API was very slow to roll out, is not included in these metrics. So it's actually quite hard to put on there. As someone who also does charts, XAI is continually snubbed because they don't work well with the benchmarking people.[00:10:42] swyx: Yeah, yeah, yeah. It's a little trivia for why XAI always gets ignored. The other thing is market share. So these are slides from Sarah. We have it up on the screen. It has gone from very heavily open AI. So we have some numbers and estimates. These are from RAMP. Estimates of open AI market share in [00:11:00] December 2023.[00:11:01] swyx: And this is basically, what is it, GPT being 95 percent of production traffic. And I think if you correlate that with stuff that we asked. Harrison Chase on the LangChain episode, it was true. And then CLAUD 3 launched mid middle of this year. I think CLAUD 3 launched in March, CLAUD 3. 5 Sonnet was in June ish.[00:11:23] swyx: And you can start seeing the market share shift towards opening, uh, towards that topic, uh, very, very aggressively. The more recent one is Gemini. So if I scroll down a little bit, this is an even more recent dataset. So RAM's dataset ends in September 2 2. 2024. Gemini has basically launched a price war at the low end, uh, with Gemini Flash, uh, being basically free for personal use.[00:11:44] swyx: Like, I think people don't understand the free tier. It's something like a billion tokens per day. Unless you're trying to abuse it, you cannot really exhaust your free tier on Gemini. They're really trying to get you to use it. They know they're in like third place, um, fourth place, depending how you, how you count.[00:11:58] swyx: And so they're going after [00:12:00] the Lower tier first, and then, you know, maybe the upper tier later, but yeah, Gemini Flash, according to OpenRouter, is now 50 percent of their OpenRouter requests. Obviously, these are the small requests. These are small, cheap requests that are mathematically going to be more.[00:12:15] swyx: The smart ones obviously are still going to OpenAI. But, you know, it's a very, very big shift in the market. Like basically 2023, 2022, To going into 2024 opening has gone from nine five market share to Yeah. Reasonably somewhere between 50 to 75 market share.[00:12:29] Alessio: Yeah. I'm really curious how ramped does the attribution to the model?[00:12:32] Alessio: If it's API, because I think it's all credit card spin. . Well, but it's all, the credit card doesn't say maybe. Maybe the, maybe when they do expenses, they upload the PDF, but yeah, the, the German I think makes sense. I think that was one of my main 2024 takeaways that like. The best small model companies are the large labs, which is not something I would have thought that the open source kind of like long tail would be like the small model.[00:12:53] swyx: Yeah, different sizes of small models we're talking about here, right? Like so small model here for Gemini is AB, [00:13:00] right? Uh, mini. We don't know what the small model size is, but yeah, it's probably in the double digits or maybe single digits, but probably double digits. The open source community has kind of focused on the one to three B size.[00:13:11] swyx: Mm-hmm . Yeah. Maybe[00:13:12] swyx: zero, maybe 0.5 B uh, that's moon dream and that is small for you then, then that's great. It makes sense that we, we have a range for small now, which is like, may, maybe one to five B. Yeah. I'll even put that at, at, at the high end. And so this includes Gemma from Gemini as well. But also includes the Apple Foundation models, which I think Apple Foundation is 3B.[00:13:32] Alessio: Yeah. No, that's great. I mean, I think in the start small just meant cheap. I think today small is actually a more nuanced discussion, you know, that people weren't really having before.[00:13:43] swyx: Yeah, we can keep going. This is a slide that I smiley disagree with Sarah. She's pointing to the scale SEAL leaderboard. I think the Researchers that I talked with at NeurIPS were kind of positive on this because basically you need private test [00:14:00] sets to prevent contamination.[00:14:02] swyx: And Scale is one of maybe three or four people this year that has really made an effort in doing a credible private test set leaderboard. Llama405B does well compared to Gemini and GPT 40. And I think that's good. I would say that. You know, it's good to have an open model that is that big, that does well on those metrics.[00:14:23] swyx: But anyone putting 405B in production will tell you, if you scroll down a little bit to the artificial analysis numbers, that it is very slow and very expensive to infer. Um, it doesn't even fit on like one node. of, uh, of H100s. Cerebras will be happy to tell you they can serve 4 or 5B on their super large chips.[00:14:42] swyx: But, um, you know, if you need to do anything custom to it, you're still kind of constrained. So, is 4 or 5B really that relevant? Like, I think most people are basically saying that they only use 4 or 5B as a teacher model to distill down to something. Even Meta is doing it. So with Lama 3. [00:15:00] 3 launched, they only launched the 70B because they use 4 or 5B to distill the 70B.[00:15:03] swyx: So I don't know if like open source is keeping up. I think they're the, the open source industrial complex is very invested in telling you that the, if the gap is narrowing, I kind of disagree. I think that the gap is widening with O1. I think there are very, very smart people trying to narrow that gap and they should.[00:15:22] swyx: I really wish them success, but you cannot use a chart that is nearing 100 in your saturation chart. And look, the distance between open source and closed source is narrowing. Of course it's going to narrow because you're near 100. This is stupid. But in metrics that matter, is open source narrowing?[00:15:38] swyx: Probably not for O1 for a while. And it's really up to the open source guys to figure out if they can match O1 or not.[00:15:46] Alessio: I think inference time compute is bad for open source just because, you know, Doc can donate the flops at training time, but he cannot donate the flops at inference time. So it's really hard to like actually keep up on that axis.[00:15:59] Alessio: Big, big business [00:16:00] model shift. So I don't know what that means for the GPU clouds. I don't know what that means for the hyperscalers, but obviously the big labs have a lot of advantage. Because, like, it's not a static artifact that you're putting the compute in. You're kind of doing that still, but then you're putting a lot of computed inference too.[00:16:17] swyx: Yeah, yeah, yeah. Um, I mean, Llama4 will be reasoning oriented. We talked with Thomas Shalom. Um, kudos for getting that episode together. That was really nice. Good, well timed. Actually, I connected with the AI meta guy, uh, at NeurIPS, and, um, yeah, we're going to coordinate something for Llama4. Yeah, yeah,[00:16:32] Alessio: and our friend, yeah.[00:16:33] Alessio: Clara Shi just joined to lead the business agent side. So I'm sure we'll have her on in the new year.[00:16:39] swyx: Yeah. So, um, my comment on, on the business model shift, this is super interesting. Apparently it is wide knowledge that OpenAI wanted more than 6. 6 billion dollars for their fundraise. They wanted to raise, you know, higher, and they did not.[00:16:51] swyx: And what that means is basically like, it's very convenient that we're not getting GPT 5, which would have been a larger pre train. We should have a lot of upfront money. And [00:17:00] instead we're, we're converting fixed costs into variable costs, right. And passing it on effectively to the customer. And it's so much easier to take margin there because you can directly attribute it to like, Oh, you're using this more.[00:17:12] swyx: Therefore you, you pay more of the cost and I'll just slap a margin in there. So like that lets you control your growth margin and like tie your. Your spend, or your sort of inference spend, accordingly. And it's just really interesting to, that this change in the sort of inference paradigm has arrived exactly at the same time that the funding environment for pre training is effectively drying up, kind of.[00:17:36] swyx: I feel like maybe the VCs are very in tune with research anyway, so like, they would have noticed this, but, um, it's just interesting.[00:17:43] Alessio: Yeah, and I was looking back at our yearly recap of last year. Yeah. And the big thing was like the mixed trial price fights, you know, and I think now it's almost like there's nowhere to go, like, you know, Gemini Flash is like basically giving it away for free.[00:17:55] Alessio: So I think this is a good way for the labs to generate more revenue and pass down [00:18:00] some of the compute to the customer. I think they're going to[00:18:02] swyx: keep going. I think that 2, will come.[00:18:05] Alessio: Yeah, I know. Totally. I mean, next year, the first thing I'm doing is signing up for Devin. Signing up for the pro chat GBT.[00:18:12] Alessio: Just to try. I just want to see what does it look like to spend a thousand dollars a month on AI?[00:18:17] swyx: Yes. Yes. I think if your, if your, your job is a, at least AI content creator or VC or, you know, someone who, whose job it is to stay on, stay on top of things, you should already be spending like a thousand dollars a month on, on stuff.[00:18:28] swyx: And then obviously easy to spend, hard to use. You have to actually use. The good thing is that actually Google lets you do a lot of stuff for free now. So like deep research. That they just launched. Uses a ton of inference and it's, it's free while it's in preview.[00:18:45] Alessio: Yeah. They need to put that in Lindy.[00:18:47] Alessio: I've been using Lindy lately. I've been a built a bunch of things once we had flow because I liked the new thing. It's pretty good. I even did a phone call assistant. Um, yeah, they just launched Lindy voice. Yeah, I think once [00:19:00] they get advanced voice mode like capability today, still like speech to text, you can kind of tell.[00:19:06] Alessio: Um, but it's good for like reservations and things like that. So I have a meeting prepper thing. And so[00:19:13] swyx: it's good. Okay. I feel like we've, we've covered a lot of stuff. Uh, I, yeah, I, you know, I think We will go over the individual, uh, talks in a separate episode. Uh, I don't want to take too much time with, uh, this stuff, but that suffice to say that there is a lot of progress in each field.[00:19:28] swyx: Uh, we covered vision. Basically this is all like the audience voting for what they wanted. And then I just invited the best people I could find in each audience, especially agents. Um, Graham, who I talked to at ICML in Vienna, he is currently still number one. It's very hard to stay on top of SweetBench.[00:19:45] swyx: OpenHand is currently still number one. switchbench full, which is the hardest one. He had very good thoughts on agents, which I, which I'll highlight for people. Everyone is saying 2025 is the year of agents, just like they said last year. And, uh, but he had [00:20:00] thoughts on like eight parts of what are the frontier problems to solve in agents.[00:20:03] swyx: And so I'll highlight that talk as well.[00:20:05] Alessio: Yeah. The number six, which is the Hacken agents learn more about the environment, has been a Super interesting to us as well, just to think through, because, yeah, how do you put an agent in an enterprise where most things in an enterprise have never been public, you know, a lot of the tooling, like the code bases and things like that.[00:20:23] Alessio: So, yeah, there's not indexing and reg. Well, yeah, but it's more like. You can't really rag things that are not documented. But people know them based on how they've been doing it. You know, so I think there's almost this like, you know, Oh, institutional knowledge. Yeah, the boring word is kind of like a business process extraction.[00:20:38] Alessio: Yeah yeah, I see. It's like, how do you actually understand how these things are done? I see. Um, and I think today the, the problem is that, Yeah, the agents are, that most people are building are good at following instruction, but are not as good as like extracting them from you. Um, so I think that will be a big unlock just to touch quickly on the Jeff Dean thing.[00:20:55] Alessio: I thought it was pretty, I mean, we'll link it in the, in the things, but. I think the main [00:21:00] focus was like, how do you use ML to optimize the systems instead of just focusing on ML to do something else? Yeah, I think speculative decoding, we had, you know, Eugene from RWKB on the podcast before, like he's doing a lot of that with Fetterless AI.[00:21:12] swyx: Everyone is. I would say it's the norm. I'm a little bit uncomfortable with how much it costs, because it does use more of the GPU per call. But because everyone is so keen on fast inference, then yeah, makes sense.[00:21:24] Alessio: Exactly. Um, yeah, but we'll link that. Obviously Jeff is great.[00:21:30] swyx: Jeff is, Jeff's talk was more, it wasn't focused on Gemini.[00:21:33] swyx: I think people got the wrong impression from my tweet. It's more about how Google approaches ML and uses ML to design systems and then systems feedback into ML. And I think this ties in with Lubna's talk.[00:21:45] Synthetic Data and Future Trends[00:21:45] swyx: on synthetic data where it's basically the story of bootstrapping of humans and AI in AI research or AI in production.[00:21:53] swyx: So her talk was on synthetic data, where like how much synthetic data has grown in 2024 in the pre training side, the post training side, [00:22:00] and the eval side. And I think Jeff then also extended it basically to chips, uh, to chip design. So he'd spend a lot of time talking about alpha chip. And most of us in the audience are like, we're not working on hardware, man.[00:22:11] swyx: Like you guys are great. TPU is great. Okay. We'll buy TPUs.[00:22:14] Alessio: And then there was the earlier talk. Yeah. But, and then we have, uh, I don't know if we're calling them essays. What are we calling these? But[00:22:23] swyx: for me, it's just like bonus for late in space supporters, because I feel like they haven't been getting anything.[00:22:29] swyx: And then I wanted a more high frequency way to write stuff. Like that one I wrote in an afternoon. I think basically we now have an answer to what Ilya saw. It's one year since. The blip. And we know what he saw in 2014. We know what he saw in 2024. We think we know what he sees in 2024. He gave some hints and then we have vague indications of what he saw in 2023.[00:22:54] swyx: So that was the Oh, and then 2016 as well, because of this lawsuit with Elon, OpenAI [00:23:00] is publishing emails from Sam's, like, his personal text messages to Siobhan, Zelis, or whatever. So, like, we have emails from Ilya saying, this is what we're seeing in OpenAI, and this is why we need to scale up GPUs. And I think it's very prescient in 2016 to write that.[00:23:16] swyx: And so, like, it is exactly, like, basically his insights. It's him and Greg, basically just kind of driving the scaling up of OpenAI, while they're still playing Dota. They're like, no, like, we see the path here.[00:23:30] Alessio: Yeah, and it's funny, yeah, they even mention, you know, we can only train on 1v1 Dota. We need to train on 5v5, and that takes too many GPUs.[00:23:37] Alessio: Yeah,[00:23:37] swyx: and at least for me, I can speak for myself, like, I didn't see the path from Dota to where we are today. I think even, maybe if you ask them, like, they wouldn't necessarily draw a straight line. Yeah,[00:23:47] Alessio: no, definitely. But I think like that was like the whole idea of almost like the RL and we talked about this with Nathan on his podcast.[00:23:55] Alessio: It's like with RL, you can get very good at specific things, but then you can't really like generalize as much. And I [00:24:00] think the language models are like the opposite, which is like, you're going to throw all this data at them and scale them up, but then you really need to drive them home on a specific task later on.[00:24:08] Alessio: And we'll talk about the open AI reinforcement, fine tuning, um, announcement too, and all of that. But yeah, I think like scale is all you need. That's kind of what Elia will be remembered for. And I think just maybe to clarify on like the pre training is over thing that people love to tweet. I think the point of the talk was like everybody, we're scaling these chips, we're scaling the compute, but like the second ingredient which is data is not scaling at the same rate.[00:24:35] Alessio: So it's not necessarily pre training is over. It's kind of like What got us here won't get us there. In his email, he predicted like 10x growth every two years or something like that. And I think maybe now it's like, you know, you can 10x the chips again, but[00:24:49] swyx: I think it's 10x per year. Was it? I don't know.[00:24:52] Alessio: Exactly. And Moore's law is like 2x. So it's like, you know, much faster than that. And yeah, I like the fossil fuel of AI [00:25:00] analogy. It's kind of like, you know, the little background tokens thing. So the OpenAI reinforcement fine tuning is basically like, instead of fine tuning on data, you fine tune on a reward model.[00:25:09] Alessio: So it's basically like, instead of being data driven, it's like task driven. And I think people have tasks to do, they don't really have a lot of data. So I'm curious to see how that changes, how many people fine tune, because I think this is what people run into. It's like, Oh, you can fine tune llama. And it's like, okay, where do I get the data?[00:25:27] Alessio: To fine tune it on, you know, so it's great that we're moving the thing. And then I really like he had this chart where like, you know, the brain mass and the body mass thing is basically like mammals that scaled linearly by brain and body size, and then humans kind of like broke off the slope. So it's almost like maybe the mammal slope is like the pre training slope.[00:25:46] Alessio: And then the post training slope is like the, the human one.[00:25:49] swyx: Yeah. I wonder what the. I mean, we'll know in 10 years, but I wonder what the y axis is for, for Ilya's SSI. We'll try to get them on.[00:25:57] Alessio: Ilya, if you're listening, you're [00:26:00] welcome here. Yeah, and then he had, you know, what comes next, like agent, synthetic data, inference, compute, I thought all of that was like that.[00:26:05] Alessio: I don't[00:26:05] swyx: think he was dropping any alpha there. Yeah, yeah, yeah.[00:26:07] Alessio: Yeah. Any other new reps? Highlights?[00:26:10] swyx: I think that there was comparatively a lot more work. Oh, by the way, I need to plug that, uh, my friend Yi made this, like, little nice paper. Yeah, that was really[00:26:20] swyx: nice.[00:26:20] swyx: Uh, of, uh, of, like, all the, he's, she called it must read papers of 2024.[00:26:26] swyx: So I laid out some of these at NeurIPS, and it was just gone. Like, everyone just picked it up. Because people are dying for, like, little guidance and visualizations And so, uh, I thought it was really super nice that we got there.[00:26:38] Alessio: Should we do a late in space book for each year? Uh, I thought about it. For each year we should.[00:26:42] Alessio: Coffee table book. Yeah. Yeah. Okay. Put it in the will. Hi, Will. By the way, we haven't introduced you. He's our new, you know, general organist, Jamie. You need to[00:26:52] swyx: pull up more things. One thing I saw that, uh, Okay, one fun one, and then one [00:27:00] more general one. So the fun one is this paper on agent collusion. This is a paper on steganography.[00:27:06] swyx: This is secret collusion among AI agents, multi agent deception via steganography. I tried to go to NeurIPS in order to find these kinds of papers because the real reason Like NeurIPS this year has a lottery system. A lot of people actually even go and don't buy tickets because they just go and attend the side events.[00:27:22] swyx: And then also the people who go and end up crowding around the most popular papers, which you already know and already read them before you showed up to NeurIPS. So the only reason you go there is to talk to the paper authors, but there's like something like 10, 000 other. All these papers out there that, you know, are just people's work that they, that they did on the air and they failed to get attention for one reason or another.[00:27:42] swyx: And this was one of them. Uh, it was like all the way at the back. And this is a deep mind paper that actually focuses on collusion between AI agents, uh, by hiding messages in the text that they generate. Uh, so that's what steganography is. So a very simple example would be the first letter of every word.[00:27:57] swyx: If you Pick that out, you know, and the code sends a [00:28:00] different message than that. But something I've always emphasized is to LLMs, we read left to right. LLMs can read up, down, sideways, you know, in random character order. And it's the same to them as it is to us. So if we were ever to get You know, self motivated, underlined LLMs that we're trying to collaborate to take over the planet.[00:28:19] swyx: This would be how they do it. They spread messages among us in the messages that we generate. And he developed a scaling law for that. So he marked, I'm showing it on screen right now, the emergence of this phenomenon. Basically, for example, for Cypher encoding, GPT 2, Lama 2, mixed trial, GPT 3. 5, zero capabilities, and sudden 4.[00:28:40] swyx: And this is the kind of Jason Wei type emergence properties that people kind of look for. I think what made this paper stand out as well, so he developed the benchmark for steganography collusion, and he also focused on shelling point collusion, which is very low coordination. For agreeing on a decoding encoding format, you kind of need to have some [00:29:00] agreement on that.[00:29:00] swyx: But, but shelling point means like very, very low or almost no coordination. So for example, if I, if I ask someone, if the only message I give you is meet me in New York and you're not aware. Or when you would probably meet me at Grand Central Station. That is the Grand Central Station is a shelling point.[00:29:16] swyx: And it's probably somewhere, somewhere during the day. That is the shelling point of New York is Grand Central. To that extent, shelling points for steganography are things like the, the, the common decoding methods that we talked about. It will be interesting at some point in the future when we are worried about alignment.[00:29:30] swyx: It is not interesting today, but it's interesting that DeepMind is already thinking about this.[00:29:36] Alessio: I think that's like one of the hardest things about NeurIPS. It's like the long tail. I[00:29:41] swyx: found a pricing guy. I'm going to feature him on the podcast. Basically, this guy from NVIDIA worked out the optimal pricing for language models.[00:29:51] swyx: It's basically an econometrics paper at NeurIPS, where everyone else is talking about GPUs. And the guy with the GPUs is[00:29:57] Alessio: talking[00:29:57] swyx: about economics instead. [00:30:00] That was the sort of fun one. So the focus I saw is that model papers at NeurIPS are kind of dead. No one really presents models anymore. It's just data sets.[00:30:12] swyx: This is all the grad students are working on. So like there was a data sets track and then I was looking around like, I was like, you don't need a data sets track because every paper is a data sets paper. And so data sets and benchmarks, they're kind of flip sides of the same thing. So Yeah. Cool. Yeah, if you're a grad student, you're a GPU boy, you kind of work on that.[00:30:30] swyx: And then the, the sort of big model that people walk around and pick the ones that they like, and then they use it in their models. And that's, that's kind of how it develops. I, I feel like, um, like, like you didn't last year, you had people like Hao Tian who worked on Lava, which is take Lama and add Vision.[00:30:47] swyx: And then obviously actually I hired him and he added Vision to Grok. Now he's the Vision Grok guy. This year, I don't think there was any of those.[00:30:55] Alessio: What were the most popular, like, orals? Last year it was like the [00:31:00] Mixed Monarch, I think, was like the most attended. Yeah, uh, I need to look it up. Yeah, I mean, if nothing comes to mind, that's also kind of like an answer in a way.[00:31:10] Alessio: But I think last year there was a lot of interest in, like, furthering models and, like, different architectures and all of that.[00:31:16] swyx: I will say that I felt the orals, oral picks this year were not very good. Either that or maybe it's just a So that's the highlight of how I have changed in terms of how I view papers.[00:31:29] swyx: So like, in my estimation, two of the best papers in this year for datasets or data comp and refined web or fine web. These are two actually industrially used papers, not highlighted for a while. I think DCLM got the spotlight, FineWeb didn't even get the spotlight. So like, it's just that the picks were different.[00:31:48] swyx: But one thing that does get a lot of play that a lot of people are debating is the role that's scheduled. This is the schedule free optimizer paper from Meta from Aaron DeFazio. And this [00:32:00] year in the ML community, there's been a lot of chat about shampoo, soap, all the bathroom amenities for optimizing your learning rates.[00:32:08] swyx: And, uh, most people at the big labs are. Who I asked about this, um, say that it's cute, but it's not something that matters. I don't know, but it's something that was discussed and very, very popular. 4Wars[00:32:19] Alessio: of AI recap maybe, just quickly. Um, where do you want to start? Data?[00:32:26] swyx: So to remind people, this is the 4Wars piece that we did as one of our earlier recaps of this year.[00:32:31] swyx: And the belligerents are on the left, journalists, writers, artists, anyone who owns IP basically, New York Times, Stack Overflow, Reddit, Getty, Sarah Silverman, George RR Martin. Yeah, and I think this year we can add Scarlett Johansson to that side of the fence. So anyone suing, open the eye, basically. I actually wanted to get a snapshot of all the lawsuits.[00:32:52] swyx: I'm sure some lawyer can do it. That's the data quality war. On the right hand side, we have the synthetic data people, and I think we talked about Lumna's talk, you know, [00:33:00] really showing how much synthetic data has come along this year. I think there was a bit of a fight between scale. ai and the synthetic data community, because scale.[00:33:09] swyx: ai published a paper saying that synthetic data doesn't work. Surprise, surprise, scale. ai is the leading vendor of non synthetic data. Only[00:33:17] Alessio: cage free annotated data is useful.[00:33:21] swyx: So I think there's some debate going on there, but I don't think it's much debate anymore that at least synthetic data, for the reasons that are blessed in Luna's talk, Makes sense.[00:33:32] swyx: I don't know if you have any perspectives there.[00:33:34] Alessio: I think, again, going back to the reinforcement fine tuning, I think that will change a little bit how people think about it. I think today people mostly use synthetic data, yeah, for distillation and kind of like fine tuning a smaller model from like a larger model.[00:33:46] Alessio: I'm not super aware of how the frontier labs use it outside of like the rephrase, the web thing that Apple also did. But yeah, I think it'll be. Useful. I think like whether or not that gets us the big [00:34:00] next step, I think that's maybe like TBD, you know, I think people love talking about data because it's like a GPU poor, you know, I think, uh, synthetic data is like something that people can do, you know, so they feel more opinionated about it compared to, yeah, the optimizers stuff, which is like,[00:34:17] swyx: they don't[00:34:17] Alessio: really work[00:34:18] swyx: on.[00:34:18] swyx: I think that there is an angle to the reasoning synthetic data. So this year, we covered in the paper club, the star series of papers. So that's star, Q star, V star. It basically helps you to synthesize reasoning steps, or at least distill reasoning steps from a verifier. And if you look at the OpenAI RFT, API that they released, or that they announced, basically they're asking you to submit graders, or they choose from a preset list of graders.[00:34:49] swyx: Basically It feels like a way to create valid synthetic data for them to fine tune their reasoning paths on. Um, so I think that is another angle where it starts to make sense. And [00:35:00] so like, it's very funny that basically all the data quality wars between Let's say the music industry or like the newspaper publishing industry or the textbooks industry on the big labs.[00:35:11] swyx: It's all of the pre training era. And then like the new era, like the reasoning era, like nobody has any problem with all the reasoning, especially because it's all like sort of math and science oriented with, with very reasonable graders. I think the more interesting next step is how does it generalize beyond STEM?[00:35:27] swyx: We've been using O1 for And I would say like for summarization and creative writing and instruction following, I think it's underrated. I started using O1 in our intro songs before we killed the intro songs, but it's very good at writing lyrics. You know, I can actually say like, I think one of the O1 pro demos.[00:35:46] swyx: All of these things that Noam was showing was that, you know, you can write an entire paragraph or three paragraphs without using the letter A, right?[00:35:53] Creative Writing with AI[00:35:53] swyx: So like, like literally just anything instead of token, like not even token level, character level manipulation and [00:36:00] counting and instruction following. It's, uh, it's very, very strong.[00:36:02] swyx: And so no surprises when I ask it to rhyme, uh, and to, to create song lyrics, it's going to do that very much better than in previous models. So I think it's underrated for creative writing.[00:36:11] Alessio: Yeah.[00:36:12] Legal and Ethical Issues in AI[00:36:12] Alessio: What do you think is the rationale that they're going to have in court when they don't show you the thinking traces of O1, but then they want us to, like, they're getting sued for using other publishers data, you know, but then on their end, they're like, well, you shouldn't be using my data to then train your model.[00:36:29] Alessio: So I'm curious to see how that kind of comes. Yeah, I mean, OPA has[00:36:32] swyx: many ways to publish, to punish people without bringing, taking them to court. Already banned ByteDance for distilling their, their info. And so anyone caught distilling the chain of thought will be just disallowed to continue on, on, on the API.[00:36:44] swyx: And it's fine. It's no big deal. Like, I don't even think that's an issue at all, just because the chain of thoughts are pretty well hidden. Like you have to work very, very hard to, to get it to leak. And then even when it leaks the chain of thought, you don't know if it's, if it's [00:37:00] The bigger concern is actually that there's not that much IP hiding behind it, that Cosign, which we talked about, we talked to him on Dev Day, can just fine tune 4.[00:37:13] swyx: 0 to beat 0. 1 Cloud SONET so far is beating O1 on coding tasks without, at least O1 preview, without being a reasoning model, same for Gemini Pro or Gemini 2. 0. So like, how much is reasoning important? How much of a moat is there in this, like, All of these are proprietary sort of training data that they've presumably accomplished.[00:37:34] swyx: Because even DeepSeek was able to do it. And they had, you know, two months notice to do this, to do R1. So, it's actually unclear how much moat there is. Obviously, you know, if you talk to the Strawberry team, they'll be like, yeah, I mean, we spent the last two years doing this. So, we don't know. And it's going to be Interesting because there'll be a lot of noise from people who say they have inference time compute and actually don't because they just have fancy chain of thought.[00:38:00][00:38:00] swyx: And then there's other people who actually do have very good chain of thought. And you will not see them on the same level as OpenAI because OpenAI has invested a lot in building up the mythology of their team. Um, which makes sense. Like the real answer is somewhere in between.[00:38:13] Alessio: Yeah, I think that's kind of like the main data war story developing.[00:38:18] The Data War: GPU Poor vs. GPU Rich[00:38:18] Alessio: GPU poor versus GPU rich. Yeah. Where do you think we are? I think there was, again, going back to like the small model thing, there was like a time in which the GPU poor were kind of like the rebel faction working on like these models that were like open and small and cheap. And I think today people don't really care as much about GPUs anymore.[00:38:37] Alessio: You also see it in the price of the GPUs. Like, you know, that market is kind of like plummeted because there's people don't want to be, they want to be GPU free. They don't even want to be poor. They just want to be, you know, completely without them. Yeah. How do you think about this war? You[00:38:52] swyx: can tell me about this, but like, I feel like the, the appetite for GPU rich startups, like the, you know, the, the funding plan is we will raise 60 million and [00:39:00] we'll give 50 of that to NVIDIA.[00:39:01] swyx: That is gone, right? Like, no one's, no one's pitching that. This was literally the plan, the exact plan of like, I can name like four or five startups, you know, this time last year. So yeah, GPU rich startups gone.[00:39:12] The Rise of GPU Ultra Rich[00:39:12] swyx: But I think like, The GPU ultra rich, the GPU ultra high net worth is still going. So, um, now we're, you know, we had Leopold's essay on the trillion dollar cluster.[00:39:23] swyx: We're not quite there yet. We have multiple labs, um, you know, XAI very famously, you know, Jensen Huang praising them for being. Best boy number one in spinning up 100, 000 GPU cluster in like 12 days or something. So likewise at Meta, likewise at OpenAI, likewise at the other labs as well. So like the GPU ultra rich are going to keep doing that because I think partially it's an article of faith now that you just need it.[00:39:46] swyx: Like you don't even know what it's going to, what you're going to use it for. You just, you just need it. And it makes sense that if, especially if we're going into. More researchy territory than we are. So let's say 2020 to 2023 was [00:40:00] let's scale big models territory because we had GPT 3 in 2020 and we were like, okay, we'll go from 1.[00:40:05] swyx: 75b to 1. 8b, 1. 8t. And that was GPT 3 to GPT 4. Okay, that's done. As far as everyone is concerned, Opus 3. 5 is not coming out, GPT 4. 5 is not coming out, and Gemini 2, we don't have Pro, whatever. We've hit that wall. Maybe I'll call it the 2 trillion perimeter wall. We're not going to 10 trillion. No one thinks it's a good idea, at least from training costs, from the amount of data, or at least the inference.[00:40:36] swyx: Would you pay 10x the price of GPT Probably not. Like, like you want something else that, that is at least more useful. So it makes sense that people are pivoting in terms of their inference paradigm.[00:40:47] Emerging Trends in AI Models[00:40:47] swyx: And so when it's more researchy, then you actually need more just general purpose compute to mess around with, uh, at the exact same time that production deployments of the old, the previous paradigm is still ramping up,[00:40:58] swyx: um,[00:40:58] swyx: uh, pretty aggressively.[00:40:59] swyx: So [00:41:00] it makes sense that the GPU rich are growing. We have now interviewed both together and fireworks and replicates. Uh, we haven't done any scale yet. But I think Amazon, maybe kind of a sleeper one, Amazon, in a sense of like they, at reInvent, I wasn't expecting them to do so well, but they are now a foundation model lab.[00:41:18] swyx: It's kind of interesting. Um, I think, uh, you know, David went over there and started just creating models.[00:41:25] Alessio: Yeah, I mean, that's the power of prepaid contracts. I think like a lot of AWS customers, you know, they do this big reserve instance contracts and now they got to use their money. That's why so many startups.[00:41:37] Alessio: Get bought through the AWS marketplace so they can kind of bundle them together and prefer pricing.[00:41:42] swyx: Okay, so maybe GPU super rich doing very well, GPU middle class dead, and then GPU[00:41:48] Alessio: poor. I mean, my thing is like, everybody should just be GPU rich. There shouldn't really be, even the GPU poorest, it's like, does it really make sense to be GPU poor?[00:41:57] Alessio: Like, if you're GPU poor, you should just use the [00:42:00] cloud. Yes, you know, and I think there might be a future once we kind of like figure out what the size and shape of these models is where like the tiny box and these things come to fruition where like you can be GPU poor at home. But I think today is like, why are you working so hard to like get these models to run on like very small clusters where it's like, It's so cheap to run them.[00:42:21] Alessio: Yeah, yeah,[00:42:22] swyx: yeah. I think mostly people think it's cool. People think it's a stepping stone to scaling up. So they aspire to be GPU rich one day and they're working on new methods. Like news research, like probably the most deep tech thing they've done this year is Distro or whatever the new name is.[00:42:38] swyx: There's a lot of interest in heterogeneous computing, distributed computing. I tend generally to de emphasize that historically, but it may be coming to a time where it is starting to be relevant. I don't know. You know, SF compute launched their compute marketplace this year, and like, who's really using that?[00:42:53] swyx: Like, it's a bunch of small clusters, disparate types of compute, and if you can make that [00:43:00] useful, then that will be very beneficial to the broader community, but maybe still not the source of frontier models. It's just going to be a second tier of compute that is unlocked for people, and that's fine. But yeah, I mean, I think this year, I would say a lot more on device, We are, I now have Apple intelligence on my phone.[00:43:19] swyx: Doesn't do anything apart from summarize my notifications. But still, not bad. Like, it's multi modal.[00:43:25] Alessio: Yeah, the notification summaries are so and so in my experience.[00:43:29] swyx: Yeah, but they add, they add juice to life. And then, um, Chrome Nano, uh, Gemini Nano is coming out in Chrome. Uh, they're still feature flagged, but you can, you can try it now if you, if you use the, uh, the alpha.[00:43:40] swyx: And so, like, I, I think, like, you know, We're getting the sort of GPU poor version of a lot of these things coming out, and I think it's like quite useful. Like Windows as well, rolling out RWKB in sort of every Windows department is super cool. And I think the last thing that I never put in this GPU poor war, that I think I should now, [00:44:00] is the number of startups that are GPU poor but still scaling very well, as sort of wrappers on top of either a foundation model lab, or GPU Cloud.[00:44:10] swyx: GPU Cloud, it would be Suno. Suno, Ramp has rated as one of the top ranked, fastest growing startups of the year. Um, I think the last public number is like zero to 20 million this year in ARR and Suno runs on Moto. So Suno itself is not GPU rich, but they're just doing the training on, on Moto, uh, who we've also talked to on, on the podcast.[00:44:31] swyx: The other one would be Bolt, straight cloud wrapper. And, and, um, Again, another, now they've announced 20 million ARR, which is another step up from our 8 million that we put on the title. So yeah, I mean, it's crazy that all these GPU pores are finding a way while the GPU riches are also finding a way. And then the only failures, I kind of call this the GPU smiling curve, where the edges do well, because you're either close to the machines, and you're like [00:45:00] number one on the machines, or you're like close to the customers, and you're number one on the customer side.[00:45:03] swyx: And the people who are in the middle. Inflection, um, character, didn't do that great. I think character did the best of all of them. Like, you have a note in here that we apparently said that character's price tag was[00:45:15] Alessio: 1B.[00:45:15] swyx: Did I say that?[00:45:16] Alessio: Yeah. You said Google should just buy them for 1B. I thought it was a crazy number.[00:45:20] Alessio: Then they paid 2. 7 billion. I mean, for like,[00:45:22] swyx: yeah.[00:45:22] Alessio: What do you pay for node? Like, I don't know what the game world was like. Maybe the starting price was 1B. I mean, whatever it was, it worked out for everybody involved.[00:45:31] The Multi-Modality War[00:45:31] Alessio: Multimodality war. And this one, we never had text to video in the first version, which now is the hottest.[00:45:37] swyx: Yeah, I would say it's a subset of image, but yes.[00:45:40] Alessio: Yeah, well, but I think at the time it wasn't really something people were doing, and now we had VO2 just came out yesterday. Uh, Sora was released last month, last week. I've not tried Sora, because the day that I tried, it wasn't, yeah. I[00:45:54] swyx: think it's generally available now, you can go to Sora.[00:45:56] swyx: com and try it. Yeah, they had[00:45:58] Alessio: the outage. Which I [00:46:00] think also played a part into it. Small things. Yeah. What's the other model that you posted today that was on Replicate? Video or OneLive?[00:46:08] swyx: Yeah. Very, very nondescript name, but it is from Minimax, which I think is a Chinese lab. The Chinese labs do surprisingly well at the video models.[00:46:20] swyx: I'm not sure it's actually Chinese. I don't know. Hold me up to that. Yep. China. It's good. Yeah, the Chinese love video. What can I say? They have a lot of training data for video. Or a more relaxed regulatory environment.[00:46:37] Alessio: Uh, well, sure, in some way. Yeah, I don't think there's much else there. I think like, you know, on the image side, I think it's still open.[00:46:45] Alessio: Yeah, I mean,[00:46:46] swyx: 11labs is now a unicorn. So basically, what is multi modality war? Multi modality war is, do you specialize in a single modality, right? Or do you have GodModel that does all the modalities? So this is [00:47:00] definitely still going, in a sense of 11 labs, you know, now Unicorn, PicoLabs doing well, they launched Pico 2.[00:47:06] swyx: 0 recently, HeyGen, I think has reached 100 million ARR, Assembly, I don't know, but they have billboards all over the place, so I assume they're doing very, very well. So these are all specialist models, specialist models and specialist startups. And then there's the big labs who are doing the sort of all in one play.[00:47:24] swyx: And then here I would highlight Gemini 2 for having native image output. Have you seen the demos? Um, yeah, it's, it's hard to keep up. Literally they launched this last week and a shout out to Paige Bailey, who came to the Latent Space event to demo on the day of launch. And she wasn't prepared. She was just like, I'm just going to show you.[00:47:43] swyx: So they have voice. They have, you know, obviously image input, and then they obviously can code gen and all that. But the new one that OpenAI and Meta both have but they haven't launched yet is image output. So you can literally, um, I think their demo video was that you put in an image of a [00:48:00] car, and you ask for minor modifications to that car.[00:48:02] swyx: They can generate you that modification exactly as you asked. So there's no need for the stable diffusion or comfy UI workflow of like mask here and then like infill there in paint there and all that, all that stuff. This is small model nonsense. Big model people are like, huh, we got you in as everything in the transformer.[00:48:21] swyx: This is the multimodality war, which is, do you, do you bet on the God model or do you string together a whole bunch of, uh, Small models like a, like a chump. Yeah,[00:48:29] Alessio: I don't know, man. Yeah, that would be interesting. I mean, obviously I use Midjourney for all of our thumbnails. Um, they've been doing a ton on the product, I would say.[00:48:38] Alessio: They launched a new Midjourney editor thing. They've been doing a ton. Because I think, yeah, the motto is kind of like, Maybe, you know, people say black forest, the black forest models are better than mid journey on a pixel by pixel basis. But I think when you put it, put it together, have you tried[00:48:53] swyx: the same problems on black forest?[00:48:55] Alessio: Yes. But the problem is just like, you know, on black forest, it generates one image. And then it's like, you got to [00:49:00] regenerate. You don't have all these like UI things. Like what I do, no, but it's like time issue, you know, it's like a mid[00:49:06] swyx: journey. Call the API four times.[00:49:08] Alessio: No, but then there's no like variate.[00:49:10] Alessio: Like the good thing about mid journey is like, you just go in there and you're cooking. There's a lot of stuff that just makes it really easy. And I think people underestimate that. Like, it's not really a skill issue, because I'm paying mid journey, so it's a Black Forest skill issue, because I'm not paying them, you know?[00:49:24] Alessio: Yeah,[00:49:25] swyx: so, okay, so, uh, this is a UX thing, right? Like, you, you, you understand that, at least, we think that Black Forest should be able to do all that stuff. I will also shout out, ReCraft has come out, uh, on top of the image arena that, uh, artificial analysis has done, has apparently, uh, Flux's place. Is this still true?[00:49:41] swyx: So, Artificial Analysis is now a company. I highlighted them I think in one of the early AI Newses of the year. And they have launched a whole bunch of arenas. So, they're trying to take on LM Arena, Anastasios and crew. And they have an image arena. Oh yeah, Recraft v3 is now beating Flux 1. 1. Which is very surprising [00:50:00] because Flux And Black Forest Labs are the old stable diffusion crew who left stability after, um, the management issues.[00:50:06] swyx: So Recurve has come from nowhere to be the top image model. Uh, very, very strange. I would also highlight that Grok has now launched Aurora, which is, it's very interesting dynamics between Grok and Black Forest Labs because Grok's images were originally launched, uh, in partnership with Black Forest Labs as a, as a thin wrapper.[00:50:24] swyx: And then Grok was like, no, we'll make our own. And so they've made their own. I don't know, there are no APIs or benchmarks about it. They just announced it. So yeah, that's the multi modality war. I would say that so far, the small model, the dedicated model people are winning, because they are just focused on their tasks.[00:50:42] swyx: But the big model, People are always catching up. And the moment I saw the Gemini 2 demo of image editing, where I can put in an image and just request it and it does, that's how AI should work. Not like a whole bunch of complicated steps. So it really is something. And I think one frontier that we haven't [00:51:00] seen this year, like obviously video has done very well, and it will continue to grow.[00:51:03] swyx: You know, we only have Sora Turbo today, but at some point we'll get full Sora. Oh, at least the Hollywood Labs will get Fulsora. We haven't seen video to audio, or video synced to audio. And so the researchers that I talked to are already starting to talk about that as the next frontier. But there's still maybe like five more years of video left to actually be Soda.[00:51:23] swyx: I would say that Gemini's approach Compared to OpenAI, Gemini seems, or DeepMind's approach to video seems a lot more fully fledged than OpenAI. Because if you look at the ICML recap that I published that so far nobody has listened to, um, that people have listened to it. It's just a different, definitely different audience.[00:51:43] swyx: It's only seven hours long. Why are people not listening? It's like everything in Uh, so, so DeepMind has, is working on Genie. They also launched Genie 2 and VideoPoet. So, like, they have maybe four years advantage on world modeling that OpenAI does not have. Because OpenAI basically only started [00:52:00] Diffusion Transformers last year, you know, when they hired, uh, Bill Peebles.[00:52:03] swyx: So, DeepMind has, has a bit of advantage here, I would say, in, in, in showing, like, the reason that VO2, while one, They cherry pick their videos. So obviously it looks better than Sora, but the reason I would believe that VO2, uh, when it's fully launched will do very well is because they have all this background work in video that they've done for years.[00:52:22] swyx: Like, like last year's NeurIPS, I already was interviewing some of their video people. I forget their model name, but for, for people who are dedicated fans, they can go to NeurIPS 2023 and see, see that paper.[00:52:32] Alessio: And then last but not least, the LLMOS. We renamed it to Ragops, formerly known as[00:52:39] swyx: Ragops War. I put the latest chart on the Braintrust episode.[00:52:43] swyx: I think I'm going to separate these essays from the episode notes. So the reason I used to do that, by the way, is because I wanted to show up on Hacker News. I wanted the podcast to show up on Hacker News. So I always put an essay inside of there because Hacker News people like to read and not listen.[00:52:58] Alessio: So episode essays,[00:52:59] swyx: I remember [00:53:00] purchasing them separately. You say Lanchain Llama Index is still growing.[00:53:03] Alessio: Yeah, so I looked at the PyPy stats, you know. I don't care about stars. On PyPy you see Do you want to share your screen? Yes. I prefer to look at actual downloads, not at stars on GitHub. So if you look at, you know, Lanchain still growing.[00:53:20] Alessio: These are the last six months. Llama Index still growing. What I've basically seen is like things that, One, obviously these things have A commercial product. So there's like people buying this and sticking with it versus kind of hopping in between things versus, you know, for example, crew AI, not really growing as much.[00:53:38] Alessio: The stars are growing. If you look on GitHub, like the stars are growing, but kind of like the usage is kind of like flat. In the last six months, have they done some[00:53:4

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20Product: Is an AI Winter Approaching | The Future of AI Software Development: What is Real & What is BS | OpenAI: WTF is Going on & How Far Into Application Layer Do They Go | The Future Role of Software Engineers with Guy Podjarny

The Twenty Minute VC: Venture Capital | Startup Funding | The Pitch

Play Episode Listen Later Nov 29, 2024 46:11

Guy Podjarny founded Tessl, Snyk and Blaze. Tessl is reimagining software development for the AI era and shaping AI Native Development. Snyk created and leads the Developer Security category, and is now a multi-billion dollar company with over 1,000 employees. Guy was previously CTO at Akamai (following its acquisition of Blaze), is an active angel investor, and co-hosts of the AI Native Dev podcast. In Today's Episode with Guy Podjarny We Discuss: 03:02 Discussion on NVIDIA's Market Position 04:14 Will We See a Trough of Disillusionment in AI 07:36 The Future of AI Development and Specialized Models 10:17 Challenges and Opportunities in AI Dev Tools 17:41 Concerns About Closed vs. Open Development Platforms 21:27 Speculations on AI's Role in Application Layers 24:40 Google's Competitive Edge 25:28 IPO and M&A in the Trump Era 26:45 The Future Role of Software Developers 32:20 Security Challenges in AI Development 33:41 Spicy Questions and Charity Donations 36:05 Quickfire Round: Insights and Advice

AI Winter? Revolve vs. About You | Mit Gemini reden und mit Perplexity shoppen #407

Doppelgänger Tech Talk

Play Episode Listen Later Nov 19, 2024 94:44

407: Wir vergleichen Revolve mit About You und Zalando, testen die Gemini App und sprechen über Shop with Perplexity. Wir reden kurz über Lindner, Trump und Tiktok und Shein. Kommt jetzt der AI Winter? Was steht im State of European Tech Report 2024 von Atomico? Werbung: Wenn du in einem modernen IT Umfeld in Hannover, Hamburg oder Köln arbeiten möchtest, gehe auf HDI.group/DG. Dort sind alle offenen Stellen und zudem kannst du dich mit André und Jonas auf LinkedIn vernetzen. Philipp Glöckler und Philipp Klöckner sprechen heute über: (00:00:00) Intro (00:03:00) Feedback Kultur (00:06:45) Das liberale Dehbuch (00:10:30) Revolve vs. About You und Zalando (00:32:00) Selbstfahrende Autos (00:36:45) Elon & Twitter (00:42:45) Salesforce Agentforce (00:45:00) Gemini Live (00:54:00) Shop with Perplexity (01:05:30) TikTok (01:10:25) Shein IPO (01:13:20) NVIDIA & AI Winter (01:24:00) Trump Section 230 (01:26:00) Chrome Abspaltung , Playstore (01:27:00) European Tech Report Snownotes Das liberale Drehbuch für den Regierungssturz Die Zeit Trump-Team will US-Regeln für selbstfahrende Autos lockern Bloomberg Elon Musk hat Twitter-Algorithmen geändert, um die Wahl zu beeinflussen LinkedIn Post von Alexander Granderath Salesforce stellt 1.000 Mitarbeiter für den Vertrieb von KI-Produkten ein Bloomberg Die KI-Suchmaschine von Perplexity kann jetzt (in den USA) Produkte für Sie kaufen The Verge TikTok-Mutter ByteDance 300 Milliarden Dollar wert Manager Magazin Shein plant Börsengang in London im Jahr 2025 The Times Nvidia-Kunden sorgen sich um Probleme mit neuen KI-Chip-Servern The Information State of European Tech Report 2024 von Atomico Sifted fasst die wichtigsten Erkenntnisse aus der 10. Ausgabe des Atomico-Berichts zusammen Sifted DOJ wird Google zum Verkauf von Chrome drängen, um das Suchmonopol zu brechen Bloomberg

Woke has Won? White Doctors not Racist, Kamala Farewell, Trump Picks, AI Winter,

Anti Woke Podcast

Play Episode Listen Later Nov 18, 2024 76:11

Twitter: https://twitter.com/AntiWokePodcast Youtube: https://www.youtube.com/@antiwokepodcast8381/featured Tik Tok: https://www.tiktok.com/@anti_woke_podcast

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Barbs Wire: Video of bear suit insurance scam goes viral, AI winter coke ad get backlash, and 'Legendary Glamma'

The Morning Review with Lester Kiewit Podcast

Play Episode Listen Later Nov 18, 2024 7:35

What's trending on socials? Barbara Friedman give us the scoop.See omnystudio.com/listener for privacy information.

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The Age of Transitions and Uncle 10-25-2024 Eray Özkurl(3)

The Ochelli Effect

Play Episode Listen Later Oct 29, 2024 126:13

The Age of Transitions and Uncle 10-24-2024 Eray ÖzkuralAOT #439This week's episode of The Age of Transitions podcast is an interview of Eray Özkural. An AI professional, transhumanist, and all around free thinker, he has a lot of first hand experience from within the world of artificial intelligence technology that he talks about here.Topics include: Marvin Minsky, Usenet, philosophy of mind, machine learning, biocomputing, history of AI, AI research shut down on purpose, AI Winter artificially induced, experts can be fooled by propaganda, misinformation, science of influence, media, examachine, Open AI, scare tactics to keep tech in hands of a few, Sam Altman, free marketsUTP #349Uncle does his show while World Series game one ends. Chuck helps out with a little bit of play by play, and Ed comes on the line to give some post game commentary.Topics include: low numbers of live listeners, World Series game one, LA Rams, exciting game, Dodgers, Yankees, Isaiah popular name, extra innings, overlap of every sport, pitching changes, Freeman, grand slam home run, digital score boards, Ed calls, Uncle's team allegiances, pitching, Bronx Bombers, Brooklyn bums, ankle injuries, unique quality of game of baseball, pitch clock, squirrelslinks to Eray Ozkuralhttps://x.com/examachinehttps://examachine.net/blog/scratch-artificial-is-intelligence-an-existential-risk/FRANZ MAIN HUB:https://theageoftransitions.com/PATREONhttps://www.patreon.com/aaronfranzUNCLEhttps://unclethepodcast.com/ORhttps://theageoftransitions.com/category/uncle-the-podcast/FRANZ and UNCLE Merchhttps://theageoftransitions.com/category/support-the-podcasts/KEEP OCHELLI GOING. You are the EFFECT if you support OCHELLI https://ochelli.com/donate/Dallas Marriott Downtown Virtual Tickets starting at 74.99In-Person Tickets start at 144.99Student Price is 39.99, must show proof of being a studentTickets on sale atassassinationconference.comUse codeOchelli10for 10% off your ticketDallas Marriott DowntownRoom prices starting at $169 per nightTo book a room call Marriott Reservations at1 (800) 228-9290 or (214) 979-9000and mention the November in Dallas Conference Group RateIf you would like assistance finding discount flights to the conference or activities for your spouse to do in Dallas reach out to Gabbie's Getaway Adventures through Facebook or emailgabbiesgetawayadventure@gmail.com

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Eray Özkural interview on AI, AoT#439

Trans Resister Radio

Play Episode Listen Later Oct 28, 2024 60:59

This week's episode of The Age of Transitions podcast is an interview of Eray Özkural. An AI professional, transhumanist, and all around free thinker, he has a lot of first hand experience from within the world of artificial intelligence technology that he talks about here. Topics include: Marvin Minsky, Usenet, philosophy of mind, machine learning, biocomputing, history of AI, AI research shut down on purpose, AI Winter artificially induced, experts can be fooled by propaganda, misinformation, science of influence, media, examachine, Open AI, scare tactics to keep tech in hands of a few, Sam Altman, free markets

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Building the AI Engineer Nation — with Josephine Teo, Minister of Digital Development and Information, Singapore

Latent Space: The AI Engineer Podcast â€” CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

Play Episode Listen Later Oct 19, 2024 56:39

Singapore's GovTech is hosting an AI CTF challenge with ~$15,000 in prizes, starting October 26th, open to both local and virtual hackers. It will be hosted on Dreadnode's Crucible platform; signup here!It is common to say if you want to work in AI, you should come to San Francisco. Not everyone can. Not everyone should. If you can only do meaningful AI work in one city, then AI has failed to generalize meaningfully.As non-Americans working in the US, we know what it's like to see AI progress so rapidly here, and yet be at a loss for what our home countries can do. Through Latent Space we've tried to tell the story of AI outside of the Bay Area bubble; we talked to Notion in New York and Humanloop and Wondercraft in London and HuggingFace in Paris and ICLR in Vienna, and the Reka, RWKV, and Winds of AI Winter episodes were taped in Singapore (the World's Fair also had Latin America representation and we intend to at least add China, Japan, and India next year).The Role of Government with AIAs an intentionally technical resource, we've mostly steered clear of regulation and safety debates on the podcast; whether it is safety bills or technoalarmism, often at the cost of our engagement numbers or ability to book big name guests with a political agenda. When SOTA shifts 3x faster than it takes to pass a law, when nobody agrees on definitions of important things, when you can elicit never-before-seen behavior by slightly different prompting or sampling, it is hard enough to simply keep up to speed, so we are happy limiting our role to that. The story of AI progress has more often been achieved in the private sector, usually in spite of, rather than with thanks to, government intervention.But industrial policy is inextricably linked to the business of AI, which we do very much care about, has an explicitly accelerationist intent if not impact, and has a track record of success in correcting for legitimate market failures in private sector investment, particularly outside of the US. It is with this lens we approach today's episode and special guest, our first with a sitting Cabinet member.Singapore's National AI StrategyIt is well understood that much of Singapore's economic success is attributable to industrial policy, from direct efforts like the Jurong Town Corporation industrialization to indirect ones like going all in on English as national first language. Singapore's National AI Strategy grew out of its 2014 Smart Nation initiative, first launched in 2019 and then refreshed in 2023 by Minister Josephine Teo, our guest today.While Singapore is not often thought of as an AI leader, the National University ranks in the top 10 in publications (above Oxford/Harvard!), and many overseas Singaporeans work at the leading AI companies and institutions in the US (and some of us even run leading AI Substacks?). OpenAI has often publicly named the Singapore government as their model example of government collaborator and is opening an office in Singapore in time for DevDay 2024.AI Engineer NationsSwyx first pitched the AI Engineer Nation concept at a private Sovereign AI summit featuring Dr. He Ruimin, Chief AI Officer of Singapore, which eventually led to an invitation to discuss the concept with Minister Teo, the country's de-facto minister for tech (she calls it Digital Development, for good reasons she explains in the pod).This chat happened (with thanks to Jing Long, Joyce, and other folks from MDDI)!The central pitch for any country, not just Singapore, to emphasize and concentrate bets on AI Engineers, compared with other valuable efforts like training more researchers, releasing more government-approved data, or offering more AI funding, is a calculated one, based on the fact that: * GPU clusters and researchers have massive returns to scale and colocation, mostly concentrated in the US, that are irresponsibly expensive to replicate* Even if research stopped today and there was no progress for the next 30 years, there are far more capabilities to unlock and productize from existing foundation models and we

Generally AI - Season 2 - Episode 3: Surviving the AI Winter

The InfoQ Podcast

Play Episode Listen Later Oct 16, 2024 45:41

In this episode of Generally AI, Roland Meertens and Anthony Alford discuss the historical cycles of AI "summers" and "winters": periods of optimism and decline in AI research. The conversation follows the story of neural networks, from Rosenblatt's perceptron, to the resurgence of AI with backpropagation and deep learning in the 2010s. They also explore the potential for a future "AI Winter", as technological advances face both hype and skepticism. They then discuss the A* search algorithm, which was developed in 1968 and remains a key tool in AI and computer science. The algorithm's lasting relevance and flexibility make it useful across fields like mapping, video games, and even chess engines. Read a transcript of this interview: https://bit.ly/3BxgaMW Subscribe to the Software Architects' Newsletter for your monthly guide to the essential news and experience from industry peers on emerging patterns and technologies: https://www.infoq.com/software-architects-newsletter Upcoming Events: QCon San Francisco (November 18-22, 2024) Get practical inspiration and best practices on emerging software trends directly from senior software developers at early adopter companies. https://qconsf.com/ QCon London (April 7-9, 2025) Discover new ideas and insights from senior practitioners driving change and innovation in software development. https://qconlondon.com/ Save the date: InfoQ Dev Summit Boston (June 9-10, 2025) Actionable insights on today's critical dev priorities. The InfoQ Podcasts: Weekly inspiration to drive innovation and build great teams from senior software leaders. Listen to all our podcasts and read interview transcripts: - The InfoQ Podcast https://www.infoq.com/podcasts/ - Engineering Culture Podcast by InfoQ https://www.infoq.com/podcasts/#engineering_culture - Generally AI: https://www.infoq.com/generally-ai-podcast/ Follow InfoQ: - Mastodon: https://techhub.social/@infoq - Twitter: twitter.com/InfoQ - LinkedIn: www.linkedin.com/company/infoq - Facebook: bit.ly/2jmlyG8 - Instagram: @infoqdotcom - Youtube: www.youtube.com/infoq Write for InfoQ: Learn and share the changes and innovations in professional software development. - Join a community of experts. - Increase your visibility. - Grow your career. https://www.infoq.com/write-for-infoq

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Production AI Engineering starts with Evals — with Ankur Goyal of Braintrust

Latent Space: The AI Engineer Podcast â€” CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

Play Episode Listen Later Oct 11, 2024 116:40

We are in

AI Magic: Shipping 1000s of successful products with no managers and a team of 12 — Jeremy Howard of Answer.ai

Latent Space: The AI Engineer Podcast â€” CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

Play Episode Listen Later Aug 16, 2024 58:56

Disclaimer: We recorded this episode ~1.5 months ago, timing for the FastHTML release. It then got bottlenecked by Llama3.1, Winds of AI Winter, and SAM2 episodes, so we're a little late. Since then FastHTML was released, swyx is building an app in it for AINews, and Anthropic has also released their prompt caching API. Remember when Dylan Patel of SemiAnalysis coined the GPU Rich vs GPU Poor war? (if not, see our pod with him). The idea was that if you're GPU poor you shouldn't waste your time trying to solve GPU rich problems (i.e. pre-training large models) and are better off working on fine-tuning, optimized inference, etc. Jeremy Howard (see our “End of Finetuning” episode to catchup on his background) and Eric Ries founded Answer.AI to do exactly that: “Practical AI R&D”, which is very in-line with the GPU poor needs. For example, one of their first releases was a system based on FSDP + QLoRA that let anyone train a 70B model on two NVIDIA 4090s. Since then, they have come out with a long list of super useful projects (in no particular order, and non-exhaustive):* FSDP QDoRA: this is just as memory efficient and scalable as FSDP/QLoRA, and critically is also as accurate for continued pre-training as full weight training.* Cold Compress: a KV cache compression toolkit that lets you scale sequence length without impacting speed.* colbert-small: state of the art retriever at only 33M params* JaColBERTv2.5: a new state-of-the-art retrievers on all Japanese benchmarks.* gpu.cpp: portable GPU compute for C++ with WebGPU.* Claudette: a better Anthropic API SDK. They also recently released FastHTML, a new way to create modern interactive web apps. Jeremy recently released a 1 hour “Getting started” tutorial on YouTube; while this isn't AI related per se, but it's close to home for any AI Engineer who are looking to iterate quickly on new products: In this episode we broke down 1) how they recruit 2) how they organize what to research 3) and how the community comes together. At the end, Jeremy gave us a sneak peek at something new that he's working on that he calls dialogue engineering: So I've created a new approach. It's not called prompt engineering. I'm creating a system for doing dialogue engineering. It's currently called AI magic. I'm doing most of my work in this system and it's making me much more productive than I was before I used it.He explains it a bit more ~44:53 in the pod, but we'll just have to wait for the public release to figure out exactly what he means.Timestamps* [00:00:00] Intro by Suno AI* [00:03:02] Continuous Pre-Training is Here* [00:06:07] Schedule-Free Optimizers and Learning Rate Schedules* [00:07:08] Governance and Structural Issues within OpenAI and Other AI Labs* [00:13:01] How Answer.ai works* [00:23:40] How to Recruit Productive Researchers* [00:27:45] Building a new BERT* [00:31:57] FSDP, QLoRA, and QDoRA: Innovations in Fine-Tuning Large Models* [00:36:36] Research and Development on Model Inference Optimization* [00:39:49] FastHTML for Web Application Development* [00:46:53] AI Magic & Dialogue Engineering* [00:52:19] AI wishlist & predictionsShow Notes* Jeremy Howard* Previously on Latent Space: The End of Finetuning, NeurIPS Startups* Answer.ai* Fast.ai* FastHTML* answerai-colbert-small-v1* gpu.cpp* Eric Ries* Aaron DeFazio* Yi Tai* Less Wright* Benjamin Warner* Benjamin Clavié* Jono Whitaker* Austin Huang* Eric Gilliam* Tim Dettmers* Colin Raffel* Sebastian Raschka* Carson Gross* Simon Willison* Sepp Hochreiter* Llama3.1 episode* Snowflake Arctic* Ranger Optimizer* Gemma.cpp* HTMX* UL2* BERT* DeBERTa* Efficient finetuning of Llama 3 with FSDP QDoRA* xLSTMTranscriptAlessio [00:00:00]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO-in-Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol AI.Swyx [00:00:14]: And today we're back with Jeremy Howard, I think your third appearance on Latent Space. Welcome.Jeremy [00:00:19]: Wait, third? Second?Swyx [00:00:21]: Well, I grabbed you at NeurIPS.Jeremy [00:00:23]: I see.Swyx [00:00:24]: Very fun, standing outside street episode.Jeremy [00:00:27]: I never heard that, by the way. You've got to send me a link. I've got to hear what it sounded like.Swyx [00:00:30]: Yeah. Yeah, it's a NeurIPS podcast.Alessio [00:00:32]: I think the two episodes are six hours, so there's plenty to listen, we'll make sure to send it over.Swyx [00:00:37]: Yeah, we're trying this thing where at the major ML conferences, we, you know, do a little audio tour of, give people a sense of what it's like. But the last time you were on, you declared the end of fine tuning. I hope that I sort of editorialized the title a little bit, and I know you were slightly uncomfortable with it, but you just own it anyway. I think you're very good at the hot takes. And we were just discussing in our pre-show that it's really happening, that the continued pre-training is really happening.Jeremy [00:01:02]: Yeah, absolutely. I think people are starting to understand that treating the three ULM FIT steps of like pre-training, you know, and then the kind of like what people now call instruction tuning, and then, I don't know if we've got a general term for this, DPO, RLHFE step, you know, or the task training, they're not actually as separate as we originally suggested they were in our paper, and when you treat it more as a continuum, and that you make sure that you have, you know, more of kind of the original data set incorporated into the later stages, and that, you know, we've also seen with LLAMA3, this idea that those later stages can be done for a lot longer. These are all of the things I was kind of trying to describe there. It wasn't the end of fine tuning, but more that we should treat it as a continuum, and we should have much higher expectations of how much you can do with an already trained model. You can really add a lot of behavior to it, you can change its behavior, you can do a lot. So a lot of our research has been around trying to figure out how to modify the model by a larger amount rather than starting from random weights, because I get very offended at the idea of starting from random weights.Swyx [00:02:14]: Yeah, I saw that in ICLR in Vienna, there was an outstanding paper about starting transformers from data-driven piers. I don't know if you saw that one, they called it sort of never trained from scratch, and I think it was kind of rebelling against like the sort of random initialization.Jeremy [00:02:28]: Yeah, I've, you know, that's been our kind of continuous message since we started Fast AI, is if you're training for random weights, you better have a really good reason, you know, because it seems so unlikely to me that nobody has ever trained on data that has any similarity whatsoever to the general class of data you're working with, and that's the only situation in which I think starting from random weights makes sense.Swyx [00:02:51]: The other trends since our last pod that I would point people to is I'm seeing a rise in multi-phase pre-training. So Snowflake released a large model called Snowflake Arctic, where they detailed three phases of training where they had like a different mixture of like, there was like 75% web in the first instance, and then they reduced the percentage of the web text by 10% each time and increased the amount of code in each phase. And I feel like multi-phase is being called out in papers more. I feel like it's always been a thing, like changing data mix is not something new, but calling it a distinct phase is new, and I wonder if there's something that you're seeingJeremy [00:03:32]: on your end. Well, so they're getting there, right? So the point at which they're doing proper continued pre-training is the point at which that becomes a continuum rather than a phase. So the only difference with what I was describing last time is to say like, oh, there's a function or whatever, which is happening every batch. It's not a huge difference. You know, I always used to get offended when people had learning rates that like jumped. And so one of the things I started doing early on in Fast.ai was to say to people like, no, you should actually have your learning rate schedule should be a function, not a list of numbers. So now I'm trying to give the same idea about training mix.Swyx [00:04:07]: There's been pretty public work from Meta on schedule-free optimizers. I don't know if you've been following Aaron DeFazio and what he's doing, just because you mentioned learning rate schedules, you know, what if you didn't have a schedule?Jeremy [00:04:18]: I don't care very much, honestly. I don't think that schedule-free optimizer is that exciting. It's fine. We've had non-scheduled optimizers for ages, like Less Wright, who's now at Meta, who was part of the Fast.ai community there, created something called the Ranger optimizer. I actually like having more hyperparameters. You know, as soon as you say schedule-free, then like, well, now I don't get to choose. And there isn't really a mathematically correct way of, like, I actually try to schedule more parameters rather than less. So like, I like scheduling my epsilon in my atom, for example. I schedule all the things. But then the other thing we always did with the Fast.ai library was make it so you don't have to set any schedules. So Fast.ai always supported, like, you didn't even have to pass a learning rate. Like, it would always just try to have good defaults and do the right thing. But to me, I like to have more parameters I can play with if I want to, but you don't have to.Alessio [00:05:08]: And then the more less technical side, I guess, of your issue, I guess, with the market was some of the large research labs taking all this innovation kind of behind closed doors and whether or not that's good, which it isn't. And now we could maybe make it more available to people. And then a month after we released the episode, there was the whole Sam Altman drama and like all the OpenAI governance issues. And maybe people started to think more, okay, what happens if some of these kind of labs, you know, start to break from within, so to speak? And the alignment of the humans is probably going to fall before the alignment of the models. So I'm curious, like, if you have any new thoughts and maybe we can also tie in some of the way that we've been building Answer as like a public benefit corp and some of those aspects.Jeremy [00:05:51]: Sure. So, yeah, I mean, it was kind of uncomfortable because two days before Altman got fired, I did a small public video interview in which I said, I'm quite sure that OpenAI's current governance structure can't continue and that it was definitely going to fall apart. And then it fell apart two days later and a bunch of people were like, what did you know, Jeremy?Alessio [00:06:13]: What did Jeremy see?Jeremy [00:06:15]: I didn't see anything. It's just obviously true. Yeah. So my friend Eric Ries and I spoke a lot before that about, you know, Eric's, I think probably most people would agree, the top expert in the world on startup and AI governance. And you know, we could both clearly see that this didn't make sense to have like a so-called non-profit where then there are people working at a company, a commercial company that's owned by or controlled nominally by the non-profit, where the people in the company are being given the equivalent of stock options, like everybody there was working there with expecting to make money largely from their equity. So the idea that then a board could exercise control by saying like, oh, we're worried about safety issues and so we're going to do something that decreases the profit of the company, when every stakeholder in the company, their remuneration pretty much is tied to their profit, it obviously couldn't work. So I mean, that was a huge oversight there by someone. I guess part of the problem is that the kind of people who work at non-profits and in this case the board, you know, who are kind of academics and, you know, people who are kind of true believers. I think it's hard for them to realize that 99.999% of the world is driven very heavily by money, especially huge amounts of money. So yeah, Eric and I had been talking for a long time before that about what could be done differently, because also companies are sociopathic by design and so the alignment problem as it relates to companies has not been solved. Like, companies become huge, they devour their founders, they devour their communities and they do things where even the CEOs, you know, often of big companies tell me like, I wish our company didn't do that thing. You know, I know that if I didn't do it, then I would just get fired and the board would put in somebody else and the board knows if they don't do it, then their shareholders can sue them because they're not maximizing profitability or whatever. So what Eric's spent a lot of time doing is trying to think about how do we make companies less sociopathic, you know, how to, or more, you know, maybe a better way to think of it is like, how do we make it so that the founders of companies can ensure that their companies continue to actually do the things they want them to do? You know, when we started a company, hey, we very explicitly decided we got to start a company, not a academic lab, not a nonprofit, you know, we created a Delaware Seacorp, you know, the most company kind of company. But when we did so, we told everybody, you know, including our first investors, which was you Alessio. They sound great. We are going to run this company on the basis of maximizing long-term value. And in fact, so when we did our second round, which was an angel round, we had everybody invest through a long-term SPV, which we set up where everybody had to agree to vote in line with long-term value principles. So like never enough just to say to people, okay, we're trying to create long-term value here for society as well as for ourselves and everybody's like, oh, yeah, yeah, I totally agree with that. But when it comes to like, okay, well, here's a specific decision we have to make, which will not maximize short-term value, people suddenly change their mind. So you know, it has to be written into the legal documents of everybody so that no question that that's the way the company has to be managed. So then you mentioned the PBC aspect, Public Benefit Corporation, which I never quite understood previously. And turns out it's incredibly simple, like it took, you know, like one paragraph added to our corporate documents to become a PBC. It was cheap, it was easy, but it's got this huge benefit, which is if you're not a public benefit corporation, then somebody can come along and offer to buy you with a stated description of like turning your company into the thing you most hate, right? And if they offer you more than the market value of your company and you don't accept it, then you are not necessarily meeting the kind of your fiduciary responsibilities. So the way like Eric always described it to me is like, if Philip Morris came along and said that you've got great technology for marketing cigarettes to children, so we're going to pivot your company to do that entirely, and we're going to pay you 50% more than the market value, you're going to have to say yes. If you have a PBC, then you are more than welcome to say no, if that offer is not in line with your stated public benefit. So our stated public benefit is to maximize the benefit to society through using AI. So given that more children smoking doesn't do that, then we can say like, no, we're not selling to you.Alessio [00:11:01]: I was looking back at some of our emails. You sent me an email on November 13th about talking and then on the 14th, I sent you an email working together to free AI was the subject line. And then that was kind of the start of the C round. And then two days later, someone got fired. So you know, you were having these thoughts even before we had like a public example of like why some of the current structures didn't work. So yeah, you were very ahead of the curve, so to speak. You know, people can read your awesome introduction blog and answer and the idea of having a R&D lab versus our lab and then a D lab somewhere else. I think to me, the most interesting thing has been hiring and some of the awesome people that you've been bringing on that maybe don't fit the central casting of Silicon Valley, so to speak. Like sometimes I got it like playing baseball cards, you know, people are like, oh, what teams was this person on, where did they work versus focusing on ability. So I would love for you to give a shout out to some of the awesome folks that you have on the team.Jeremy [00:11:58]: So, you know, there's like a graphic going around describing like the people at XAI, you know, Elon Musk thing. And like they are all connected to like multiple of Stanford, Meta, DeepMind, OpenAI, Berkeley, Oxford. Look, these are all great institutions and they have good people. And I'm definitely not at all against that, but damn, there's so many other people. And one of the things I found really interesting is almost any time I see something which I think like this is really high quality work and it's something I don't think would have been built if that person hadn't built the thing right now, I nearly always reach out to them and ask to chat. And I tend to dig in to find out like, okay, you know, why did you do that thing? Everybody else has done this other thing, your thing's much better, but it's not what other people are working on. And like 80% of the time, I find out the person has a really unusual background. So like often they'll have like, either they like came from poverty and didn't get an opportunity to go to a good school or had dyslexia and, you know, got kicked out of school in year 11, or they had a health issue that meant they couldn't go to university or something happened in their past and they ended up out of the mainstream. And then they kind of succeeded anyway. Those are the people that throughout my career, I've tended to kind of accidentally hire more of, but it's not exactly accidentally. It's like when I see somebody who's done, two people who have done extremely well, one of them did extremely well in exactly the normal way from the background entirely pointing in that direction and they achieved all the hurdles to get there. And like, okay, that's quite impressive, you know, but another person who did just as well, despite lots of constraints and doing things in really unusual ways and came up with different approaches. That's normally the person I'm likely to find useful to work with because they're often like risk-takers, they're often creative, they're often extremely tenacious, they're often very open-minded. So that's the kind of folks I tend to find myself hiring. So now at Answer.ai, it's a group of people that are strong enough that nearly every one of them has independently come to me in the past few weeks and told me that they have imposter syndrome and they're not convinced that they're good enough to be here. And I kind of heard it at the point where I was like, okay, I don't think it's possible that all of you are so far behind your peers that you shouldn't get to be here. But I think part of the problem is as an R&D lab, the great developers look at the great researchers and they're like, wow, these big-brained, crazy research people with all their math and s**t, they're too cool for me, oh my God. And then the researchers look at the developers and they're like, oh, they're killing it, making all this stuff with all these people using it and talking on Twitter about how great it is. I think they're both a bit intimidated by each other, you know. And so I have to kind of remind them like, okay, there are lots of things in this world where you suck compared to lots of other people in this company, but also vice versa, you know, for all things. And the reason you came here is because you wanted to learn about those other things from those other people and have an opportunity to like bring them all together into a single unit. You know, it's not reasonable to expect you're going to be better at everything than everybody else. I guess the other part of it is for nearly all of the people in the company, to be honest, they have nearly always been better than everybody else at nearly everything they're doing nearly everywhere they've been. So it's kind of weird to be in this situation now where it's like, gee, I can clearly see that I suck at this thing that I'm meant to be able to do compared to these other people where I'm like the worst in the company at this thing for some things. So I think that's a healthy place to be, you know, as long as you keep reminding each other about that's actually why we're here. And like, it's all a bit of an experiment, like we don't have any managers. We don't have any hierarchy from that point of view. So for example, I'm not a manager, which means I don't get to tell people what to do or how to do it or when to do it. Yeah, it's been a bit of an experiment to see how that would work out. And it's been great. So for instance, Ben Clavier, who you might have come across, he's the author of Ragatouille, he's the author of Rerankers, super strong information retrieval guy. And a few weeks ago, you know, this additional channel appeared on Discord, on our private Discord called Bert24. And these people started appearing, as in our collab sections, we have a collab section for like collaborating with outsiders. And these people started appearing, there are all these names that I recognize, like Bert24, and they're all talking about like the next generation of Bert. And I start following along, it's like, okay, Ben decided that I think, quite rightly, we need a new Bert. Because everybody, like so many people are still using Bert, and it's still the best at so many things, but it actually doesn't take advantage of lots of best practices. And so he just went out and found basically everybody who's created better Berts in the last four or five years, brought them all together, suddenly there's this huge collaboration going on. So yeah, I didn't tell him to do that. He didn't ask my permission to do that. And then, like, Benjamin Warner dived in, and he's like, oh, I created a whole transformers from scratch implementation designed to be maximally hackable. He originally did it largely as a teaching exercise to show other people, but he was like, I could, you know, use that to create a really hackable BERT implementation. In fact, he didn't say that. He said, I just did do that, you know, and I created a repo, and then everybody's like starts using it. They're like, oh my god, this is amazing. I can now implement all these other BERT things. And it's not just answer AI guys there, you know, there's lots of folks, you know, who have like contributed new data set mixes and blah, blah, blah. So, I mean, I can help in the same way that other people can help. So like, then Ben Clavier reached out to me at one point and said, can you help me, like, what have you learned over time about how to manage intimidatingly capable and large groups of people who you're nominally meant to be leading? And so, you know, I like to try to help, but I don't direct. Another great example was Kerem, who, after our FSTP QLORA work, decided quite correctly that it didn't really make sense to use LoRa in today's world. You want to use the normalized version, which is called Dora. Like two or three weeks after we did FSTP QLORA, he just popped up and said, okay, I've just converted the whole thing to Dora, and I've also created these VLLM extensions, and I've got all these benchmarks, and, you know, now I've got training of quantized models with adapters that are as fast as LoRa, and as actually better than, weirdly, fine tuning. Just like, okay, that's great, you know. And yeah, so the things we've done to try to help make these things happen as well is we don't have any required meetings, you know, but we do have a meeting for each pair of major time zones that everybody's invited to, and, you know, people see their colleagues doing stuff that looks really cool and say, like, oh, how can I help, you know, or how can I learn or whatever. So another example is Austin, who, you know, amazing background. He ran AI at Fidelity, he ran AI at Pfizer, he ran browsing and retrieval for Google's DeepMind stuff, created Jemma.cpp, and he's been working on a new system to make it easier to do web GPU programming, because, again, he quite correctly identified, yeah, so I said to him, like, okay, I want to learn about that. Not an area that I have much expertise in, so, you know, he's going to show me what he's working on and teach me a bit about it, and hopefully I can help contribute. I think one of the key things that's happened in all of these is everybody understands what Eric Gilliam, who wrote the second blog post in our series, the R&D historian, describes as a large yard with narrow fences. Everybody has total flexibility to do what they want. We all understand kind of roughly why we're here, you know, we agree with the premises around, like, everything's too expensive, everything's too complicated, people are building too many vanity foundation models rather than taking better advantage of fine-tuning, like, there's this kind of general, like, sense of we're all on the same wavelength about, you know, all the ways in which current research is fucked up, and, you know, all the ways in which we're worried about centralization. We all care a lot about not just research for the point of citations, but research that actually wouldn't have happened otherwise, and actually is going to lead to real-world outcomes. And so, yeah, with this kind of, like, shared vision, people understand, like, you know, so when I say, like, oh, well, you know, tell me, Ben, about BERT 24, what's that about? And he's like, you know, like, oh, well, you know, you can see from an accessibility point of view, or you can see from a kind of a actual practical impact point of view, there's far too much focus on decoder-only models, and, you know, like, BERT's used in all of these different places and industry, and so I can see, like, in terms of our basic principles, what we're trying to achieve, this seems like something important. And so I think that's, like, a really helpful that we have that kind of shared perspective, you know?Alessio [00:21:14]: Yeah. And before we maybe talk about some of the specific research, when you're, like, reaching out to people, interviewing them, what are some of the traits, like, how do these things come out, you know, usually? Is it working on side projects that you, you know, you're already familiar with? Is there anything, like, in the interview process that, like, helps you screen for people that are less pragmatic and more research-driven versus some of these folks that are just gonna do it, you know? They're not waiting for, like, the perfect process.Jeremy [00:21:40]: Everybody who comes through the recruiting is interviewed by everybody in the company. You know, our goal is 12 people, so it's not an unreasonable amount. So the other thing to say is everybody so far who's come into the recruiting pipeline, everybody bar one, has been hired. So which is to say our original curation has been good. And that's actually pretty easy, because nearly everybody who's come in through the recruiting pipeline are people I know pretty well. So Jono Whitaker and I, you know, he worked on the stable diffusion course we did. He's outrageously creative and talented, and he's super, like, enthusiastic tinkerer, just likes making things. Benjamin was one of the strongest parts of the fast.ai community, which is now the alumni. It's, like, hundreds of thousands of people. And you know, again, like, they're not people who a normal interview process would pick up, right? So Benjamin doesn't have any qualifications in math or computer science. Jono was living in Zimbabwe, you know, he was working on, like, helping some African startups, you know, but not FAANG kind of credentials. But yeah, I mean, when you actually see people doing real work and they stand out above, you know, we've got lots of Stanford graduates and open AI people and whatever in our alumni community as well. You know, when you stand out above all of those people anyway, obviously you've got something going for you. You know, Austin, him and I worked together on the masks study we did in the proceeding at the National Academy of Science. You know, we had worked together, and again, that was a group of, like, basically the 18 or 19 top experts in the world on public health and epidemiology and research design and so forth. And Austin, you know, one of the strongest people in that collaboration. So yeah, you know, like, I've been lucky enough to have had opportunities to work with some people who are great and, you know, I'm a very open-minded person, so I kind of am always happy to try working with pretty much anybody and some people stand out. You know, there have been some exceptions, people I haven't previously known, like Ben Clavier, actually, I didn't know before. But you know, with him, you just read his code, and I'm like, oh, that's really well-written code. And like, it's not written exactly the same way as everybody else's code, and it's not written to do exactly the same thing as everybody else's code. So yeah, and then when I chatted to him, it's just like, I don't know, I felt like we'd known each other for years, like we just were on the same wavelength, but I could pretty much tell that was going to happen just by reading his code. I think you express a lot in the code you choose to write and how you choose to write it, I guess. You know, or another example, a guy named Vic, who was previously the CEO of DataQuest, and like, in that case, you know, he's created a really successful startup. He won the first, basically, Kaggle NLP competition, which was automatic essay grading. He's got the current state-of-the-art OCR system, Surya. Again, he's just a guy who obviously just builds stuff, you know, he doesn't ask for permission, he doesn't need any, like, external resources. Actually, Karim's another great example of this, I mean, I already knew Karim very well because he was my best ever master's student, but it wasn't a surprise to me then when he then went off to create the world's state-of-the-art language model in Turkish on his own, in his spare time, with no budget, from scratch. This is not fine-tuning or whatever, he, like, went back to Common Crawl and did everything. Yeah, it's kind of, I don't know what I'd describe that process as, but it's not at all based on credentials.Swyx [00:25:17]: Assemble based on talent, yeah. We wanted to dive in a little bit more on, you know, turning from the people side of things into the technical bets that you're making. Just a little bit more on Bert. I was actually, we just did an interview with Yi Tay from Reka, I don't know if you're familiar with his work, but also another encoder-decoder bet, and one of his arguments was actually people kind of over-index on the decoder-only GPT-3 type paradigm. I wonder if you have thoughts there that is maybe non-consensus as well. Yeah, no, absolutely.Jeremy [00:25:45]: So I think it's a great example. So one of the people we're collaborating with a little bit with BERT24 is Colin Raffle, who is the guy behind, yeah, most of that stuff, you know, between that and UL2, there's a lot of really interesting work. And so one of the things I've been encouraging the BERT group to do, Colin has as well, is to consider using a T5 pre-trained encoder backbone as a thing you fine-tune, which I think would be really cool. You know, Colin was also saying actually just use encoder-decoder as your Bert, you know, why don't you like use that as a baseline, which I also think is a good idea. Yeah, look.Swyx [00:26:25]: What technical arguments are people under-weighting?Jeremy [00:26:27]: I mean, Colin would be able to describe this much better than I can, but I'll give my slightly non-expert attempt. Look, I mean, think about like diffusion models, right? Like in stable diffusion, like we use things like UNet. You have this kind of downward path and then in the upward path you have the cross connections, which it's not a tension, but it's like a similar idea, right? You're inputting the original encoding path into your decoding path. It's critical to make it work, right? Because otherwise in the decoding part, the model has to do so much kind of from scratch. So like if you're doing translation, like that's a classic kind of encoder-decoder example. If it's decoder only, you never get the opportunity to find the right, you know, feature engineering, the right feature encoding for the original sentence. And it kind of means then on every token that you generate, you have to recreate the whole thing, you know? So if you have an encoder, it's basically saying like, okay, this is your opportunity model to create a really useful feature representation for your input information. So I think there's really strong arguments for encoder-decoder models anywhere that there is this kind of like context or source thing. And then why encoder only? Well, because so much of the time what we actually care about is a classification, you know? It's like an output. It's like generating an arbitrary length sequence of tokens. So anytime you're not generating an arbitrary length sequence of tokens, decoder models don't seem to make much sense. Now the interesting thing is, you see on like Kaggle competitions, that decoder models still are at least competitive with things like Deberta v3. They have to be way bigger to be competitive with things like Deberta v3. And the only reason they are competitive is because people have put a lot more time and money and effort into training the decoder only ones, you know? There isn't a recent Deberta. There isn't a recent Bert. Yeah, it's a whole part of the world that people have slept on a little bit. And this is just what happens. This is how trends happen rather than like, to me, everybody should be like, oh, let's look at the thing that has shown signs of being useful in the past, but nobody really followed up with properly. That's the more interesting path, you know, where people tend to be like, oh, I need to get citations. So what's everybody else doing? Can I make it 0.1% better, you know, or 0.1% faster? That's what everybody tends to do. Yeah. So I think it's like, Itay's work commercially now is interesting because here's like a whole, here's a whole model that's been trained in a different way. So there's probably a whole lot of tasks it's probably better at than GPT and Gemini and Claude. So that should be a good commercial opportunity for them if they can figure out what those tasks are.Swyx [00:29:07]: Well, if rumors are to be believed, and he didn't comment on this, but, you know, Snowflake may figure out the commercialization for them. So we'll see.Jeremy [00:29:14]: Good.Alessio [00:29:16]: Let's talk about FSDP, Qlora, Qdora, and all of that awesome stuff. One of the things we talked about last time, some of these models are meant to run on systems that nobody can really own, no single person. And then you were like, well, what if you could fine tune a 70B model on like a 4090? And I was like, no, that sounds great, Jeremy, but like, can we actually do it? And then obviously you all figured it out. Can you maybe tell us some of the worst stories behind that, like the idea behind FSDP, which is kind of taking sharded data, parallel computation, and then Qlora, which is do not touch all the weights, just go quantize some of the model, and then within the quantized model only do certain layers instead of doing everything.Jeremy [00:29:57]: Well, do the adapters. Yeah.Alessio [00:29:59]: Yeah. Yeah. Do the adapters. Yeah. I will leave the floor to you. I think before you published it, nobody thought this was like a short term thing that we're just going to have. And now it's like, oh, obviously you can do it, but it's not that easy.Jeremy [00:30:12]: Yeah. I mean, to be honest, it was extremely unpleasant work to do. It's like not at all enjoyable. I kind of did version 0.1 of it myself before we had launched the company, or at least the kind of like the pieces. They're all pieces that are difficult to work with, right? So for the quantization, you know, I chatted to Tim Detmers quite a bit and, you know, he very much encouraged me by saying like, yeah, it's possible. He actually thought it'd be easy. It probably would be easy for him, but I'm not Tim Detmers. And, you know, so he wrote bits and bytes, which is his quantization library. You know, he wrote that for a paper. He didn't write that to be production like code. It's now like everybody's using it, at least the CUDA bits. So like, it's not particularly well structured. There's lots of code paths that never get used. There's multiple versions of the same thing. You have to try to figure it out. So trying to get my head around that was hard. And you know, because the interesting bits are all written in CUDA, it's hard to like to step through it and see what's happening. And then, you know, FSTP is this very complicated library and PyTorch, which not particularly well documented. So the only really, really way to understand it properly is again, just read the code and step through the code. And then like bits and bytes doesn't really work in practice unless it's used with PEF, the HuggingFace library and PEF doesn't really work in practice unless you use it with other things. And there's a lot of coupling in the HuggingFace ecosystem where like none of it works separately. You have to use it all together, which I don't love. So yeah, trying to just get a minimal example that I can play with was really hard. And so I ended up having to rewrite a lot of it myself to kind of create this like minimal script. One thing that helped a lot was Medec had this LlamaRecipes repo that came out just a little bit before I started working on that. And like they had a kind of role model example of like, here's how to train FSTP, LoRa, didn't work with QLoRa on Llama. A lot of the stuff I discovered, the interesting stuff would be put together by Les Wright, who's, he was actually the guy in the Fast.ai community I mentioned who created the Ranger Optimizer. So he's doing a lot of great stuff at Meta now. So yeah, I kind of, that helped get some minimum stuff going and then it was great once Benjamin and Jono joined full time. And so we basically hacked at that together and then Kerim joined like a month later or something. And it was like, gee, it was just a lot of like fiddly detailed engineering on like barely documented bits of obscure internals. So my focus was to see if it kind of could work and I kind of got a bit of a proof of concept working and then the rest of the guys actually did all the work to make it work properly. And, you know, every time we thought we had something, you know, we needed to have good benchmarks, right? So we'd like, it's very easy to convince yourself you've done the work when you haven't, you know, so then we'd actually try lots of things and be like, oh, and these like really important cases, the memory use is higher, you know, or it's actually slower. And we'd go in and we just find like all these things that were nothing to do with our library that just didn't work properly. And nobody had noticed they hadn't worked properly because nobody had really benchmarked it properly. So we ended up, you know, trying to fix a whole lot of different things. And even as we did so, new regressions were appearing in like transformers and stuff that Benjamin then had to go away and figure out like, oh, how come flash attention doesn't work in this version of transformers anymore with this set of models and like, oh, it turns out they accidentally changed this thing, so it doesn't work. You know, there's just, there's not a lot of really good performance type evals going on in the open source ecosystem. So there's an extraordinary amount of like things where people say like, oh, we built this thing and it has this result. And when you actually check it, so yeah, there's a shitload of war stories from getting that thing to work. And it did require a particularly like tenacious group of people and a group of people who don't mind doing a whole lot of kind of like really janitorial work, to be honest, to get the details right, to check them. Yeah.Alessio [00:34:09]: We had a trade out on the podcast and we talked about how a lot of it is like systems work to make some of these things work. It's not just like beautiful, pure math that you do on a blackboard. It's like, how do you get into the nitty gritty?Jeremy [00:34:22]: I mean, flash attention is a great example of that. Like it's, it basically is just like, oh, let's just take the attention and just do the tiled version of it, which sounds simple enough, you know, but then implementing that is challenging at lots of levels.Alessio [00:34:36]: Yeah. What about inference? You know, obviously you've done all this amazing work on fine tuning. Do you have any research you've been doing on the inference side, how to make local inference really fast on these models too?Jeremy [00:34:47]: We're doing quite a bit on that at the moment. We haven't released too much there yet. But one of the things I've been trying to do is also just to help other people. And one of the nice things that's happened is that a couple of folks at Meta, including Mark Seraphim, have done a nice job of creating this CUDA mode community of people working on like CUDA kernels or learning about that. And I tried to help get that going well as well and did some lessons to help people get into it. So there's a lot going on in both inference and fine tuning performance. And a lot of it's actually happening kind of related to that. So PyTorch team have created this Torch AO project on quantization. And so there's a big overlap now between kind of the FastAI and AnswerAI and CUDA mode communities of people working on stuff for both inference and fine tuning. But we're getting close now. You know, our goal is that nobody should be merging models, nobody should be downloading merged models, everybody should be using basically quantized plus adapters for almost everything and just downloading the adapters. And that should be much faster. So that's kind of the place we're trying to get to. It's difficult, you know, because like Karim's been doing a lot of work with VLM, for example. These inference engines are pretty complex bits of code. They have a whole lot of custom kernel stuff going on as well, as do the quantization libraries. So we've been working on, we're also quite a bit of collaborating with the folks who do HQQ, which is a really great quantization library and works super well. So yeah, there's a lot of other people outside AnswerAI that we're working with a lot who are really helping on all this performance optimization stuff, open source.Swyx [00:36:27]: Just to follow up on merging models, I picked up there that you said nobody should be merging models. That's interesting because obviously a lot of people are experimenting with this and finding interesting results. I would say in defense of merging models, you can do it without data. That's probably the only thing that's going for it.Jeremy [00:36:45]: To explain, it's not that you shouldn't merge models. You shouldn't be distributing a merged model. You should distribute a merged adapter 99% of the time. And actually often one of the best things happening in the model merging world is actually that often merging adapters works better anyway. The point is, Sean, that once you've got your new model, if you distribute it as an adapter that sits on top of a quantized model that somebody's already downloaded, then it's a much smaller download for them. And also the inference should be much faster because you're not having to transfer FB16 weights from HPM memory at all or ever load them off disk. You know, all the main weights are quantized and the only floating point weights are in the adapters. So that should make both inference and fine tuning faster. Okay, perfect.Swyx [00:37:33]: We're moving on a little bit to the rest of the fast universe. I would have thought that, you know, once you started Answer.ai, that the sort of fast universe would be kind of on hold. And then today you just dropped Fastlight and it looks like, you know, there's more activity going on in sort of Fastland.Jeremy [00:37:49]: Yeah. So Fastland and Answerland are not really distinct things. Answerland is kind of like the Fastland grown up and funded. They both have the same mission, which is to maximize the societal benefit of AI broadly. We want to create thousands of commercially successful products at Answer.ai. And we want to do that with like 12 people. So that means we need a pretty efficient stack, you know, like quite a few orders of magnitude more efficient, not just for creation, but for deployment and maintenance than anything that currently exists. People often forget about the D part of our R&D firm. So we've got to be extremely good at creating, deploying and maintaining applications, not just models. Much to my horror, the story around creating web applications is much worse now than it was 10 or 15 years ago in terms of, if I say to a data scientist, here's how to create and deploy a web application, you know, either you have to learn JavaScript or TypeScript and about all the complex libraries like React and stuff, and all the complex like details around security and web protocol stuff around how you then talk to a backend and then all the details about creating the backend. You know, if that's your job and, you know, you have specialists who work in just one of those areas, it is possible for that to all work. But compared to like, oh, write a PHP script and put it in the home directory that you get when you sign up to this shell provider, which is what it was like in the nineties, you know, here are those 25 lines of code and you're done and now you can pass that URL around to all your friends, or put this, you know, .pl file inside the CGI bin directory that you got when you signed up to this web host. So yeah, the thing I've been mainly working on the last few weeks is fixing all that. And I think I fixed it. I don't know if this is an announcement, but I tell you guys, so yeah, there's this thing called fastHTML, which basically lets you create a complete web application in a single Python file. Unlike excellent projects like Streamlit and Gradio, you're not working on top of a highly abstracted thing. That's got nothing to do with web foundations. You're working with web foundations directly, but you're able to do it by using pure Python. There's no template, there's no ginger, there's no separate like CSS and JavaScript files. It looks and behaves like a modern SPA web application. And you can create components for like daisy UI, or bootstrap, or shoelace, or whatever fancy JavaScript and or CSS tailwind etc library you like, but you can write it all in Python. You can pip install somebody else's set of components and use them entirely from Python. You can develop and prototype it all in a Jupyter notebook if you want to. It all displays correctly, so you can like interactively do that. And then you mentioned Fastlight, so specifically now if you're using SQLite in particular, it's like ridiculously easy to have that persistence, and all of your handlers will be passed database ready objects automatically, that you can just call dot delete dot update dot insert on. Yeah, you get session, you get security, you get all that. So again, like with most everything I do, it's very little code. It's mainly tying together really cool stuff that other people have written. You don't have to use it, but a lot of the best stuff comes from its incorporation of HTMX, which to me is basically the thing that changes your browser to make it work the way it always should have. So it just does four small things, but those four small things are the things that are basically unnecessary constraints that HTML should never have had, so it removes the constraints. It sits on top of Starlet, which is a very nice kind of lower level platform for building these kind of web applications. The actual interface matches as closely as possible to FastAPI, which is a really nice system for creating the kind of classic JavaScript type applications. And Sebastian, who wrote FastAPI, has been kind enough to help me think through some of these design decisions, and so forth. I mean, everybody involved has been super helpful. Actually, I chatted to Carson, who created HTMX, you know, so about it. Some of the folks involved in Django, like everybody in the community I've spoken to definitely realizes there's a big gap to be filled around, like, highly scalable, web foundation-based, pure Python framework with a minimum of fuss. So yeah, I'm getting a lot of support and trying to make sure that FastHTML works well for people.Swyx [00:42:38]: I would say, when I heard about this, I texted Alexio. I think this is going to be pretty huge. People consider Streamlit and Gradio to be the state of the art, but I think there's so much to improve, and having what you call web foundations and web fundamentals at the core of it, I think, would be really helpful.Jeremy [00:42:54]: I mean, it's based on 25 years of thinking and work for me. So like, FastML was built on a system much like this one, but that was of hell. And so I spent, you know, 10 years working on that. We had millions of people using that every day, really pushing it hard. And I really always enjoyed working in that. Yeah. So, you know, and obviously lots of other people have done like great stuff, and particularly HTMX. So I've been thinking about like, yeah, how do I pull together the best of the web framework I created for FastML with HTMX? There's also things like PicoCSS, which is the CSS system, which by default, FastHTML comes with. Although, as I say, you can pip install anything you want to, but it makes it like super easy to, you know, so we try to make it so that just out of the box, you don't have any choices to make. Yeah. You can make choices, but for most people, you just, you know, it's like the PHP in your home directory thing. You just start typing and just by default, you'll get something which looks and feels, you know, pretty okay. And if you want to then write a version of Gradio or Streamlit on top of that, you totally can. And then the nice thing is if you then write it in kind of the Gradio equivalent, which will be, you know, I imagine we'll create some kind of pip installable thing for that. Once you've outgrown, or if you outgrow that, it's not like, okay, throw that all away and start again. And this like whole separate language that it's like this kind of smooth, gentle path that you can take step-by-step because it's all just standard web foundations all the way, you know.Swyx [00:44:29]: Just to wrap up the sort of open source work that you're doing, you're aiming to create thousands of projects with a very, very small team. I haven't heard you mention once AI agents or AI developer tooling or AI code maintenance. I know you're very productive, but you know, what is the role of AI in your own work?Jeremy [00:44:47]: So I'm making something. I'm not sure how much I want to say just yet.Swyx [00:44:52]: Give us a nibble.Jeremy [00:44:53]: All right. I'll give you the key thing. So I've created a new approach. It's not called prompt engineering. It's called dialogue engineering. But I'm creating a system for doing dialogue engineering. It's currently called AI magic. I'm doing most of my work in this system and it's making me much more productive than I was before I used it. So I always just build stuff for myself and hope that it'll be useful for somebody else. Think about chat GPT with code interpreter, right? The basic UX is the same as a 1970s teletype, right? So if you wrote APL on a teletype in the 1970s, you typed onto a thing, your words appeared at the bottom of a sheet of paper and you'd like hit enter and it would scroll up. And then the answer from APL would be printed out, scroll up, and then you would type the next thing. And like, which is also the way, for example, a shell works like bash or ZSH or whatever. It's not terrible, you know, like we all get a lot done in these like very, very basic teletype style REPL environments, but I've never felt like it's optimal and everybody else has just copied chat GPT. So it's also the way BART and Gemini work. It's also the way the Claude web app works. And then you add code interpreter. And the most you can do is to like plead with chat GPT to write the kind of code I want. It's pretty good for very, very, very beginner users who like can't code at all, like by default now the code's even hidden away, so you never even have to see it ever happened. But for somebody who's like wanting to learn to code or who already knows a bit of code or whatever, it's, it seems really not ideal. So okay, that's one end of the spectrum. The other end of the spectrum, which is where Sean's work comes in, is, oh, you want to do more than chat GPT? No worries. Here is Visual Studio Code. I run it. There's an empty screen with a flashing cursor. Okay, start coding, you know, and it's like, okay, you can use systems like Sean's or like cursor or whatever to be like, okay, Apple K in cursors, like a creative form that blah, blah, blah. But in the end, it's like a convenience over the top of this incredibly complicated system that full-time sophisticated software engineers have designed over the past few decades in a totally different environment as a way to build software, you know. And so we're trying to like shoehorn in AI into that. And it's not easy to do. And I think there are like much better ways of thinking about the craft of software development in a language model world to be much more interactive, you know. So the thing that I'm building is neither of those things. It's something between the two. And it's built around this idea of crafting a dialogue, you know, where the outcome of the dialogue is the artifacts that you want, whether it be a piece of analysis or whether it be a Python library or whether it be a technical blog post or whatever. So as part of building that, I've created something called Claudette, which is a library for Claude. I've created something called Cosette, which is a library for OpenAI. They're libraries which are designed to make those APIs much more usable, much easier to use, much more concise. And then I've written AI magic on top of those. And that's been an interesting exercise because I did Claudette first, and I was looking at what Simon Willison did with his fantastic LLM library. And his library is designed around like, let's make something that supports all the LLM inference engines and commercial providers. I thought, okay, what if I did something different, which is like make something that's as Claude friendly as possible and forget everything else. So that's what Claudette was. So for example, one of the really nice things in Claude is prefill. So by telling the assistant that this is what your response started with, there's a lot of powerful things you can take advantage of. So yeah, I created Claudette to be as Claude friendly as possible. And then after I did that, and then particularly with GPT 4.0 coming out, I kind of thought, okay, now let's create something that's as OpenAI friendly as possible. And then I tried to look to see, well, where are the similarities and where are the differences? And now can I make them compatible in places where it makes sense for them to be compatible without losing out on the things that make each one special for what they are. So yeah, those are some of the things I've been working on in that space. And I'm thinking we might launch AI magic via a course called how to solve it with code. The name is based on the classic Polya book, if you know how to solve it, which is, you know, one of the classic math books of all time, where we're basically going to try to show people how to solve challenging problems that they didn't think they could solve without doing a full computer science course, by taking advantage of a bit of AI and a bit of like practical skills, as particularly for this like whole generation of people who are learning to code with and because of ChatGPT. Like I love it, I know a lot of people who didn't really know how to code, but they've created things because they use ChatGPT, but they don't really know how to maintain them or fix them or add things to them that ChatGPT can't do, because they don't really know how to code. And so this course will be designed to show you how you can like either become a developer who can like supercharge their capabilities by using language models, or become a language model first developer who can supercharge their capabilities by understanding a bit about process and fundamentals.Alessio [00:50:19]: Nice. That's a great spoiler. You know, I guess the fourth time you're going to be on learning space, we're going to talk about AI magic. Jeremy, before we wrap, this was just a great run through everything. What are the things that when you next come on the podcast in nine, 12 months, we're going to be like, man, Jeremy was like really ahead of it. Like, is there anything that you see in the space that maybe people are not talking enough? You know, what's the next company that's going to fall, like have drama internally, anything in your mind?Jeremy [00:50:47]: You know, hopefully we'll be talking a lot about fast HTML and hopefully the international community that at that point has come up around that. And also about AI magic and about dialogue engineering. Hopefully dialogue engineering catches on because I think it's the right way to think about a lot of this stuff. What else? Just trying to think about all on the research side. Yeah. I think, you know, I mean, we've talked about a lot of it. Like I think encoder decoder architectures, encoder only architectures, hopefully we'll be talking about like the whole re-interest in BERT that BERT 24 stimulated.Swyx [00:51:17]: There's a safe space model that came out today that might be interesting for this general discussion. One thing that stood out to me with Cartesia's blog posts was that they were talking about real time ingestion, billions and trillions of tokens, and keeping that context, obviously in the state space that they have.Jeremy [00:51:34]: Yeah.Swyx [00:51:35]: I'm wondering what your thoughts are because you've been entirely transformers the whole time.Jeremy [00:51:38]: Yeah. No. So obviously my background is RNNs and LSTMs. Of course. And I'm still a believer in the idea that state is something you can update, you know? So obviously Sepp Hochreiter came up, came out with xLSTM recently. Oh my God. Okay. Another whole thing we haven't talked about, just somewhat related. I've been going crazy for like a long time about like, why can I not pay anybody to save my KV cash? I just ingested the Great Gatsby or the documentation for Starlet or whatever, you know, I'm sending it as my prompt context. Why are you redoing it every time? So Gemini is about to finally come out with KV caching, and this is something that Austin actually in Gemma.cpp had had on his roadmap for years, well not years, months, long time. The idea that the KV cache is like a thing that, it's a third thing, right? So there's RAG, you know, there's in-context learning, you know, and prompt engineering, and there's KV cache creation. I think it creates like a whole new class almost of applications or as techniques where, you know, for me, for example, I very often work with really new libraries or I've created my own library that I'm now writing with rather than on. So I want all the docs in my new library to be there all the time. So I want to upload them once, and then we have a whole discussion about building this application using FastHTML. Well nobody's got FastHTML in their language model yet, I don't want to send all the FastHTML docs across every time. So one of the things I'm looking at doing in AI Magic actually is taking advantage of some of these ideas so that you can have the documentation of the libraries you're working on be kind of always available. Something over the next 12 months people will be spending time thinking about is how to like, where to use RAG, where to use fine-tuning, where to use KV cache storage, you know. And how to use state, because in state models and XLSTM, again, state is something you update. So how do we combine the best of all of these worlds?Alessio [00:53:46]: And Jeremy, I know before you talked about how some of the autoregressive models are not maybe a great fit for agents. Any other thoughts on like JEPA, diffusion for text, any interesting thing that you've seen pop up?Jeremy [00:53:58]: In the same way that we probably ought to have state that you can update, i.e. XLSTM and state models, in the same way that a lot of things probably should have an encoder, JEPA and diffusion both seem like the right conceptual mapping for a lot of things we probably want to do. So the idea of like, there should be a piece of the generative pipeline, which is like thinking about the answer and coming up with a sketch of what the answer looks like before you start outputting tokens. That's where it kind of feels like diffusion ought to fit, you know. And diffusion is, because it's not autoregressive, it's like, let's try to like gradually de-blur the picture of how to solve this. So this is also where dialogue engineering fits in, by the way. So with dialogue engineering, one of the reasons it's working so well for me is I use it to kind of like craft the thought process before I generate the code, you know. So yeah, there's a lot of different pieces here and I don't know how they'll all kind of exactly fit together. I don't know if JEPA is going to actually end up working in the text world. I don't know if diffusion will end up working in the text world, but they seem to be like trying to solve a class of problem which is currently unsolved.Alessio [00:55:13]: Awesome, Jeremy. This was great, as usual. Thanks again for coming back on the pod and thank you all for listening. Yeah, that was fantastic. Get full access to Latent Space at www.latent.space/subscribe

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Segment Anything 2: Demo-first Model Development

Latent Space: The AI Engineer Podcast â€” CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

Play Episode Listen Later Aug 7, 2024 63:30

Because of the nature of SAM, this is more video heavy than usual. See our YouTube!Because vision is first among equals in multimodality, and yet SOTA vision language models are closed, we've always had an interest in learning what's next in vision. Our first viral episode was Segment Anything 1, and we have since covered LLaVA, IDEFICS, Adept, and Reka. But just like with Llama 3, FAIR holds a special place in our hearts as the New Kings of Open Source AI.The list of sequels better than the originals is usually very short, but SAM 2 delighted us by not only being a better image segmentation model than SAM 1, it also conclusively and inexpensively solved video segmentation in just an elegant a way as SAM 1 did for images, and releasing everything to the community as Apache 2/CC by 4.0.“In video segmentation, we observe better accuracy, using 3x fewer interactions than prior approaches. In image segmentation, our model is more accurate and 6x faster than the Segment Anything Model (SAM).”Surprisingly EfficientThe paper reports that SAM 2 was trained on 256 A100 GPUs for 108 hours (59% more than SAM 1). Taking the upper end $2 A100 cost off gpulist.ai means SAM2 cost ~$50k to train if it had an external market-rate cost - surprisingly cheap for adding video understanding!The newly released SA-V dataset is also the largest video segment dataset to date, with careful attention given to scene/object/geographical diversity, including that of annotators. In some ways, we are surprised that SOTA video segmentation can be done on only ~50,000 videos (and 640k masklet annotations). Model-in-the-loop Data Engine for Annotations and Demo-first DevelopmentSimilar to SAM 1, a 3 Phase Data Engine helped greatly in bootstrapping this dataset. As Nikhila says in the episode, the demo you see wasn't just for show, they actually used this same tool to do annotations for the model that is now demoed in the tool:“With the original SAM, we put a lot of effort in building a high-quality demo. And the other piece here is that the demo is actually the annotation tool. So we actually use the demo as a way to improve our annotation tool. And so then it becomes very natural to invest in building a good demo because it speeds up your annotation. and improve the data quality, and that will improve the model quality. With this approach, we found it to be really successful.”An incredible 90% speedup in annotation happened due to this virtuous cycle which helped SA-V reach this incredible scale.Building the demo also helped the team live the context that their own downstream users, like Roboflow, would experience, and forced them to make choices accordingly.As Nikhila says:“It's a really encouraging trend for not thinking about only the new model capability, but what sort of applications folks want to build with models as a result of that downstream.I think it also really forces you to think about many things that you might postpone. For example, efficiency. For a good demo experience, making it real time is super important. No one wants to wait. And so it really forces you to think about these things much sooner and actually makes us think about what kind of image encoder we want to use or other things. hardware efficiency improvements. So those kind of things, I think, become a first-class citizen when you put the demo first.”Indeed, the team swapped out standard ViT-H Vision Transformers for Hiera (Hierarchical) Vision Transformers as a result of efficiency considerations.Memory AttentionSpeaking of architecture, the model design is probably the sleeper hit of a project filled with hits. The team adapted SAM 1 to video by adding streaming memory for real-time video processing:Specifically adding memory attention, memory encoder, and memory bank, which surprisingly ablated better than more intuitive but complex architectures like Gated Recurrent Units.One has to wonder if streaming memory can be added to pure language models with a similar approach… (pls comment if there's an obvious one we haven't come across yet!)Video PodcastTune in to Latent Space TV for the video demos mentioned in this video podcast!Timestamps* [00:00:00] The Rise of SAM by Udio (David Ding Edit)* [00:03:07] Introducing Nikhila* [00:06:38] The Impact of SAM 1 in 2023* [00:12:15] Do People Finetune SAM?* [00:16:05] Video Demo of SAM* [00:20:01] Why the Demo is so Important* [00:23:23] SAM 1 vs SAM 2 Architecture* [00:26:46] Video Demo of SAM on Roboflow* [00:32:44] Extending SAM 2 with other models* [00:35:00] Limitations of SAM: Screenshots* [00:38:56] SAM 2 Paper* [00:39:15] SA-V Dataset and SAM Data Engine* [00:43:15] Memory Attention to solve Video* [00:47:24] "Context Length" in Memory Attention* [00:48:17] Object Tracking* [00:50:52] The Future of FAIR* [00:52:23] CVPR, Trends in Vision* [01:02:04] Calls to ActionTranscript[00:00:00] [music intro][00:02:11] AI Charlie: Happy Yoga! This is your AI co host Charlie. Thank you for all the love for our special 1 million downloads Wins of AI Winter episode last week, especially Sam, Archie, Trellis, Morgan, Shrey, Han, and more. For this episode, we have to go all the way back to the first viral episode of the podcast Segment Anything Model and the Hard Problems of Computer Vision, which we discussed with Joseph Nelson of Roboflow.[00:02:39] AI Charlie: Since Meta released SAM 2 last week, we are delighted to welcome Joseph back as our fourth guest co host to chat with Nikhila Ravi, Research Engineering Manager at Facebook AI Research and lead author of SAM 2. Just like our SAM 1 podcast, this is a multimodal pod because of the vision element, so we definitely encourage you to hop over to our YouTube at least for the demos, if not our faces.[00:03:04] AI Charlie: Watch out and take care.[00:03:10] Introducing Nikhila[00:03:10] swyx: Welcome to the latest podcast. I'm delighted to do segment anything to our first, one of our very first viral podcasts was segment anything one with Joseph. Welcome back. Thanks so much. And this time we are joined by the lead author of Segment Anything 2, Nikki Ravi, welcome.[00:03:25] Nikhila Ravi: Thank you. Thanks for having me.[00:03:26] swyx: There's a whole story that we can refer people back to episode of the podcast way back when for the story of Segment Anything, but I think we're interested in just introducing you as a researcher, as a, on the human side what was your path into AI research? Why, you know, why did you choose computer vision coming out of your specialization at Cambridge?[00:03:46] Nikhila Ravi: So I did my undergraduate. Degree in engineering at Cambridge university. The engineering program is very general. So first couple of years, you sort of study everything from mechanical engineering to fluid mechanics, structural mechanics, material science, and also computer science.[00:04:04] Nikhila Ravi: Towards the end of my degree, I started taking more classes in machine learning and computational neuroscience, and I really enjoyed it. And actually after graduating from undergrad, I had a place at Oxford to study medicine. And so I was. Initially planning on becoming a doctor, had everything planned and then decided to take a gap year after finishing undergrad.[00:04:28] Nikhila Ravi: And actually that was around the time that sort of deep learning was emerging. And in my machine learning class in undergrad, I remember one day our professor came in and that was when Google acquired DeepMind. And so that became like a huge thing. We talked about it for the whole class. It kind of really stuck.[00:04:48] Nikhila Ravi: And I was kicked off thinking about, okay, maybe I want to try something different other than medicine. Maybe this is a different path I want to take. And then in the gap year, I did a bunch of coding, worked on a number of projects. Did some sort of freelance contracting work. And then I got a scholarship to come and study in America.[00:05:06] Nikhila Ravi: So I went to Harvard for a year, took a bunch of computer science classes at Harvard and MIT, worked on a number of AI projects, especially in computer vision. I really, really enjoyed working in computer vision. I applied to Facebook and got this job at Facebook, and I've now at Facebook at the time, now Meta, and I've been here for seven years, so very circuitous path, probably not a very unconventional, I didn't do a PhD, I'm not like a research, typical research scientist, definitely came from more of an engineering background, but since being at Meta, Have had amazing opportunities to work across so many different interesting problems in computer vision from 3D computer vision.[00:05:50] Nikhila Ravi: How can you go from images of objects to 3D structures and then going back to 2D computer vision and actually understanding the objects and the pixels and the images themselves. So it's been a very interesting journey over the past seven years.[00:06:05] swyx: It's weird because like, I guess with segment anything too, it's like 4D because you solve time, you know, you started with 3D and now you're solving the 4D.[00:06:14] Nikhila Ravi: Yeah, it's just going from 3D to images to video. It's really covering the full spectrum. And actually, one of the nice things has been, so I think I mentioned I, Wanted to become a doctor, but actually Sam is having so much impact in medicine, probably more than I could have ever had as a doctor myself. So I think, you know, hopefully Sam too can also have a similar sort of impact in medicine and other fields.[00:06:39] The Impact of SAM 1 in 2023[00:06:39] swyx: Yeah. I want to give Joseph a chance to comment. Does that also mirror your, we know your story about going into, into vision, but like in the past year, since we did our podcast on Sam what's been the impact that you've seen?[00:06:51] Joseph Nelson: Segment anything. Set a new standard in computer vision, you know recapping from from the first release to present Sam introduces the ability for models to near zero shot meaning without any training identify kind of perfect polygons and outlines of items and objects inside images and that capability previously required a Lots of manual labeling, lots of manual preparation, clicking very meticulously to create outlines of individuals and people.[00:07:25] Joseph Nelson: And there were some models that attempted to do zero shot segmentation. of items inside images, though none were as high quality as segment anything. And with the introduction of segment anything, you can pass an image with SAM1, SAM2 videos as well, and get perfect pixel perfect outlines of most everything inside the images.[00:07:52] Joseph Nelson: Now there are some edge cases across domains and Similar to the human eye, sometimes you need to say, like, which item maybe you most care about for the downstream task and problem you're working on. Though, SAM has accelerated the rate at which developers are able to use computer vision in production applications.[00:08:13] Joseph Nelson: So, at RoboFlow, we were very quick to enable the community of computer vision developers and engineers to use SAM and apply it to their problems. The principle ways of using SAM, you could kind of use SAM as is to like pass an image and receive back masks. Another use case for SAM is in preparation of data for other types of problems.[00:08:37] Joseph Nelson: So, for example, in the medical domain, let's say that you're working on a problem where you have a bunch of images from a wet lab experiment. And from each of those images, you need to count the presence of a particular protein that reacts to some experiment. To count all the individual protein reactions, You can go in and lab assistants to this day will still like kind of individually count and say what are the presence of all those proteins.[00:09:07] Joseph Nelson: With Segment Anything, it's able to identify all of those individual items correctly. But often you may need to also add like a class name to what the protein is. Or you may need to say, hey, like, I care about the protein portion of this. I don't care about the rest of the portion of this in the image.[00:09:26] Joseph Nelson: And, or what it encourages and asks for the user to do is to provide some visual prompting to say, hey, which part, like, Sam says, hey, I can find segments of anything, but which segments do you care about? And so you can do visual prompting, which is kind of a new primitive that Sam introduced. And so at RoboFlow, we have one portion of our tool stack enables users to very quickly label data.[00:09:48] Joseph Nelson: With segment anything, Sam can already provide, hey, here's where I see the outlines of objects. Or a user can click to prompt to say, Hey, here's where the outlines of objects matter. And I recently pulled statistics from the usage of SAM in RoboFlow over the course of the last year. And users have labeled about 49 million images using segment anything on the hosted side of the RoboFlow platform.[00:10:12] Joseph Nelson: And that's like 5 million in the last 30 days alone. And of those images, We did kind of like a rough bafka napkin calculation of like how much time that has saved. Because, again, the alternative is you're clicking individual points to create a polygon, and with SAM you just click once and it guesses where the polygon is.[00:10:32] Joseph Nelson: And I'm sure in a bit we can maybe screen share and show some examples of what this experience is like. And in that time estimation, it's like, On average saves, you know, maybe a dozen or so seconds. And we estimate that this is probably saved on the order of magnitude of 35 years of time for users.[00:10:53] Nikhila Ravi: That's incredible.[00:10:54] Joseph Nelson: So, I mean, basically like in the first, the first year of a model being available, not only can you say, Hey, I'm just going to go use this model, those numbers that like 49 million images. is an estimate directly related to just the hosted side. So imagine all of the users that are self hosting or using SAM for robotics applications or out in the field or offline where it's not even, like, the time or the image counts are tabulated.[00:11:20] Joseph Nelson: And we're probably talking about, you know, just a fraction of the amount of value that's actually being produced for a number of downstream tasks. So to say that the impact has been You know, people use terms like game changing and these sorts of things. It has changed the industry. It's set a new standard.[00:11:36] Joseph Nelson: And with the release of SAM 2, I think we're about to see an acceleration of those capabilities for a lot of reasons.[00:11:42] Nikhila Ravi: That's really great to hear. I think one of the, really SAM 1 was. How many fields actually rely on manual segmentation? I think we're not really exposed to that. Maybe you are at Roboflow because you get to see all the users of these tools.[00:11:57] Nikhila Ravi: But for me, it was, you know, people working on understanding coral reef bleaching or farmers counting their cows and so many different applications that as a researcher. You never get exposed to, but you can have impact towards. So I think that was really awesome to hear.[00:12:15] Do People Finetune SAM?[00:12:15] swyx: So as sort of audience surrogate, who knows less than the two of you, I'm going to ask a really dumb question maybe, but is everyone using stock, a segment, anything?[00:12:23] swyx: Are they fine tuning for the medical domain? Like how on earth could it work for the medical field without fine tuning, right? Like, is that a thing?[00:12:32] Nikhila Ravi: So I mean, I can give a quick perspective from the research side. So one of the things, design decisions we made in SAM was to not have class labels. And so all the data is annotated in a class agnostic way.[00:12:48] Nikhila Ravi: So anything that has a boundary, we consider to be an object. So for example, in any image, there's lots of small objects. We might not know what the name of them are, but they're If you can draw a boundary around it, so you can imagine that we have 11 million images in the SA 1B dataset, we annotated all the objects, there's many, many small objects.[00:13:12] Nikhila Ravi: And so if you think about cells, they're also kind of small objects, there's probably things in the training data. That looked like it, but we didn't have to label it. And so that means that even when you use SAM for applications that it wasn't really trained for, because we didn't restrict it to a certain set of categories, you can actually use it out of the box without custom adaptation.[00:13:35] Nikhila Ravi: But having said that, there's probably certain domains where you need some expertise in order to be able to segment something properly. And for those use cases, Having some extra fine tuning data would probably help, and we've sort of seen that there's some papers that have come out that do this, and, you know, we'd love to hear, Joseph, how people are collecting data with SAM and fine tuning for their use cases.[00:13:59] Joseph Nelson: Once SAM came out, there were adaptations that said, could we use SAM to be, you know, like, efficient SAM? Like, basically take SAM and maybe accelerate it. And then there were domain adapted SAMs, like CellSAM, for example, out of the UC system. Now, what's interesting is, there's, like, adapting SAM to a domain, there's kind of two ways by which that's done.[00:14:21] Joseph Nelson: One is, as you mentioned, like, potentially SAM doesn't have a good concept of The objects of interest. And so you need to do domain adaptation and increase the accuracy for zero shot prediction. The second way though, is it's not fine tuning. It's actually just prompting. It's just guiding the model existing knowledge.[00:14:42] Joseph Nelson: to say which segments you care about. And both those are actually kind of equally important on the application side. You need to, like, a priori ensure that the objects of interest can be correctly segmented and maybe collect data to do that. But even if you had, like, a perfect SAM, like an omniscient SAM that could see every segment in every domain with all pixels perfectly outlined, in production, you would still need some way to Almost like signal to the model what you care about like to paint this picture if you are like a retailer and you are providing Photos of models wearing your clothing on your retail site You may care about you know only the shirt and Sam by default might segment the full person And so there's you know visual prompting that you can do to ensure that you only outline Maybe the shirt for the purposes of swapping in and out different shirts for displaying a given model on a retail page You And so I think what's interesting is that's where, like I wouldn't call it domain adaptation, but that's where, like, when you apply to industry, like, one thing that's particularly important with tooling and enabling SAM to reach its full potential.[00:15:51] swyx: That's really encouraging to hear. I should also think, like, you know, the last time we talked about this, we wanted to, the very natural addition on the class labeling side is the grounding Dino work, right? So I think people, built a grounding SAM and all the other extensions.[00:16:05] Video Demo of SAM[00:16:05] swyx: I think it's, it's probably a good time to cut to a quick demo of SAM2 for people who are, who are tuning in for SAM2 and who better to demo SAM2 than Nikki.[00:16:15] Nikhila Ravi: Sure. So I'll try to narrate what I'm what I'm doing. So audio listeners can also understand. So we have a web demo where anyone can try SAM2 on a video. Here we have a video of someone kicking a football, and I'm going to click on the football to select the object in the first frame. But you can actually select the object in any frame of the video, and this will work.[00:16:40] Nikhila Ravi: The next step is to hit track. So the model's now tracking this in real time. We don't save any of this, it's all running in real time. And now you can see the ball has been tracked throughout the entire video. There's even like a little bit of a challenging case here where the shoe covers the football.[00:16:59] Nikhila Ravi: And actually, you know, the model makes a little bit of a mistake, but that's okay. Because we can actually, here, the model makes a little bit of a mistake here. But you know, we can actually add a refinement click. You can add negative clicks until we get the mask that we want on this frame. And then you can hit track again, and the model will track the object, taking into account the additional information I've provided at that frame.[00:17:25] Nikhila Ravi: We've also added a couple of other fun things you can do on top of the track, like add effects. We can add you know, foreground effects, background effects. And these are just ways of showing how we can use the output from SAM2 as part of other tools like video editing tools. Other systems, so this is just a preview of what you can do with SAM2, but the really cool use cases are places where we might not have even imagined SAM2 being useful.[00:17:54] Nikhila Ravi: So we have a number of examples of things you might want to use it for. There's like underwater videos that it works actually really well for even though we, models never really seen an octopus before and octopus have a lot of moving parts that SAM2 can actually quite effectively. Keep track of all the different tentacles and we can probably see it more clearly if I desaturate the background.[00:18:18] Nikhila Ravi: We can see that actually the tracking of all the different tentacles is Quite accurate. Another challenge with video is that objects can actually become occluded. They can disappear from view and reappear. And a really fun example here is the shuffling cup game, which many of you might have seen. And so here I can click on the ball in the first frame.[00:18:41] Nikhila Ravi: I can also, You know, click on a different cup. And so here, the additional challenge is that there's three cups that look exactly the same. And then there's the ball that will get occluded by the cup. So the ball's no longer visible, the cups are all moving around, they all look the same. But the model actually keeps track of the cup that we selected.[00:19:02] Nikhila Ravi: And, as you can see at the end, here I'll jump to the end so you can see. It actually finds the cup again. I wanted to point out a couple of fun demo UX features that we added that actually really helped with this. So if you can see at the bottom, there's these swim lanes and then the swim lanes, actually the thickness of the swim lane tells you if the object's visible or not.[00:19:22] Nikhila Ravi: So at the beginning, the object's visible,[00:19:25] swyx: the object[00:19:26] Nikhila Ravi: disappears, and then the object comes back. So you can actually visually tell. When the object's being occluded and when it's not, and so it's a nice way of like, knowing if you need to go in and fix the model prediction or not. And so these are some of the UX innovations that we came up with, as well as the model innovations.[00:19:46] Joseph Nelson: One thing that I think is really notable here, there's two things. One is that like, I'd love to have a little bit of a discussion about how the models keeping track of the embedded scene to keep track of the ball and the cup in different places. Put a pause on that for a second.[00:19:59] Why the Demo is so Important[00:19:59] Joseph Nelson: One thing that Meta has put an emphasis on here in a much greater degree than other model releases is the demo experience of recognizing that in addition to having a model that can do zero shot segmentation, you've created a web experience that allows folks to kind of experience both the video effects but the types of UX innovations that encourage usage and adoption.[00:20:23] Joseph Nelson: It's actually kind of reminiscent of The underlying technology of ChatGPT was available prior to the web experience of ChatGPT. Can you talk a bit about why that was a consideration to your team and how you thought about the creation of The demo experience in tandem with training and releasing a new model.[00:20:41] Nikhila Ravi: Yeah, absolutely. I think that's a really great example of how, you know, Chad, GPT was really more of a UX innovation. Obviously it was like a number of research innovations that helped to get to this point. But as you said, like the underlying technology was around for a while. And, you know, putting this UX around as a chat interface helped tremendously with the.[00:21:03] Nikhila Ravi: Adoption and people understanding how it could be useful for real world use cases. And in computer vision, especially, it's so visual. The best way to show how these models work. Is by trying it on your own image or your own video with the original SAM, we put a lot of effort in building like a high quality demo.[00:21:23] Nikhila Ravi: And the other piece here is that the demo is actually the annotation tool. So we actually. Use the demo as a way to improve our annotation tool. And so then it becomes very natural to invest in building a good demo because it speeds up your annotation and improves the data quality and that will improve the model quality.[00:21:43] Nikhila Ravi: With this approach, we found it to be really successful. And obviously externally, people really liked being able to try it. I think, you know, people in fields outside of machine learning would never have tried SAM if we didn't have that demo. And I think that definitely led to a lot of the adoption in, like, diverse fields.[00:22:05] Nikhila Ravi: And so because we saw that with SAM 2, like, the demo was a priority first class citizen from day one. And so we really invested in making that. And I think with SAM2 as well, we wanted to have like a step change in the demo experience. Interactive video segmentation, I think that experience is something that maybe has not had much thought given to it.[00:22:27] Nikhila Ravi: And we really wanted to be like, okay, if we are to design a step changing video segmentation experience, what would that look like? And that really did influence our model. And annotation design as well.[00:22:40] Joseph Nelson: It's a really encouraging trend for not thinking about only the new model capability, but what sort of applications folks want to build with models as a result of that downstream.[00:22:49] Nikhila Ravi: I think it also really forces you to think about many things that you might postpone, for example, efficiency.[00:22:55] Joseph Nelson: Yes.[00:22:55] Nikhila Ravi: For a good demo experience. Making it real time is super important. No one wants to wait. And so it really forces you to think about these things much sooner and actually makes us think about how to, what kind of image encoder we want to use or like other hardware efficiency improvements.[00:23:13] Nikhila Ravi: So those kinds of things, I think, become a first class citizen when you put the demo first.[00:23:19] SAM 1 vs SAM 2 Architecture[00:23:19] Joseph Nelson: That's one thing I was going to ask about, and this is related to the architecture change. So SAM1 and the SAM1 demo experience. You have the encoder that's creating the embeddings of all the potential spaces.[00:23:31] Joseph Nelson: That needs to be run on a GPU. That's a relatively intensive operation. But then the query of those embeddings can be run independently and on a cheaper process. So in the SAM1 demo, the way that it was structured, and also this is the way that we have our SAM tool structured in Robloflow as well, is images go to a GPU to get all the SAM based embeddings.[00:23:53] Joseph Nelson: But then for querying those embeddings, we do that client side, in the browser, so that the user can very quickly, you know, you can move your mouse over and you get the proposed candidate masks that Sam found for that region of the image. In SAM 2 you dropped that in the web demo. And I think that's because you made some notable improvements to the rate at which encoding happens.[00:24:16] Joseph Nelson: Can you talk a bit about what led to those speed increases and, again, how that interplays with providing a fast encryption? user experience for interacting with the model.[00:24:29] Nikhila Ravi: Yeah. So the SAM2 web demo is primarily focused on video. We, we decided to just keep it simple and focus on video and on GitHub, we have a Colab notebook that shows how to run SAM2 on images.[00:24:41] Nikhila Ravi: So if you're interested in using, replacing SAM with SAM2 for images, check out GitHub, but on the SAM2 demo, it's not as straightforward to adopt the same architecture as SAM. For video, because we can't send the per frame image embeddings for an entire video back to the front end. In SAM, each frame embedding was like four megabytes, but if you have a long video and that's like per frame, it would become impossible to send that back to the front end.[00:25:11] Nikhila Ravi: So, SAM 2 actually, in terms of the architecture details, I was actually just looking at this earlier, but SAM1 model was around 630 million parameters. It's a fraction of the size of these large language models, but very small. Actually, SAM2, the largest model, is around 224 million parameters. So it's actually One third the size of the SAM original model.[00:25:38] Nikhila Ravi: So we changed the imaging coder from A-V-I-T-H and SAM to a higher model, which has also developed by by meta. So that definitely was something that helped. And in terms of the efficiency compared to sam, so if we were to run SAM per frame on a video or run SAM two, it's around six times faster to run SAM two versus run SAM per frame.[00:26:03] Nikhila Ravi: A number of things improved the efficiency of SAM2 such that we were actually able to run this entirely on the server and not have any component in the front end. But I am very curious to see who puts this on device, like I'm pretty sure soon we'll see like an on device SAM2 or, you know, maybe even running in the browser or something, so.[00:26:25] Nikhila Ravi: I think that could definitely unlock some of these edge use cases that we were able to make a compelling web demo without having to do that.[00:26:34] swyx: Hugging face is probably already working on Transformers. js version of it, but totally makes sense. I want to talk about more about things from the paper, but I think we're still in this sort of demo section.[00:26:42] Video Demo of SAM on Roboflow[00:26:42] swyx: And so I want to hand it to Joseph for his demo to see what the RoboFlow site looks like.[00:26:47] Joseph Nelson: So I can, I can give some context into one key area that Nicola, you mentioned earlier, which is. Sam has made the decision, both Sam 1 and Sam 2, to be class agnostic in terms of its predictions. And that, you then have the ability to have a generalizable, model for zero shot capability.[00:27:05] Joseph Nelson: However, in a lot of domain applications, you do want the class wise name. And so a lot of the challenge can be adding that class wise name for the, at least the annotation to an experience that we've created. That's one of the key considerations. So I will similarly Share my screen and show an example.[00:27:27] Joseph Nelson: Here, I have a bunch of images, and there's a number of ways that I could annotate things, like I could prompt a large multimodal model with like grounding capabilities, you know, you could outsource it, or I can do manual labeling. And with the manual labeling, this is where we make use of models like segment anything.[00:27:45] Joseph Nelson: to propose candidate masks and make it faster. So we have, you know, this annotation pane and what we call the smart poly tool, which is powered by Segment Anything. This is currently Segment Anything 1. We're accelerating and seeing improvements from similar to what the paper shows of Segment Anything 2 performed better on E3.[00:28:06] Joseph Nelson: Images as well as video, but with a segment, anything I'm able to basically prompt regions of my image of interest. So for example, if like, I wanted to say, I want to like add the drum set. You'll see here that like, the original candidate proposal is just the base drum, but let's say I wanted the whole drum set.[00:28:26] Joseph Nelson: So the UX primitive of being able to add and subtract candidate regions of interest is really intuitive here. And now, great, I have this outline, but in fact what I want is, I want to name that as a class. Because maybe for the model that I'm building, I want to build like a task specific model, you know, like an object detection model or an instant segmentation model.[00:28:50] Joseph Nelson: Or, you know, maybe I'm even using like a multimodal model and I want that multimodal model to refer to regions of interest in the images as a specific thing. And so I think what's, you know, really powerful is, of course, like, I get this really rich zero shot prediction. And here we have our friend Rick.[00:29:10] Joseph Nelson: So I get this really rich candidate set of predictions. But then by adding the class wise label, I can, you know, very quickly make sure that any downstream tasks are aware not just of the segment, but also of the, what is inside that segment. Which actually takes me to A separate point of something that I predict that's probably going to happen and Nikhil, I'm actually kind of interested why maybe your team made a conscious decision to not do this initially with SAM2.[00:29:40] Joseph Nelson: There's been an emergent set of models that are also adding open text prompting capabilities to grounding models. So for example, like you've seen models like Grounding Dino or Owlvit, which, you know, you can do. Even image to image or text to image based prompting to find regions of interest. And maybe maybe I can actually give an example of that even in the context of this same data.[00:30:05] Joseph Nelson: So if I wanted to try out, you know, grounding dino on this same set of images, I could try out, you know, prompting grounding dino for a set of different classes. And what's notable is let's do, I don't know, let's prompt for person and we'll prompt for person and prompt for I don't know, microphone.[00:30:26] Joseph Nelson: NLASC or microphone. Here I can text prompt the image and then the understanding, in this case Grounding Dino's understanding, of where people are in this image allows me to create, in this case, bounding boxes, but, you know, soon you can do segmentations or in tandem with SAM do segmentations. And, you know, we've already seen applications of using SAM2 in tandem with models like Grounding Dino or Florence 2.[00:30:54] Joseph Nelson: So that people can basically text prompt and then get the benefits of the zero shot segmentation at the same time as getting the open form querying. And in doing so, you know, we maintain a framework called like autodistill so like folks can very quickly, you know, bring some images and then using autodistill to find some ontology and then prompt and say what you want from that ontology.[00:31:19] Nikhila Ravi: So you already do this for video as well?[00:31:21] Joseph Nelson: You can apply videos or groups of images, yes. So this is using a project called Autodistill. And the concept of Autodistill is, use a base model, like a big base model, which could be like SAM or Grounding Dino, and then you pass a directory of images, which also could be video, broken into individual frames, and you pass an ontology as well.[00:31:43] Joseph Nelson: So an example I was just showing was like the hello world we have, which is like a shipping container. And then the combination of the grounding capabilities of, in the example I was showing, Florence 2 plus SAM, looks for the concept of container, and then SAM does the rich segmentation of turning that concept of container into the candidate proposal of the region, so that a user could just say, hey, I want all the shipping containers, run this across a bunch of images or video frames, And then get back the class wise labels plus the regions of interest.[00:32:17] Joseph Nelson: And this feels like a natural extension. And in fact, like the open form grounding capabilities between SAM1 and SAM2 became something the field was broadly doing. So I'm curious, like, from your perspective, one of the things I thought maybe SAM2 would do is actually add this capability natively. So I'm curious to hear, like, the conscious decision to say, hey, we want to continue to be class agnostic.[00:32:39] Extending SAM 2 with other models[00:32:39] Joseph Nelson: We don't want to add yet maybe open form text prompting as a part of finding the segments and parts of images. And I'd love to hear about like the decision to think about it that way. And if you are encouraged or if you want kind of like what's happening here where people are naturally combining these capabilities as something that you would expect and encourage to happen despite not having it.[00:33:00] Joseph Nelson: In the base model itself.[00:33:02] Nikhila Ravi: Yeah, it's a great question. So I think it's really cool that the community is taking SAM and taking SAM 2 and building on top of it and coming up with cool applications. We love to see that. That's exactly why we open source our work. And then in terms of why we didn't put it into SAM 2, so as you've probably seen with SAM and SAM 2, it's a fairly narrow problem.[00:33:25] Nikhila Ravi: But we really tried to make it a step change in the capability. And so with each version, we are trying to limit the focus on one thing that we can know we can do really well. And in this case, like the first SAM, it was class agnostic segmentation, but can we do it so well that it's effectively solved?[00:33:47] Nikhila Ravi: And similarly, can we do that same thing, but with Video segmentation. So one step at a time, we are working on each of these problems one at a time so that we can actually deliver something that's really world class and step changing.[00:34:03] Joseph Nelson: So does that mean SAM 3 will have the text prompting? Problem is like the next challenge.[00:34:09] Nikhila Ravi: Who knows, who knows? Maybe the community will, will we'll build that too. So[00:34:15] Joseph Nelson: it makes sense to like very narrowly do something very well. And that's, I think, proven to be well accomplished.[00:34:21] Nikhila Ravi: It's like taking the, the, both the data, the model and the demo, and how can we push all three towards solving one thing really well?[00:34:30] Nikhila Ravi: So we found that. That's like a good recipe and that's what we've limited the focus of these, of each of these models.[00:34:38] swyx: This development reminds me of how, you know, when you do, and you break out the interpretability of ConvNets and you can see like, Oh, this is the edge detection one. I feel like SAM is the edge detection version equivalent.[00:34:51] swyx: And then you build up to whatever the next feature is on top of that.[00:34:54] Limitations of SAM: Screenshots[00:34:54] Joseph Nelson: Can I bring up one? Limitation of SAM. So like we've like even SAM one, SAM two, and the monitor is released at 4 PM Pacific on Monday. We're recording this on 11 AM Pacific on, on, on Thursday. So the, it's very fresh for a lot of the capabilities and.[00:35:09] Joseph Nelson: It is so clear that it is a stepwise change in the capability that, Nikhila, you mentioned your team wants to do, which is extend SAM's zero shot class agnostic capability to video, like, A plus, kind of mission accomplished. One thing that's interesting is finding, like, domain problems where there might be still domain applicability and domain adaptation that is available.[00:35:32] Joseph Nelson: One benchmark that we introduced at CBPR is this thing called RF100, which is like, seven different domain type problems that the industry commonly is working on in vision, like underwater document processing, aerial examples, medicine examples. And one place where interestingly segment anything maybe less performant than other models is handling screenshots.[00:35:57] Joseph Nelson: For example, like a lot of folks that are building agents to interact with the web are particularly interested in that challenge of given a screenshot of a computer, what are all the buttons. And how could I autonomously navigate and prompt and tell it to click? And I can show an example of like maybe what, how like Sam kind of performs on this challenge just to outline some of the context of this problem.[00:36:23] Joseph Nelson: But I'm curious like how you think about limitations like this and what you would expect to want to be the case. So here I just have a notebook where I run Sam on the source image on the left. Or the source image on the left and then Sam output is on the right. And this is just a screenshot of, of a website where we just grab like the top 100 websites by traffic and grab screenshots from them.[00:36:42] Joseph Nelson: One example of a place where I could see the community improving on Sam, and I'm curious how you think about this challenge and maybe why Sam is less well adapted for this type of problem. Is processing screenshots. So I'll share my screen to give an example for, for viewers that are participating here, you see like an example, a screenshot of a website on the left, and then right is SAM two running on that image.[00:37:06] Joseph Nelson: And in the context of agents, folks usually want to have like, Hey, tell me all of the buttons that a, an agent could press. Tell me like maybe the headlines of the articles tell me the individual images and Sam two behaves perhaps predictably, where it outlines like people in the images and like some of like the, the screen text.[00:37:22] Joseph Nelson: I'm curious, like, how you think about a challenge like this for a model that sees everything in the world, what about handling digital contexts? And Why maybe it could perform better here and how you would expect to see improvement for domains that might have been out of distribution from the training data?[00:37:40] Nikhila Ravi: Yeah, this is a good question. So fair, we don't really build with a specific use case in mind. We try to build like these foundational models that can be applied to lots of different use cases out of the box. So I think in this kind of example, potentially people might want to annotate some data.[00:37:59] Nikhila Ravi: Fine tune on top of what we release. I think we probably won't build things that are very custom for different use cases. I think that's not a direction we'll go in, but as you said, like the model is an annotation tool to improve the model. And so I think that's definitely the approach we want to take is we provide the tools for you to improve the model as well as the model itself.[00:38:27] Joseph Nelson: That makes sense. Focus on like as many. Multi or zero shot problems and then allow the community to pick up the torch for domain adaptation.[00:38:34] Nikhila Ravi: Yeah, absolutely. Like, we can't solve all the problems ourselves. Like, we can't solve all the different domains. But if we can provide a sort of base hammer tool, and then people can apply it to all their different problems.[00:38:48] SAM 2 Paper[00:38:48] swyx: If you don't mind, I guess we want to transition to a little bit on like asking more questions about the paper.[00:38:53] Udio AI: Sure.[00:38:54] swyx: There's a lot in here. I love the transparency from Meta recently with like LLAMA 3 last week and then, and was it last week? Maybe, maybe a little bit less than last week. But just like just really, really well written and a lot of disclosures, including the data set as well.[00:39:08] SA-V Dataset and SAM Data Engine[00:39:08] swyx: I think the top question that people had on the data set, you know, you release a diverse videos and there was, there's a lot of discussion about the data engine as well, which I really love. And I think it's innovative if you wanted. I think the top question is like, how do you decide the size of data set?[00:39:22] swyx: You know, what were you constrained by? People are asking about scaling laws. You had some ablations, but as a research manager for this whole thing, like how do you decide what you need?[00:39:32] Nikhila Ravi: Yeah. I mean, it's a great question. I think it's, as with all papers, you write them at the end of the project, so we can put these nice plots at the end, but going into it, I think, you know, the data engine design really follows.[00:39:47] Nikhila Ravi: So, this is sort of the model design, how we thought about the task, how we thought of the model capabilities. You can really see it's reflected in the different phases of the data engine. We started with just SAM, we apply SAM per frame. That's like the most basic way of extending SAM to video. Then the most obvious thing to do is to take the output masks from SAM and then provide it as input into a video object segmentation model that takes the mask as the first frame input.[00:40:19] Nikhila Ravi: And that's exactly what we did. We had SAM plus a version of SAM2 that only had mask as input. And then in the last phase, we got rid of SAM entirely and just had this one unified model that can do both image. And video segmentation. And I can do everything in just one model. And we found that, you know, going from each phase, it both improved the efficiency and it improved the data quality.[00:40:46] Nikhila Ravi: And in particular, when you get rid of this two part model, one of the advantages is that when you make refinement clicks, so, You prompt the model in one frame to select an object, then you propagate those predictions to all the other frames of the video to track the object. But if the model makes a mistake and you want to correct it, when you have this unified model, you only need to provide refinement clicks.[00:41:14] Nikhila Ravi: So you can provide maybe a negative click to remove a region or a positive click to add a region. But if you had this decoupled model, you would have to Delete that frame prediction and re annotate from scratch. And so you can imagine for more complex objects, this is actually adding like a lot of extra time to redefine that object every time you want to make a correction.[00:41:39] Nikhila Ravi: So both the data and the data engine phases really follow, like how we thought about the model design and the evolution of the capabilities, because it really helped us to do that. improve the data quality and the annotation efficiency as well.[00:41:54] swyx: Yeah, you had a really nice table with like time taken to annotate and it was just going down and down.[00:41:58] swyx: I think it was like down by like 90 percent by the time you hit stage[00:42:02] Joseph Nelson: three, which is kind of cool. We joke that when SAM 1 came out at RoboFlow, we're like, was this purpose built for our software? Like you have like the embedding, you have the embedding take like a big model and the querying of the embeddings A smaller model that happens in browser, which felt remarkably aligned.[00:42:18] Joseph Nelson: Now hearing you talk about how you think about building models with a demo in mind, it makes sense. Like, you're thinking about the ways that folks downstream are going to be consuming and creating value. So, what felt like maybe a coincidence was perhaps a deliberate choice by Meta to take into account how industry is going to take Seminal advances and apply them.[00:42:36] Nikhila Ravi: Yeah. And it's not just humans. Like it could also be a model that outputs boxes that then get fed into this model. So really thinking about this as a component that could be used by a human or as a component, as part of a, of a larger AI system. And that has, you know, a number of design requirements. It needs to be promptable.[00:42:56] Nikhila Ravi: It needs to be, have the zero shot generalization capability. We, you know, need it to be real time and. Those requirements really are very core to how we think about these models.[00:43:08] Memory Attention to solve Video[00:43:08] swyx: I cannot end this podcast without talking about the architecture, because this is your, effectively the sort of research level, architecture level innovation that enabled what I've been calling object permanence for SAM.[00:43:22] swyx: And it's memory retention. What was the inspiration going into it? And you know, what did you find?[00:43:27] Nikhila Ravi: Yeah, so at a high level, the way we think about extending SAM to video is that an image is just a special case of a video that just has one frame. With that idea in mind, we can extend the SAM architecture to be able to support segmentation across videos.[00:43:45] Nikhila Ravi: So this is a quick video that shows how this works. So SAM architecture, we have the image encoder, we have a prompt encoder, we have a mask decoder. You can click on an image. And that basically is a prompt, we use that prompt along with the image embedding to make a mask prediction for that image. Going to SAM2, we can also apply SAM2 to images because we can, you know, as I said, treat an image as a video with a single frame.[00:44:15] Nikhila Ravi: And so when we, in the SAM2 architecture, we introduce this new memory mechanism that consists of three main components. There's memory attention, there's a memory encoder, and then there's a memory bank. And when we apply SAM2 to images, these are effectively not used. And the architecture just collapses down to the original SAM architecture.[00:44:35] Nikhila Ravi: But when we do apply this to video, the memory components become really useful because they provide the context of the target object from Other frames. And so this could be from past frames. It can be from, there's two types of memory. So there's like the condition, conditional frames or the prompted frames, which are basically the frames at which a user or a model provides input like clicks.[00:45:01] Nikhila Ravi: And then there's like the surrounding frames. And say we use six frames around the current frame as memory of the object. So there's, there's those, those, both those types of memory that we use to make the prediction. Going into a little bit more detail about that, there's like two kinds of memory that we use.[00:45:18] Nikhila Ravi: So one is like spatial memory. So it's like this high resolution memory that captures the spatial details. And then we also have this like longer term object pointer memory that captures some of the sort of higher level concepts. And I think Swyx, you had a comment about how does this relate to sort of context window and LLMs.[00:45:37] Nikhila Ravi: And both of these types of memories have some relation to context window, so they both provide different types of information on the spatial side or in terms of the concept of the objects that we want to track. And so we found that having like six frame length for the spatial memory, Coupled with this longer period of the object pointer memory provides strong video segmentation accuracy at high speed.[00:46:01] Nikhila Ravi: So, as I mentioned, the real time aspect is really important. We have to find this speed accuracy trade off. And one way in which we sort of circumvent this is by allowing additional prompts on subsequent frames. So even if the model makes a mistake, maybe it loses the object. After an occlusion, you can provide another prompt, which actually goes into the memory.[00:46:24] Nikhila Ravi: And so the prompted frames are always in the memory. And so if you provide a prompt on a frame, we will, or the model will always remember what you provided. And so that's a way in which we can sort of avoid some of the model failure cases that actually is a big limitation of current models, current video object segmentation models.[00:46:45] Nikhila Ravi: Don't allow any way to recover if the model makes a mistake. And so, Joseph, going back to your point about the demo, that's something that we found just by playing with these models. There's no way to make a correction, and in many real world use cases, like, it's not going to be a one time prediction, but you actually want to be able to intervene, like, if an LLM makes a mistake, you can actually be like, no, actually do it this way, and provide feedback, and so, We really want to bring some of that thinking into how we build these computer vision models as well.[00:47:16] "Context Length" in Memory Attention[00:47:16] swyx: Amazing. My main reaction to finding out about the context length of eight input frames and six pass frames as their default is why not 60? Why not 600? In text language models, we're very used to severely extending context windows. And what does that do to the memory of your model?[00:47:35] Nikhila Ravi: So I think maybe one, one thing that's different is that the object in video, it is challenging.[00:47:41] Nikhila Ravi: Objects can, you know, change in appearance. There's different lighting conditions. They can deform, but I think a difference to language models is probably the amount of context that you need is significantly less than maintaining a long multi time conversation. And so, you know, coupling this. Short term spatial memory with this, like, longer term object pointers we found was enough.[00:48:03] Nikhila Ravi: So, I think that's probably one difference between vision models and LLMs.[00:48:09] Object Tracking[00:48:09] Joseph Nelson: I think so. If one wanted to be really precise with how literature refers to object re identification, object re identification is not only what SAM does for identifying that an object is similar across frames, It's also assigning a unique ID.[00:48:25] Joseph Nelson: How do you think about models keeping track of occurrences of objects in addition to seeing that the same looking thing is present in multiple places?[00:48:37] Nikhila Ravi: Yeah, it's a good question. I think, you know, SAM2 definitely isn't perfect and there's many limitations that, you know, we'd love to see. People in the community help us address, but one definitely challenging case is where there are multiple similar looking objects, especially if that's like a crowded scene with multiple similar looking objects, keeping track of the target object is a challenge.[00:49:03] Nikhila Ravi: That's still something that I don't know if we've solved perfectly, but again, the ability to provide refinement clicks. That's one way to sort of circumvent that problem. In most cases, when there's lots of similar looking objects, if you add enough refinement clicks, you can get the perfect track throughout the video.[00:49:22] Nikhila Ravi: So definitely that's one way to, to solve that problem. You know, we could have better motion estimation. We could do other things in the model to be able to disambiguate similar looking objects more effectively.[00:49:35] swyx: I'm just interested in leaving breadcrumbs for other researchers, anyone interested in this kind of architecture.[00:49:41] swyx: Like, are there papers that you would refer people to that are influential in your thinking or, you know, have, have other interesting alternative approaches?[00:49:49] Nikhila Ravi: I think there's other ways in which you can do tracking and video. You might not even need the full mask. I think that's it. Some other works that just track like points on objects.[00:49:59] Nikhila Ravi: It really, really depends on what your application is. Like if you don't care about the entire mask, you could just track a bounding box. You could just track a point on an object. And so having the high fidelity mask might not actually be necessary for certain use cases. From that perspective, you might not need the full capabilities.[00:50:19] Nikhila Ravi: of SAM or SAM2. There's many different approaches to tracking, I think I would encourage people to think about like what actually they need for their use case and then try to find something that that fits versus, yeah, maybe SAM2 is too much, you know, maybe you don't even need the full mask.[00:50:37] swyx: Makes total sense, but you have solved the problem that you set out to solve, which is no mean feat, which is something that we're still appreciating even today.[00:50:44] The Future of FAIR[00:50:44] swyx: If there are no further questions, I would just transition to sort of forward looking, future looking stuff. Joseph already hinted at, like, you know, our interest in SAM and the future of SAM, and obviously you're the best person to ask about that. I'm also interested in, like, How should external people think about FAIR, you know, like there's this stuff going on, this llama, this chameleon, this voice box, this image bind, like, how is, how are things organized?[00:51:09] swyx: And, you know, where are things trending?[00:51:11] Nikhila Ravi: Yeah, so in FAIR, we, you know, we have a number of different research areas. I work in an area called perception. So we built vision systems that solve basically, Look at all the fundamental problems in Compute Division. Can we build a step change in all of these different capabilities?[00:51:29] Nikhila Ravi: SAM was one example. SAM2 is another example. There are tons of other problems in Compute Division where we've made a lot of progress, but can we really say that they're solved? And so that's really the area in which I work on. And then there's a number of other research areas in language and in embodied AI.[00:51:49] Nikhila Ravi: And more efficient models and various other topics. So fair in general is still very much pushing the boundaries on solving these foundational problems across different domains. Well,[00:52:07] swyx: fair enough, maybe just outside of fair, just the future of computer vision, right?[00:52:10] CVPR, Trends in Vision[00:52:10] swyx: Like you are very involved in the community. What's the talk of the town at CVPR? Both of you went, who's doing the most interesting work? It's a question for both of you.[00:52:19] Joseph Nelson: I think the trends we're seeing towards more zero shot capability for common examples will accelerate. I think Mutu modality, meaning using, you know, images in tandem with text for richer understanding or images and video in tandem with audio and other mixed media will be a continued acceleration trend.[00:52:43] Joseph Nelson: The way I kind of see the field continuing to progress, the problem statement of computer vision is making sense of visual input. And I think about the world as the things that need to be observed follow your traditional bell curve, where like things that most frequently exist out in the world are on the center of that bell curve.[00:53:05] Joseph Nelson: And then there's things that are less frequently occurring that are in those long tails. For example, you know, as back as like 2014, you have the Cocoa data set, which sets out to say, Hey, can we find 80 common objects in context, like silverware and fridge and these sorts of things. And we also conceptualized the challenge of computer vision in terms of breaking it down into individual task types, because that's like the tools we had for the day.[00:53:29] Joseph Nelson: So that's why, you know, you have the origination of classification, object detection, instant segmentation. And then as you see things continue to progress. You have models and things that need to observe areas in the long tails. And so if you think of the Cocoa dataset as the center of that bell curve, I think of like the long tails, like really edge case problems.[00:53:49] Joseph Nelson: Some of our customers like Rivian, for example, only Rivian knows what the inside of like a Rivian should look like as it's assembled and put together before it makes its way to a customer and they're making custom parts. Right? So how could a model you've been trained on the things that go inside the componentry of producing a vehicle and Andreesen, What's kind of happening with computer vision is you're seeing models that generalize in the middle of the bell curve push outward faster.[00:54:17] Joseph Nelson: That's where you see the advent of like open text models or the richness of understanding of multimodal models. To allow richer understanding without perhaps any training, or maybe just using pre training and applying it to a given problem. And then, there's like, you know, kind of like the messy middle in between those two, right?[00:54:38] Joseph Nelson: So like, Akila kind of talked about examples where SAM does well out of distribution, where like, it finds an octopus, even though there wasn't octopi in the training data. I showed an example where, like, screenshots, where Sam isn't yet super great at screenshots, so maybe that's, like, in the messy middle or in the longer tails for now.[00:54:54] Joseph Nelson: But what's going to happen is there needs to be systems of validating the point of view that I think about, like, tooling to also validate that models are doing what we want them to do, adapting to datasets that we want them to adapt to. And so there's a lot of things on a forward looking basis that allow propelling that expansion of generalizability.[00:55:14] Joseph Nelson: That's for open text problems. That's where scaling up of training, of dataset curation, continues to play a massive role. Something that's notable, I think, about SAM2 is it's, what, 57, 000 videos? 51,[00:55:30] Nikhila Ravi: 000 videos? About 51, 000, yeah.[00:55:32] Joseph Nelson: And 100, 000 internal datasets. That's, like, not Massive, right? And the model size also isn't, you know, the largest, largest model being a couple hundred million parameters.[00:55:43] Joseph Nelson: The smallest model is 38 million parameters and can run at 45 FPS on an A100, right? Like the capabilities of, we're going to see more capable, more generalizable models. Being able to run on a higher wide array of problems with zero or multi shot capability on a faster, a faster rate. And I think the architecture innovations and things like SAM2 of memory, of increasingly like transformers making their way into division and probably blended architectures increasingly too.[00:56:15] Joseph Nelson: So my viewpoint of like on a go forward basis is we will have that bell curve of what humans can see both in the center of that curve and the long tails. And architectural changes allow richer understanding, multi and zero shot, and putting those into systems and putting those into industry and putting those into contexts that allow using them in practical and pragmatic ways.[00:56:38] Joseph Nelson: Nicola, I'd love to hear like your thought and perspective of like how you think the research trends map or don't map to that. And like maybe some of the key innovations that you saw at CVPR this year that, you know, Got you excited about the direction and maybe some promising early directions that you're thinking about researching or pushing the boundaries of further.[00:56:56] Nikhila Ravi: Yeah, I just wanted to actually reply to a couple of things that you said about so actually in video object segmentation, the number of classes. that are annotated in these, and then the size of these datasets are really small. So with SAM, it's, you know, we had a billion masks, we had 11 million images, didn't have class labels.[00:57:17] Nikhila Ravi: But even before that, there were a lot of datasets that have class labels and are annotated. With significantly more with, with like a lot of class labels, whereas in video datasets, the number of class labels are very small. So there's like YouTube VOS, which has 94 object categories, there's Mose, which has around like 30 or so object categories.[00:57:38] Nikhila Ravi: And they're usually like people, there's cars, there's dogs and cats and all these common objects, but not really, they don't really cover a very large number of object categories. And so while Sam learned this general notion of what an object is in an image. These video tracking models actually don't have that knowledge at all.[00:58:01] Nikhila Ravi: And so that's why having this data set is really important for the segment anything capability in video because if you just provide the mask as the input to an off the shelf Video object segmentation model. It might not actually be able to track that arbitrary object mask as effectively as a SAM2 model that's actually trained to track.[00:58:24] Nikhila Ravi: Any object across the entire video. So doing these sort of combining two models together to try to get a capability that will actually only get you so far and being able to actually create that the dataset to enable that anything capability, it was actually really important and we can actually see that when we do comparisons with baselines where we provide some two with the same input mask and the baseline model with the same input mask.[00:58:53] Nikhila Ravi: For example, the t shirt of a person, SAM2 can track the t shirt effectively across the entire video, whereas these baselines might actually start tracking the entire person, because that's what they're used to doing, and isolating it to just one part of the person is not something they were ever trained to do, and so those are sort of some of the limitations.

142 - AI-Winter - wie geht's weiter? (Erstnutzer vs. Powernutzer)

Programmieren lernen - Der Developer Akademie Podcast

Play Episode Listen Later Aug 3, 2024 13:01

Bewerbung für ein Erstgespräch: https://bit.ly/3WILhx4 Nach dem großen AI-Boom hört man zur Zeit nur noch sehr wenig von weiteren großen Entwicklungen. Wir sprechen in dieser Folge über den aktuellen Stand von Künstlicher Intelligenz und beurteilen den Fortschritt als tägliche Nutzer von KI-Tools. YouTube: https://www.youtube.com/c/Programmierenlernen Instagram: https://www.instagram.com/junus.ergin/

stand weiter intelligenz entwicklungen fortschritt nutzer bewerbung erstgespr ki tools ai boom ai winter

The Winds of AI Winter (Q2 Four Wars Recap) + ChatGPT Voice Mode Preview

Latent Space: The AI Engineer Podcast â€” CodeGen, Agents, Computer Vision, Data Science, AI UX and all things Software 3.0

Play Episode Listen Later Aug 2, 2024 115:01

Thank you for 1m downloads of the podcast and 2m readers of the Substack!

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EP8: Save Time by Summarizing, Meta's Game-Changing AI Upgrade, FamilySearch's Summarization Feature, Is the AI Bubble About to Burst?

The Family History AI Show

Play Episode Listen Later Jul 31, 2024 48:26

In this week's episode, hosts Mark Thompson and Steve Little explore Meta AI 3.1's huge large language model upgrade, as well as FamilySearch's innovative, AI-based summarization feature. They then address growing concerns about AI hype. In this week's Tip of the Week, they share their approach for mastering the fine art of summarization, a crucial AI skill for genealogical research. The show rounds off with rapid-fire discussions of Google's privacy policy update, Apple's response to accusations made about their training data, and exciting developments in AI-powered education. Whether you're a tech enthusiast or a family history buff, this episode offers invaluable insights into how AI is revolutionizing genealogy and beyond.Please share this episode with a friend!Timestamps:In the News 01:01 Meta AI 3.1: A Huge Upgrade, and it's Free! 13:09 FamilySearch's New AI Summarization Feature 20:16 Addressing AI Hype Concerns Tip of the Week 24:59 AI Building Blocks: Summarization AI RapidFire 31:25 Google's Privacy Policy Update 36:57 Apple's Response to Training Data Accusations 40:02 Apple vs. Google: Platform Competition Heats Up 44:59 AI in Education: New Developments and PartnershipsResource LinksMeta AI: https://meta.aiFamilySearch: https://www.familysearch.org/en/labs/OpenAI (ChatGPT): https://openai.com/chatgptAnthropic (Claude): https://www.anthropic.comGoogle (Gemini): https://gemini.google.com/appMicrosoft: https://www.microsoft.com/en-us/aiFacebook: https://www.facebook.comApple AI: https://www.apple.com/aiYouTube: https://www.youtube.comKhan Academy: https://www.khanacademy.orgGoogle DeepMind: https://www.deepmind.comAndrej Karpathy's Eureka Labs: https://eurekalabs.ai/Tags: Artificial Intelligence, Family History, Genealogy, Large Language Models, Meta AI, Facebook AI, Family Search, AI Summarization, OCR, Land Records, AI Model Comparison, Open Source AI, AI Adoption, AI Investment, AI Winter, AI Ethics, Data Privacy, Training Data, Google Privacy Policy, YouTube Closed Captions, Apple AI, AI Research, Platform Competition, AI Ecosystems, AI in Education, Khan Academy, Microsoft AI, Andrej Karpathy, AI Tutoring, AI Learning

EA - CEEALAR: 2024 Update by CEEALAR

The Nonlinear Library

Play Episode Listen Later Jul 19, 2024 7:13

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: CEEALAR: 2024 Update, published by CEEALAR on July 19, 2024 on The Effective Altruism Forum. TL;DR: Last November we had only 4 months of runway remaining-today we have ~1.5 years. I'm reminded of the saying 'it takes a village to raise a child', and I would be writing a very different update if not for the village that came together to support us since our fundraising appeal last winter. We received several months of runway as a result of individual donations from alumni and supporters, which gave us the time to approach new funders, as well as encouragement to persevere. Others volunteered their time and energy to support us. Guests at the hotel helped with day-to-day tasks so that we could focus on fundraising, and we received priceless advice from more experienced EAs on how to improve our offering and make the value of our project more apparent to funders. As a result of all the above, with just over a month of runway remaining, we received an emergency grant from AISTOF[1] that ensured our continued operation until the end of the year. And now we've been granted an additional full year of funding from EAIF. MANY thank-yous are due: To those who donated To those who offered their time and advice To those who advocated for us To colleagues, past and present To ML4G, Stampy and PauseAI for choosing us as your venue despite our uncertain future To Wytham Abbey, for their generous donation of equipment To the grant investigators who gave us a chance to explain what this strange little hotel in Blackpool is doing and why it's worth supporting Last but not least - to our grantees and alumni, for being so committed to having a positive impact on the world, and giving us the chance to play a role in your journey. The Future of CEEALAR! AI Winter is Coming to CEEALAR CEEALAR has been hosting grantees working on AI Safety since it opened in 2018, and this winter we're going all in - we're going to be the AI Winter we want to see in the world. From September until the end of the year we're going to direct our outreach and programming toward AI Safety.[2] Keep an eye out for a future update where we'll go more into the details of what we have planned - which isn't much right now, so if you've got ideas and would like to collaborate with us on AI Winter, get in touch! If you'd like a reminder, or are interested in participating or collaborating in some fashion - please fill out this tiny form (

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Discussing the Impending AI Winter and CCaaS Bloodbath

UC Today - Out Loud

Play Episode Listen Later Jun 3, 2024 29:14

Join us as we dive deep into the latest TalkingPointz Insider Report with Dave Michels from TalkingPointz.In this engaging conversation, Dave and UC Today's Rob Scott explore the most significant trends and predictions shaping the enterprise communications landscape. From the potential AI winter to the future of Microsoft Teams and the rise of AI schedulers, this episode covers it all.TalkingPointz Insider Report: Dave provides an overview of the Insider Report, highlighting its relevance for industry insiders, vendors, consultants, and analysts.AI Winter: Exploring the signs of an AI slowdown and what it means for the industry.Microsoft Teams Milestones: Discussing the significance of Microsoft reaching over 1 million Teams Rooms and 20 million PSTN users.AI Schedulers: The game-changing potential of AI in automating calendar management and improving productivity.Customer Engagement Trends: Analyzing the impending consolidation in the CCaaS market and what it means for vendors and customers.Industry Moves: NEC's exit from premises-based UC and Meta's retirement of Workplace, plus the rise of Google Vids as a productivity tool.

ai technology news microsoft workplace collaboration enterprise customer experience uc microsoft teams impending bloodbath tech news video conferencing team collaboration channel partners unified communications rob scott ucaas ai winter ccaas pstn uc today unified communications news

Eric Siegel - Are we headed towards an AI winter?

Agile Digital Transformation

Play Episode Listen Later Feb 15, 2024 22:51

Eric Siegel is a former Columbia professor, leading ML consultant and author of The AI Playbook: Mastering the Rare Art of Machine Learning Deployment, which has just recently been released.In this episode, we talk about whether or not we're headed towards an AI winter and what implications this would have for the future of innovation in artificial intelligence and machine learning. We discuss the law of human-like autonomy and why it is important for these kinds of conversations, and conclude with a look into the future of AI innovation & development.Links & mentions:amazon.com/AI-Playbook-Mastering-Deployment-Managementbizml.comlinkedin.com/in/predictiveanalytics

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Beyond Experimentation: Prof. Yoav Shoham's A121's CEO, talks on Gen AI's Future and AI21 Journey

Future of Tech

Play Episode Listen Later Jan 24, 2024 26:14

In this episode, Avishai sits down with AI pioneer Yoav Shoham. Yoav was a professor of computer science at Stanford for 28 years, where he was the director of the AI Lab. He is also a serial entrepreneur and has founded various companies across industries. Most recently, he co-founded AI21 labs. AI21 Labs aims to take AI to the next level and builds LLMs for enterprises that make machines thought partners. Join him and Avishai for a great discussion, and hear why Yoav doesn't believe Gen AI truly exists, AI21's mission, and his vision for the future. The AI winter is over. AI is at the top of everyone's mind today, but it has existed for decades. Yoav experienced the ‘AI Winter,' a period during the 1990s when interest and funding in AI dropped. Yoav says that the effects of the winter have worn off, and that the learnings and changes from the period and beyond have helped us reach the AI boom of today. Lots of experiments, little deployment. Yoav says that while there is mass experimentation with AI, there is much less deployment of the technology. Proving the ROI of AI is something companies like AI21 must do to encourage uptake from large enterprises. Trust is key. Yoav identifies that reliability, predictability and explainability are key for LLMs. These models often don't know when or why they are wrong. Ensuring that we can trust these models is key to their progression and adoption. Interested in further exploring the impact of GenAI? Tune in to Your Career: Is it Choice or Chance? Podcast for insightful discussions within the workplace domain.

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Build Intelligent Applications Faster With RelationalAI

The Machine Learning Podcast

Play Episode Listen Later Dec 31, 2023 58:24

Summary Building machine learning systems and other intelligent applications are a complex undertaking. This often requires retrieving data from a warehouse engine, adding an extra barrier to every workflow. The RelationalAI engine was built as a co-processor for your data warehouse that adds a greater degree of flexibility in the representation and analysis of the underlying information, simplifying the work involved. In this episode CEO Molham Aref explains how RelationalAI is designed, the capabilities that it adds to your data clouds, and how you can start using it to build more sophisticated applications on your data. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Your host is Tobias Macey and today I'm interviewing Molham Aref about RelationalAI and the principles behind it for powering intelligent applications Interview Introduction How did you get involved in machine learning? Can you describe what RelationalAI is and the story behind it? On your site you call your product an "AI Co-processor". Can you explain what you mean by that phrase? What are the primary use cases that you address with the RelationalAI product? What are the types of solutions that teams might build to address those problems in the absence of something like the RelationalAI engine? Can you describe the system design of RelationalAI? How have the design and goals of the platform changed since you first started working on it? For someone who is using RelationalAI to address a business need, what does the onboarding and implementation workflow look like? What is your design philosophy for identifying the balance between automating the implementation of certain categories of application (e.g. NER) vs. providing building blocks and letting teams assemble them on their own? What are the data modeling paradigms that teams should be aware of to make the best use of the RKGS platform and Rel language? What are the aspects of customer education that you find yourself spending the most time on? What are some of the most under-utilized or misunderstood capabilities of the RelationalAI platform that you think deserve more attention? What are the most interesting, innovative, or unexpected ways that you have seen the RelationalAI product used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on RelationalAI? When is RelationalAI the wrong choice? What do you have planned for the future of RelationalAI? Contact Info LinkedIn (https://www.linkedin.com/in/molham/) Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast (https://www.dataengineeringpodcast.com) covers the latest on modern data management. Podcast.__init__ () covers the Python language, its community, and the innovative ways it is being used. Visit the site (https://www.themachinelearningpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com (mailto:hosts@themachinelearningpodcast.com)) with your story. To help other people find the show please leave a review on iTunes (https://podcasts.apple.com/us/podcast/the-machine-learning-podcast/id1626358243) and tell your friends and co-workers. Links RelationalAI (https://relational.ai/) Snowflake (https://www.snowflake.com/en/) AI Winter (https://en.wikipedia.org/wiki/AI_winter) BigQuery (https://cloud.google.com/bigquery) Gradient Descent (https://en.wikipedia.org/wiki/Gradient_descent) B-Tree (https://en.wikipedia.org/wiki/B-tree) Navigational Database (https://en.wikipedia.org/wiki/Navigational_database) Hadoop (https://hadoop.apache.org/) Teradata (https://www.teradata.com/) Worst Case Optimal Join (https://relational.ai/blog/worst-case-optimal-join-algorithms-techniques-results-and-open-problems) Semantic Query Optimization (https://relational.ai/blog/semantic-optimizer) Relational Algebra (https://en.wikipedia.org/wiki/Relational_algebra) HyperGraph (https://en.wikipedia.org/wiki/Hypergraph) Linear Algebra (https://en.wikipedia.org/wiki/Linear_algebra) Vector Database (https://en.wikipedia.org/wiki/Vector_database) Pathway (https://pathway.com/) Data Engineering Podcast Episode (https://www.dataengineeringpodcast.com/pathway-database-that-thinks-episode-334/) Pinecone (https://www.pinecone.io/) Data Engineering Podcast Episode (https://www.dataengineeringpodcast.com/pinecone-vector-database-similarity-search-episode-189/) The intro and outro music is from Hitman's Lovesong feat. Paola Graziano (https://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Tales_Of_A_Dead_Fish/Hitmans_Lovesong/) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/)/CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0/)

Episode 049: All Things AI - Big Data Broke the AI Winter

Creative Business Marketing

Play Episode Listen Later Nov 9, 2023 3:14

Topics covered in this episode include… - Importance of vast data availability in internet for AI development, ending its "winter" phase- Impact of internet revolution on data sharing, providing access to large data sets for AI research- Internet's role in boosting collaborative work and technology development, accelerating idea exchange and research - Utilisation of collective processing power of multiple computers via the internet for complex model training, especially with cloud computing- Legal and privacy concerns arising from exploitation of personal data by tech giants, underscoring the permanence of internet data.______________

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AI's Evolution: A Historical Snapshot of Artificial Intelligence for Educators

AI for Teachers

Play Episode Listen Later Sep 11, 2023 15:15 Transcription Available

Discover the Evolution of AI in Education: A Must-Listen for Modern EducatorsUncover the transformative journey of Artificial Intelligence in the educational landscape. This episode dives deep into the milestones that have shaped AI's role in modern education, from Turing's groundbreaking theories to today's AI-driven personalized learning experiences.In just a few minutes, you'll gain:Insight into the Turing Test and its foundational role in AI in education.Understanding programming languages like LISP has been pivotal in shaping AI's educational applications.A look at interactive learning through AI, inspired by early programs like ELIZA.Lessons from the AI Winter that teach us the value of perseverance and resilience in educational innovation.A glimpse into the rapid advancements of 21st-century AI and how they're revolutionizing teaching methods.Why is this episode a must-listen? It's not just about understanding AI; it's about leveraging it to enhance your teaching strategies and student engagement. Learn how to integrate AI responsibly and ethically in your classroom and be at the forefront of educational transformation.

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Gradient Ventures Founder Anna Patterson

How AI Happens

Play Episode Listen Later Aug 9, 2023 26:09

Key Points From This Episode:She shares her professional journey that eventually led to the founding of Gradient Ventures.How Anna would contrast AI Winter to the standard hype cycles that exist.Her thoughts on how the web and mobile sectors were under-hyped.Those who decide if something falls out of favor; according to Anna.How Anna navigates hype cycles.Her process for evaluating early-stage AI companies. How to assess whether someone is a tourist or truly committed to something.Approaching problems and discerning whether AI is the right answer.Her thoughts on the best application for AI or MLR technology. Anna shares why she is excited about large language models (LLMs).Thoughts on LLMs and whether we should or can we approach AGIs.A discussion: do we limit machines when we teach them to speak the way we speak?Quality AI and navigating fairness: the concept of the Human in the Loop.Boring but essential data tasks: whose job is that?How she feels about sensationalism. What gets her fired up when it is time to support new companies. Advice to those forging careers in the AI and ML space. Tweetables:“When that hype cycle happens, where it is overhyped and falls out of favor, then generally that is – what is called a winter.” — @AnnapPatterson [0:03:28]“No matter how hyped you think AI is now, I think we are underestimating its change.” — @AnnapPatterson [0:04:06]“When there is a lot of hype and then not as many breakthroughs or not as many applications that people think are transformational, then it starts to go through a winter.” — @AnnapPatterson [0:04:47]@AnnapPatterson [0:25:17]Links Mentioned in Today's Episode:Anna Patterson on LinkedIn‘Eight critical approaches to LLMs'‘The next programming language is English'‘The Advice Taker'GradientHow AI HappensSama

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Как Ai изменит торговлю: E-commerce и Ритейл?

Digital Voice

Play Episode Listen Later Jun 23, 2023 38:27

Искусственному интеллекту уже давно пророчат статус главной революционной технологии современности и самого важного изобретения со времен появления интернета. Кто-то боится, что компьютеры способны отобрать рабочие места у самых разных слоев населения: от водителей и курьеров до креативного класса и врачей. А кто-то, наоборот, с нетерпением ждет, когда машины станут незаменимым помощником, упрощая и улучшая нашу жизнь. Как на самом деле обстоят дела с применением искусственного интеллекта уже сегодня и каковы перспективы для бизнеса и для разных профессий мы решили поговорить в специальном выпуске Digital Voice: Эпоха искусственного интеллекта. 00:00 Вступление 00:40 Компьютер обыграл Чемпиона Мира по шахматам 01:35 Google объявил начало эпохи Ai в 2016 02:45 Рождение GPT-3 и GPT-4 03:35 Алан Тьюринг и Энигма 05:10 Ai заменит фотографов и моделей 06:40 Заменит ли Искусственный Интеллект нас на рабочих местах? 11:20 Как и для чего правильно применять Ai сегодня? 13:20 Новый уровень персонализации маркетинга 15:55 Prompt Engineering - профессия будущего или мимолетный тренд? 18:42 Искусственный Интеллект становится Ко-пилотом 24:53 Заменит ли ко-пилот слабых специалистов? 26:45 Практические способы применения Ai в торговле 32:50 Почему SAMSUNG запретил применять Ai сотрудникам? 35:05 Ai Winter - наступит ли новая «зимовка»? 36:45 Вывод от Никиты Рвачева Аудиоверсия интервью в подкастах: https://podcast.ru/1535831911 Подпишитесь на ютуб-канал DigitalVoice: https://www.youtube.com/channel/UCdB72xIO4htZaJuKuXtT2eA?sub_confirmation=1 ________ Партнеры проекта: ■ IMSHOP - лучшие мобильные приложения для ритейла : http://imshop.io/?utm_source=youtube&... ■ AWG - ведущий веб-разработчик и интегратор IT решений для крупного ритейла и банков - http://www.awg.ru ■ Dalli-Service - Быстрая доставка для интернет-магазинов: https://www.dalli-service.com ■ Kinescope - Видео сервис для бизнеса - https://kinescope.io/ru ________ Наш сайт: https://www.digitalvoice.ru Наш Telegram: https://t.me/digitalvoice_podcast Telegram канал Андрея Себранта: https://t.me/techsparks Telegram канал Никиты Рвачева: https://t.me/rvnikita_blog

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#247 AI Talk auf der OMR Conference Stage | China & Computer Chips | Outlier in Deutschland | Earnings: Atlassian, Teamviewer, DoorDash

Doppelgänger Tech Talk

Play Episode Listen Later May 9, 2023 68:04

Pip hat sich das Wochenende über eingeschlossen, um 60 Folien zu produzieren. Glöckler hat ein Bußgeld bekommen. Software wird bald anders produziert werden & VW feuert das Management der Software Sparte. Reminder: Heute 16:20 - 17:00 live Podcast auf der OMR Red Stage Philipp Glöckler (https://www.linkedin.com/in/philippgloeckler/) und Philipp Klöckner (https://twitter.com/pip_net) sprechen heute über: (00:00:00) OMR Vorgespräch (00:10:00) Glöckler Rote Ampel (00:17:30) AI, LinkedIn (00:22:00) VWs feuert Software Sparte (00:23:30) Software Monolite (00:28:00) Warum kein AI Winter (00:36:30) Chinas AI Chip Importe (00:45:30) Wo ist Deutschland Outlier (00:58:30) Atlassian Earnings (01:00:00) Teamviewer (01:01:00) DoorDash Shownotes: OMR Agenda: https://www.doppelgaenger.io/omr/ Ostrom (Investment der Doppelgänger): https://join.ostrom.de/?referralCode=@GLOECKLER Deutschland Outlier Tweets: https://twitter.com/giulio_mattioli/status/1653494701222252561 Doppelgänger Tech Talk Podcast Sheet https://doppelgaenger.io/sheet/ Disclaimer https://www.doppelgaenger.io/disclaimer/ Passionfroot Storefront www.passionfroot.xyz/doppelgaenger Post Production by Jan Wagener https://www.linkedin.com/in/jan-wagener-49270018b/ Aktuelle Doppelgänger Werbepartner https://lollipod.de/sn/doppelgaenger-werbung

"DRAKE AI - WINTER'S COLD"

Analytic Dreamz: Notorious Mass Effect

Play Episode Listen Later Apr 28, 2023 7:05

Linktree: https://linktr.ee/Analytic Drake AI - Winter's Cold Original song by @actuallylvcci source: (3) Drake AI - Winter's Cold - YouTubeSupport this podcast at — https://redcircle.com/analytic-dreamz-notorious-mass-effect/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

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Mapping The Data Infrastructure Landscape As A Venture Capitalist

Data Engineering Podcast

Play Episode Listen Later Apr 3, 2023 61:57

Summary The data ecosystem has been building momentum for several years now. As a venture capital investor Matt Turck has been trying to keep track of the main trends and has compiled his findings into the MAD (ML, AI, and Data) landscape reports each year. In this episode he shares his experiences building those reports and the perspective he has gained from the exercise. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Businesses that adapt well to change grow 3 times faster than the industry average. As your business adapts, so should your data. RudderStack Transformations lets you customize your event data in real-time with your own JavaScript or Python code. Join The RudderStack Transformation Challenge today for a chance to win a $1,000 cash prize just by submitting a Transformation to the open-source RudderStack Transformation library. Visit dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) today to learn more Your host is Tobias Macey and today I'm interviewing Matt Turck about his annual report on the Machine Learning, AI, & Data landscape and the insights around data infrastructure that he has gained in the process Interview Introduction How did you get involved in the area of data management? Can you describe what the MAD landscape report is and the story behind it? At a high level, what is your goal in the compilation and maintenance of your landscape document? What are your guidelines for what to include in the landscape? As the data landscape matures, how have you seen that influence the types of projects/companies that are founded? What are the product categories that were only viable when capital was plentiful and easy to obtain? What are the product categories that you think will be swallowed by adjacent concerns, and which are likely to consolidate to remain competitive? The rapid growth and proliferation of data tools helped establish the "Modern Data Stack" as a de-facto architectural paradigm. As we move into this phase of contraction, what are your predictions for how the "Modern Data Stack" will evolve? Is there a different architectural paradigm that you see as growing to take its place? How has your presentation and the types of information that you collate in the MAD landscape evolved since you first started it?~~ What are the most interesting, innovative, or unexpected product and positioning approaches that you have seen while tracking data infrastructure as a VC and maintainer of the MAD landscape? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the MAD landscape over the years? What do you have planned for future iterations of the MAD landscape? Contact Info Website (https://mattturck.com/) @mattturck (https://twitter.com/mattturck) on Twitter MAD Landscape Comments Email (mailto:mad2023@firstmarkcap.com) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links MAD Landscape (https://mad.firstmarkcap.com) First Mark Capital (https://firstmark.com/) Bayesian Learning (https://en.wikipedia.org/wiki/Bayesian_inference) AI Winter (https://en.wikipedia.org/wiki/AI_winter) Databricks (https://www.databricks.com/) Cloud Native Landscape (https://landscape.cncf.io/) LUMA Scape (https://lumapartners.com/lumascapes/) Hadoop Ecosystem (https://www.analyticsvidhya.com/blog/2020/10/introduction-hadoop-ecosystem/) Modern Data Stack (https://www.fivetran.com/blog/what-is-the-modern-data-stack) Reverse ETL (https://medium.com/memory-leak/reverse-etl-a-primer-4e6694dcc7fb) Generative AI (https://generativeai.net/) dbt (https://www.getdbt.com/) Transform (https://transform.co/) Podcast Episode (https://www.dataengineeringpodcast.com/transform-co-metrics-layer-episode-206/) Snowflake IPO (https://www.cnn.com/2020/09/16/investing/snowflake-ipo/index.html) Dataiku (https://www.dataiku.com/) Iceberg (https://iceberg.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/tabular-iceberg-lakehouse-tables-episode-363) Hudi (https://hudi.apache.org/) Podcast Episode (https://www.dataengineeringpodcast.com/hudi-streaming-data-lake-episode-209/) DuckDB (https://duckdb.org/) Podcast Episode (https://www.dataengineeringpodcast.com/duckdb-in-process-olap-database-episode-270/) Trino (https://trino.io/) Y42 (https://www.y42.com/) Podcast Episode (https://www.dataengineeringpodcast.com/y42-full-stack-data-platform-episode-295) Mozart Data (https://www.mozartdata.com/) Podcast Episode (https://www.dataengineeringpodcast.com/mozart-data-modern-data-stack-episode-242/) Keboola (https://www.keboola.com/) MPP Database (https://www.techtarget.com/searchdatamanagement/definition/MPP-database-massively-parallel-processing-database) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

LW - The Prospect of an AI Winter by Erich Grunewald

The Nonlinear Library

Play Episode Listen Later Mar 28, 2023 26:09

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The Prospect of an AI Winter, published by Erich Grunewald on March 27, 2023 on LessWrong. Summary William Eden forecasts an AI winter. He argues that AI systems (1) are too unreliable and too inscrutable, (2) won't get that much better (mostly due to hardware limitations) and/or (3) won't be that profitable. He says, "I'm seeing some things that make me think we are in a classic bubble scenario, and lots of trends that can't clearly continue." I put 5% on an AI winter happening by 2030, with all the robustness that having written a blog post inspires, and where AI winter is operationalised as a drawdown in annual global AI investment of ≥50%.[1] (I reckon a winter must feature not only decreased interest or excitement, but always also decreased funding, to be considered a winter proper.) There have been two previous winters, one 1974-1980 and one 1987-1993. The main factor causing these seems to have been failures to produce formidable results, and as a consequence wildly unmet expectations. Today's state-of-the-art AI systems show impressive results and are more widely adopted (though I'm not confident that the lofty expectations people have for AI today will be met). I think Moore's Law could keep going for decades.[2] But even if it doesn't, there are many other areas where improvements are being made allowing AI labs to train ever larger models: there's improved yields and other hardware cost reductions, improved interconnect speed and better utilisation, algorithmic progress and, perhaps most importantly, an increased willingness to spend. If 1e35 FLOP is enough to train a transformative AI (henceforth, TAI) system, which seems plausible, I think we could get TAI by 2040 (>50% confidence), even under fairly conservative assumptions. (And a prolonged absence of TAI wouldn't necessarily bring about an AI winter; investors probably aren't betting on TAI, but on more mundane products.) Reliability is definitely a problem for AI systems, but not as large a problem as it seems, because we pay far more attention to frontier capabilities of AI systems (which tend to be unreliable) than long-familiar capabilities (which are pretty reliable). If you fix your gaze on a specific task, you usually see a substantial and rapid improvement in reliability over the years. I reckon inference with GPT-3.5-like models will be about as cheap as search queries are today in about 3-6 years. I think ChatGPT and many other generative models will be profitable within 1-2 years if they aren't already. There's substantial demand for them (ChatGPT reached 100M monthly active users after two months, quite impressive next to Twitter's ~450M) and people are only beginning to explore their uses. If an AI winter does happen, I'd guess some of the more likely reasons would be (1) scaling hitting a wall, (2) deep-learning-based models being chronically unable to generalise out-of-distribution and/or (3) AI companies running out of good-enough data. I don't think this is very likely, but I would be relieved if it were the case, given that we as a species currently seem completely unprepared for TAI. The Prospect of a New AI Winter What does a speculative bubble look like from the inside? Trick question -- you don't see it. Or, I suppose some people do see it. One or two may even be right, and some of the others are still worth listening to. William Eden tweeting out a long thread explaining why he's not worried about risks from advanced AI is one example, I don't know of which. He argues in support of his thesis that another AI winter is looming, making the following points: AI systems aren't that good. In particular (argues Eden), they are too unreliable and too inscrutable. It's far harder to achieve three or four nines reliability than merely one or two nines; as an example, autonomous vehicles ...

ai law chatgpt speech trick ea prospect flop 100m gpt reliability tai erich 450m grunewald rationalist ai winter lesswrong

EA - The Prospect of an AI Winter by Erich Grunewald

The Nonlinear Library

Play Episode Listen Later Mar 28, 2023 0:25

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The Prospect of an AI Winter, published by Erich Grunewald on March 27, 2023 on The Effective Altruism Forum. Thanks for listening. To help us out with The Nonlinear Library or to learn more, please visit nonlinear.org.

speech ea prospect erich grunewald rationalist ai winter

LW - The Prospect of an AI Winter by Erich Grunewald

The Nonlinear Library: LessWrong

Play Episode Listen Later Mar 28, 2023 26:09

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The Prospect of an AI Winter, published by Erich Grunewald on March 27, 2023 on LessWrong. Summary William Eden forecasts an AI winter. He argues that AI systems (1) are too unreliable and too inscrutable, (2) won't get that much better (mostly due to hardware limitations) and/or (3) won't be that profitable. He says, "I'm seeing some things that make me think we are in a classic bubble scenario, and lots of trends that can't clearly continue." I put 5% on an AI winter happening by 2030, with all the robustness that having written a blog post inspires, and where AI winter is operationalised as a drawdown in annual global AI investment of ≥50%.[1] (I reckon a winter must feature not only decreased interest or excitement, but always also decreased funding, to be considered a winter proper.) There have been two previous winters, one 1974-1980 and one 1987-1993. The main factor causing these seems to have been failures to produce formidable results, and as a consequence wildly unmet expectations. Today's state-of-the-art AI systems show impressive results and are more widely adopted (though I'm not confident that the lofty expectations people have for AI today will be met). I think Moore's Law could keep going for decades.[2] But even if it doesn't, there are many other areas where improvements are being made allowing AI labs to train ever larger models: there's improved yields and other hardware cost reductions, improved interconnect speed and better utilisation, algorithmic progress and, perhaps most importantly, an increased willingness to spend. If 1e35 FLOP is enough to train a transformative AI (henceforth, TAI) system, which seems plausible, I think we could get TAI by 2040 (>50% confidence), even under fairly conservative assumptions. (And a prolonged absence of TAI wouldn't necessarily bring about an AI winter; investors probably aren't betting on TAI, but on more mundane products.) Reliability is definitely a problem for AI systems, but not as large a problem as it seems, because we pay far more attention to frontier capabilities of AI systems (which tend to be unreliable) than long-familiar capabilities (which are pretty reliable). If you fix your gaze on a specific task, you usually see a substantial and rapid improvement in reliability over the years. I reckon inference with GPT-3.5-like models will be about as cheap as search queries are today in about 3-6 years. I think ChatGPT and many other generative models will be profitable within 1-2 years if they aren't already. There's substantial demand for them (ChatGPT reached 100M monthly active users after two months, quite impressive next to Twitter's ~450M) and people are only beginning to explore their uses. If an AI winter does happen, I'd guess some of the more likely reasons would be (1) scaling hitting a wall, (2) deep-learning-based models being chronically unable to generalise out-of-distribution and/or (3) AI companies running out of good-enough data. I don't think this is very likely, but I would be relieved if it were the case, given that we as a species currently seem completely unprepared for TAI. The Prospect of a New AI Winter What does a speculative bubble look like from the inside? Trick question -- you don't see it. Or, I suppose some people do see it. One or two may even be right, and some of the others are still worth listening to. William Eden tweeting out a long thread explaining why he's not worried about risks from advanced AI is one example, I don't know of which. He argues in support of his thesis that another AI winter is looming, making the following points: AI systems aren't that good. In particular (argues Eden), they are too unreliable and too inscrutable. It's far harder to achieve three or four nines reliability than merely one or two nines; as an example, autonomous vehicles ...

ai law chatgpt speech trick ea prospect flop 100m gpt reliability tai erich 450m grunewald rationalist ai winter lesswrong

#232 GPT-4

Doppelgänger Tech Talk

Play Episode Listen Later Mar 17, 2023 100:00

Glöckler erzählt, wie Podcast Werbung seinen Geburtstag vermasselt. Wir sprechen über ChatGPT-4's Möglichkeiten und Grenzen (diese Podcast Beschreibung konnte noch nicht von AI geschrieben werden). Glöckler argumentiert, warum wir bald einen AI Winter erleben könnten. Jan wird mit einer Taschenlampen-App reich. Mark Zuckerberg wird im All-Hands gegrillt. Pip gibt eine Einschätzung zum Jobmarkt & Recruiting Unternehmen. Philipp Glöckler (https://www.linkedin.com/in/philippgloeckler/) und Philipp Klöckner (https://twitter.com/pip_net) sprechen heute über: (00:09:45) Banken Sektor update (00:23:45) AI & Chat GPT4 (00:57:45) Jobmarkt & Recruiting Unternehmen (01:06:30) Adobe Earnings (01:11:15) UiPath Earnings (01:17:30) Meta (01:23:30) TikTok Shownotes: Werbung: Jetzt auf https://doppelgaenger.io/kuhn einen Termin bei KUHN Maßkonfektion machen und 20% mit dem Gutscheincode “Doppelgänger” sparen, wenn du dich bis Ende April 2023 vor Ort beraten und vermessen lässt. Jason & SVB: https://twitter.com/BradoCapital/status/1635644287630159872 AI CEO: https://twitter.com/ruima/status/1636042033956786177 Pip's Tweet zu Bing's AI: https://twitter.com/pip_net/status/1635999200268697600?s=20 Kassenzone Party: https://www.kassenzone.de/party/ Doppelgänger Tech Talk Podcast Sheet https://doppelgaenger.io/sheet/ Disclaimer https://www.doppelgaenger.io/disclaimer/ Passionfroot Storefront www.passionfroot.xyz/doppelgaenger Post Production by Jan Wagener https://www.linkedin.com/in/jan-wagener-49270018b/ Aktuelle Doppelgänger Werbepartner https://lollipod.de/sn/doppelgaenger-werbung

AI Winter?

Digital Business Models

Play Episode Listen Later Feb 27, 2023 20:22

AI Winter?

ai winter

AI Hype Cycles And Winters On The Way To ChatGPT

The History of Computing

Play Episode Listen Later Feb 22, 2023 23:37

Carlota Perez is a researcher who has studied hype cycles for much of her career. She's affiliated with the University College London, the University of Sussex, The Tallinn University of Technology in Astonia and has worked with some influential organizations around technology and innovation. As a neo-Schumpeterian, she sees technology as a cornerstone of innovation. Her book Technological Revolutions and Financial Capital is a must-read for anyone who works in an industry that includes any of those four words, including revolutionaries. Connecticut-based Gartner Research was founded by GideonGartner in 1979. He emigrated to the United States from Tel Aviv at three years old in 1938 and graduated in the 1956 class from MIT, where he got his Master's at the Sloan School of Management. He went on to work at the software company System Development Corporation (SDC), the US military defense industry, and IBM over the next 13 years before starting his first company. After that failed, he moved into analysis work and quickly became known as a top mind in the technology industry analysts. He often bucked the trends to pick winners and made banks, funds, and investors lots of money. He was able to parlay that into founding the Gartner Group in 1979. Gartner hired senior people in different industry segments to aid in competitive intelligence, industry research, and of course, to help Wall Street. They wrote reports on industries, dove deeply into new technologies, and got to understand what we now call hype cycles in the ensuing decades. They now boast a few billion dollars in revenue per year and serve well over 10,000 customers in more than 100 countries. Gartner has developed a number of tools to make it easier to take in the types of analysis they create. One is a Magic Quadrant, reports that identify leaders in categories of companies by a vision (or a completeness of vision to be more specific) and the ability to execute, which includes things like go-to-market activities, support, etc. They lump companies into a standard four-box as Leaders, Challengers, Visionaries, and Niche Players. There's certainly an observer effect and those they put in the top right of their four box often enjoy added growth as companies want to be with the most visionary and best when picking a tool. Another of Gartner's graphical design patterns to display technology advances is what they call the “hype cycle”. The hype cycle simplifies research from career academics like Perez into five phases. * The first is the technology trigger, which is when a breakthrough is found and PoCs, or proof-of-concepts begin to emerge in the world that get press interested in the new technology. Sometimes the new technology isn't even usable, but shows promise. * The second is the Peak of Inflated Expectations, when the press picks up the story and companies are born, capital invested, and a large number of projects around the new techology fail. * The third is the Trough of Disillusionment, where interest falls off after those failures. Some companies suceeded and can show real productivity, and they continue to get investment. * The fourth is the Slope of Enlightenment, where the go-to-market activities of the surviving companies (or even a new generation) begin to have real productivity gains. Every company or IT department now runs a pilot and expectations are lower, but now achievable. * The fifth is the Plateau of Productivity, when those pilots become deployments and purchase orders. The mainstream industries embrace the new technology and case studies prove the promised productivity increases. Provided there's enough market, companies now find success. There are issues with the hype cycle. Not all technologies will follow the cycle. The Gartner approach focuses on financials and productivity rather than true adoption. It involves a lot of guesswork around subjective, synthetical, and often unsystematic research. There's also the ever-resent observer effect. However, more often than not, the hype is seperated from the tech that can give organizations (and sometimes all of humanity) real productivity gains. Further, the term cycle denotes a series of events when it should in fact be cyclical as out of the end of the fifth phase a new cycle is born, or even a set of cycles if industries grow enough to diverge. ChatGPT is all over the news feeds these days, igniting yet another cycle in the cycles of AI hype that have been prevalent since the 1950s. The concept of computer intelligence dates back to the 1942 with Alan Turing and Isaac Asimov with “Runaround” where the three laws of robotics initially emerged from. By 1952 computers could play themselves in checkers and by 1955, Arthur Samuel had written a heuristic learning algorthm he called “temporal-difference learning” to play Chess. Academics around the world worked on similar projects and by 1956 John McCarthy introduced the term “artificial intelligence” when he gathered some of the top minds in the field together for the McCarthy workshop. They tinkered and a generation of researchers began to join them. By 1964, Joseph Weizenbaum's "ELIZA" debuted. ELIZA was a computer program that used early forms of natural language processing to run what they called a “DOCTOR” script that acted as a psychotherapist. ELIZA was one of a few technologies that triggered the media to pick up AI in the second stage of the hype cycle. Others came into the industry and expectations soared, now predictably followed by dilsillusionment. Weizenbaum wrote a book called Computer Power and Human Reason: From Judgment to Calculation in 1976, in response to the critiques and some of the early successes were able to then go to wider markets as the fourth phase of the hype cycle began. ELIZA was seen by people who worked on similar software, including some games, for Apple, Atari, and Commodore. Still, in the aftermath of ELIZA, the machine translation movement in AI had failed in the eyes of those who funded the attempts because going further required more than some fancy case statements. Another similar movement called connectionism, or mostly node-based artificial neural networks is widely seen as the impetus to deep learning. David Hunter Hubel and Torsten Nils Wiesel focused on the idea of convultional neural networks in human vision, which culminated in a 1968 paper called "Receptive fields and functional architecture of monkey striate cortex.” That built on the original deep learning paper from Frank Rosenblatt of Cornell University called "Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms" in 1962 and work done behind the iron curtain by Alexey Ivakhnenko on learning algorithms in 1967. After early successes, though, connectionism - which when paired with machine learning would be called deep learning when Rina Dechter coined the term in 1986, went through a similar trough of disillusionment that kicked off in 1970. Funding for these projects shot up after the early successes and petered out ofter there wasn't much to show for them. Some had so much promise that former presidents can be seen in old photographs going through the models with the statiticians who were moving into computing. But organizations like DARPA would pull back funding, as seen with their speech recognition projects with Cargegie Mellon University in the early 1970s. These hype cycles weren't just seen in the United States. The British applied mathemetician James Lighthill wrote a report for the British Science Research Council, which was published in 1973. The paper was called “Artificial Intelligence: A General Survey” and analyzed the progress made based on the amount of money spent on artificial intelligence programs. He found none of the research had resulted in any “major impact” in fields that the academics had undertaken. Much of the work had been done at the University of Edinbourgh and funding was drastically cut, based on his findings, for AI research around the UK. Turing, Von Neumann, McCarthy, and others had either intentially or not, set an expectation that became a check the academic research community just couldn't cash. For example, the New York Times claimed Rosenblatt's perceptron would let the US Navy build computers that could “walk, talk, see, write, reproduce itself, and be conscious of its existence” in the 1950s - a goal not likely to be achieved in the near future even seventy years later. Funding was cut in the US, the UK, and even in the USSR, or Union of the Soviet Socialist Republic. Yet many persisted. Languages like Lisp had become common in the late 1970s, after engineers like Richard Greenblatt helped to make McCarthy's ideas for computer languages a reality. The MIT AI Lab developed a Lisp Machine Project and as AI work was picked up at other schools like Stanford began to look for ways to buy commercially built computers ideal to be Lisp Machines. After the post-war spending, the idea that AI could become a more commercial endeavor was attractive to many. But after plenty of hype, the Lisp machine market never materialized. The next hype cycle had begun in 1983 when the US Department of Defense pumped a billion dollars into AI, but that spending was cancelled in 1987, just after the collapse of the Lisp machine market. Another AI winter was about to begin. Another trend that began in the 1950s but picked up steam in the 1980s was expert systems. These attempt to emulate the ways that humans make decisions. Some of this work came out of the Stanford Heuristic Programming Project, pioneered by Edward Feigenbaum. Some commercial companies took the mantle and after running into barriers with CPUs, by the 1980s those got fast enough. There were inflated expectations after great papers like Richard Karp's “Reducibility among Combinatorial Problems” out of UC Berkeley in 1972. Countries like Japan dumped hundreds of millions of dollars (or yen) into projects like “Fifth Generation Computer Systems” in 1982, a 10 year project to build up massively parallel computing systems. IBM spent around the same amount on their own projects. However, while these types of projects helped to improve computing, they didn't live up to the expectations and by the early 1990s funding was cut following commercial failures. By the mid-2000s, some of the researchers in AI began to use new terms, after generations of artificial intelligence projects led to subsequent AI winters. Yet research continued on, with varying degrees of funding. Organizations like DARPA began to use challenges rather than funding large projects in some cases. Over time, successes were found yet again. Google Translate, Google Image Search, IBM's Watson, AWS options for AI/ML, home voice assistants, and various machine learning projects in the open source world led to the start of yet another AI spring in the early 2010s. New chips have built-in machine learning cores and programming languages have frameworks and new technologies like Jupyter notebooks to help organize and train data sets. By 2006, academic works and open source projects had hit a turning point, this time quietly. The Association of Computer Linguistics was founded in 1962, initially as the Association for Machine Translation and Computational Linguistics (AMTCL). As with the ACM, they have a number of special interest groups that include natural language learning, machine translation, typology, natural language generation, and the list goes on. The 2006 proceedings on the Workshop of Statistical Machine Translation began a series of dozens of workshops attended by hundreds of papers and presenters. The academic work was then able to be consumed by all, inlcuding contributions to achieve English-to-German and Frnech tasks from 2014. Deep learning models spread and become more accessible - democratic if you will. RNNs, CNNs, DNNs, GANs. Training data sets was still one of the most human intensive and slow aspects of machine learning. GANs, or Generative Adversarial Networks were one of those machine learning frameworks, initially designed by Ian Goodfellow and others in 2014. GANs use zero-sum game techniques from game theory to generate new data sets - a genrative model. This allowed for more unsupervised training of data. Now it was possible to get further, faster with AI. This brings us into the current hype cycle. ChatGPT was launched in November of 2022 by OpenAI. OpenAI was founded as a non-profit in 2015 by Sam Altman (former cofounder of location-based social network app Loopt and former president of Y Combinator) and a cast of veritable all-stars in the startup world that included: * Reid Hoffman, former Paypal COO, LinkedIn founder and venture capitalist. * Peter Thiel, former cofounder of Paypal and Palantir, as well as one of the top investors in Silicon Valley. * Jessica Livingston, founding partner at Y Combinator. * Greg Brockman, an AI researcher who had worked on projects at MIT and Harvard OpenAI spent the next few years as a non-profit and worked on GPT, or Generative Pre-trained Transformer autoregression models. GPT uses deep learning models to process human text and produce text that's more human than previous models. Not only is it capable of natural language processing but the generative pre-training of models has allowed it to take a lot of unlabeled text so people don't have to hand label weights, thus automated fine tuning of results. OpenAI dumped millions into public betas by 2016 and were ready to build products to take to market by 2019. That's when they switched from a non-profit to a for-profit. Microsoft pumped $1 billion into the company and they released DALL-E to produce generative images, which helped lead to a new generation of applications that could produce artwork on the fly. Then they released ChatGPT towards the end of 2022, which led to more media coverage and prognostication of world-changing technological breakthrough than most other hype cycles for any industry in recent memory. This, with GPT-4 to be released later in 2023. ChatGPT is most interesting through the lens of the hype cycle. There have been plenty of peaks and plateaus and valleys in artificial intelligence over the last 7+ decades. Most have been hyped up in the hallowed halls of academia and defense research. ChatGPT has hit mainstream media. The AI winter following each seems to be based on the reach of audience and depth of expectations. Science fiction continues to conflate expectations. Early prototypes that make it seem as though science fiction will be in our hands in a matter of weeks lead media to conjecture. The reckoning could be substantial. Meanwhile, projects like TinyML - with smaller potential impacts for each use but wider use cases, could become the real benefit to humanity beyond research, when it comes to everyday productivity gains. The moral of this story is as old as time. Control expectations. Undersell and overdeliver. That doesn't lead to massive valuations pumped up by hype cycles. Many CEOs and CFOs know that a jump in profits doesn't always mean the increase will continue. Some intentially slow expectations in their quarterly reports and calls with analysts. Those are the smart ones.

united states university english ai uk master apple science technology japan training british new york times deep doctors management german microsoft mit leaders defense cnn chatgpt artificial intelligence theory union silicon valley wall street ceos principles productivity hype connecticut stanford paypal workshop funding ibm peak organizations enlightenment perez countries mccarthy cycles openai cornell university uc berkeley chess tel aviv languages us navy us department gpt aws atari sussex ussr winters transformer academics plateau university college london challengers gartner peter thiel y combinator sam altman commodore darpa cfos visionaries isaac asimov turing palantir alan turing ai ml google translate acm reid hoffman disillusionment gans slope calculation cpus trough receptive pocs rosenblatt run around john mccarthy loopt lisp sloan school jupyter greg brockman von neumann technological revolution magic quadrant machine translation financial capital generative pre generative adversarial networks google image search soviet socialist republics ai winter gartner research carlota perez ian goodfellow rnns joseph weizenbaum jessica livingston gartner group schumpeterian computer power

AI Today Podcast: AI Glossary Series: AI Winters

AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

Play Episode Listen Later Dec 9, 2022 11:30

Like all technologies, Artificial Intelligence (AI) is not immune to the waves of obscurity, hyped promotion, plateauing of interest, and decline. In fact, the AI industry has been through two such major waves of interest, hype, plateau, and decline, commonly referred to as the “AI Winters”. In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer define an “AI Winter” at a high level. Continue reading AI Today Podcast: AI Glossary Series: AI Winters at Cognilytica.

ai artificial intelligence winters glossary ai today ai winter kathleen walch cognilytica ron schmelzer

Anton Teaches Packy AI | E3 | The First AI Winter

Not Boring

Play Episode Listen Later Dec 5, 2022 79:43

Packy and Anton breakdown one of the early, foundational artifical intelligence papers, "A logical calculus of the ideas immanent in nervous activity," which was first published in 1943. The researchers, Warren S. McCulloch and Walter Pitts, were trying to understand how the brain could produce such complex patterns by using basic, connected cells. Their work was foundational in understanding neurons, and l introduced the concept of the neural network which has since become a key concept in artificial intelligence. This was the oldest paper that Anton and Packy have discussed, and its naturally age led to a lengthy conversation on the history of artificial intelligence. That history -- like the history of many technological fields -- is spotted with long winters, golden ages, broken timeline promises, and sudden developments. Today, it seems, we may be in the middle of a golden age for artificial intelligence. LINKS: Youtube Link: https://youtu.be/MpBdVJEx2Aw A logical calculus of the ideas immanent in nervous activity: https://www.cs.cmu.edu/~./epxing/Class/10715/reading/McCulloch.and.Pitts.pdf History of the First AI Winter: https://towardsdatascience.com/history-of-the-first-ai-winter-6f8c2186f80b AI Winter: https://en.wikipedia.org/wiki/AI_winter#The_abandonment_of_connectionism_in_1969 Bitter Lesson: http://www.incompleteideas.net/IncIdeas/BitterLesson.html Chroma: https://www.trychroma.com/ --- Send in a voice message: https://anchor.fm/notboring/message

ai class teaches anton pitts mcculloch packy ai winter

The Race for Autonomy: David Broyles on Getting Autonomy to Work for Defense

CNA Talks

Play Episode Listen Later Aug 18, 2022 47:17

This content was originally published on BMNT's YouTube Channel. You can find the original video here. In this follow-up conversation to BMNT's June panel "The Race for Autonomy: Navigating a New Battlefield," A'ndre Gonawela talks to Dr. David Broyles, Research Program Director at the Center for Naval Analysis and co-host of "AI with AI", on the challenges facing the Department of Defense when it comes to developing and leveraging autonomous systems and capabilities. Dr. Broyles digs into why he (like our prior panelists) believes the state of autonomy today is ‘brittle', and why the end goal for many is ‘general AI' – the ability for artificial intelligence to behave and adapt like human intelligence can. We discuss Dr. Broyles' belief that an ‘AI Winter' may be approaching, where momentum in the development of systems is slowed or even halted. We then dig into where the Department of Defense is on the racetrack, dissecting the lingering confusion that underlies the differences between unmanned systems and autonomous systems, and how we can better equip DoD leaders in understanding how autonomous systems can operate. Dr. Broyles highlights opportunities to build trust in autonomous systems with the warfighter, in addition to addressing the edge cases and ‘fat tails' that can impede the success of autonomous vehicles. You can read about our first panel here: https://www.bmnt.com/post/the-race-for-autonomy-is-here Notes from Episode General consensus of state of autonomy is that it is brittle, and still in infancy when it comes to DoD Bigger debate in AI community – end state is general AI, equivalent to human intelligence, adaptable to environment, and process things like a human can. What are the tools to go about this? Two camps that disagree with each other: Neural network reward: Can employ larger neural networks, dump more data, put more processing power, and have reward schemes. Symbolic logic camps – need ways to encode information in symbols that machines can manipulate at higher levels of aggregation. Still trying to figure out the things we really need to make these things work and get rid of the bugs. AI Winter? There have been periods where the momentum in AI development stopped – last one in early 2000s, influenced by availability of graphical processing capabilities (large computational power being dumped on the problem) Are we coming to the limits of the tools and capabilities we've developed? Margins of incremental improvements are diminishing. AVs are a bellwether of progress – if progress isn't delivered in tangible ways, market could lose interest, meaning less financial investment. AI Summer? Alexnet winning image recognition competition in 2014 was first real success of neural networks, motivated community at large, many developments between 2014 through 2019. People were trying many different tools. Where's DOD with developing/leveraging autonomous systems? It's hard to pinpoint where they are on the racetrack. Confusion between unmanned and autonomous systems – can be communicated unclearly, sometimes unmanned systems are mistakenly attributed as autonomous when they aren't. First major step is for DoD to employ more unmanned systems – it's been slow, but CNO actually incorporating uncrewed systems into their force structure direction is a significant step. Lots of little things here and there are going on but there's nothing being coordinate in a big way. CDAO (Chief Digital AI Office, former JAIC), is trying to play a role here but there's more ways in which they can step in. Ensuring trust for warfighters? You can either not have enough trust, or you can overtrust, and the latter gets less attention – the example here is Tesla's autopilot system being overtrusted and then getting involved in deadly crashes. Need to get autonomous systems into the hands of the warfighters – biggest priority. Need to communicate the capabilities better to an operator, need to ensure that the operator can have other cues and/or ways of interacting with the system. Do our DoD leaders understand how autonomous systems can be used/leveraged and how they work? Can we work to educate them quickly? Area of high concern, and cyber discussions are indicative of the difficulties that could be faced as senior leaders have taken some time to embrace and understand the technologies. Very small number of senior leaders who have a good idea of what's going on, and larger number with staff who know what they're talking about, but there's issues with proposals promising to develop tech that simply won't happen. People in approval chain may not understand that these things won't work Arming senior leaders with the key questions, but that's a bandaid – we need more people with basic understandings of how these technologies work. This does not necessarily mean we hire computer scientists, but DoD can work internally to raise the floor on level of understanding – and these areas are beginning to slowly come up to speed. Addressing edge cases? Fat tails – distribution of things that you may run into, most of the stuff is going to fall into a general bin, but there'll be edge cases that'll extend. What happens if a plastic bag runs into a screen of an AV? Uber and others couldn't just throw hundreds or millions of hours of driving data to fix this. Solution is General AI – we can't throw fat tail problems into same bucket. Running simulations still runs into the same problem, and throwing info won't solve it. There really is no good answer, there's not been a good articulation of the answer. We're trying to minimize the edge cases as best we can. However, alternatives like smart roads and sensors can provide added information to help prevent accidents or minimize disruptions in environment. Experimentation – What's Commercial doing that DoD is not doing? Mechanics around how to do things are the primary thing that can hinder experimentation. There's a strange acquisition ecosystem that isn't always friendly to innovative ideas going through standard program office processes. Policy Lagging Behind on Autonomous Systems? There are some new technologies falling under clear regulation – and as long as it doesn't cause any other problem, but because these technologies are so wide ranging they can cause issues. You can forecast some of these things, but there's always an unexpected bit. Is there a general philosophy on how to handle this? There'll always be questions on privacy and safety. Is DoD adequately reaching out to small businesses? It is happening, but biggest barrier (in his view) is DoD contracting and being able to decipher postings, requirements, forms, and etc. Need to take a quantitative approach to assessing effectiveness of this.

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Using AI To Transform Your Business Without The Headache Using Graft

The Machine Learning Podcast

Play Episode Listen Later Aug 16, 2022 67:33

Summary Machine learning is a transformative tool for the organizations that can take advantage of it. While the frameworks and platforms for building machine learning applications are becoming more powerful and broadly available, there is still a significant investment of time, money, and talent required to take full advantage of it. In order to reduce that barrier further Adam Oliner and Brian Calvert, along with their other co-founders, started Graft. In this episode Adam and Brian explain how they have built a platform designed to empower everyone in the business to take part in designing and building ML projects, while managing the end-to-end workflow required to go from data to production. Announcements Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery. Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out! Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started! Your host is Tobias Macey and today I’m interviewing Brian Calvert and Adam Oliner about Graft, a cloud-native platform designed to simplify the work of applying AI to business problems Interview Introduction How did you get involved in machine learning? Can you describe what Graft is and the story behind it? What is the core thesis of the problem you are targeting? How does the Graft product address that problem? Who are the personas that you are focused on working with both now in your early stages and in the future as you evolve the product? What are the capabilities that can be unlocked in different organizations by reducing the friction and up-front investment required to adopt ML/AI? What are the user-facing interfaces that you are focused on providing to make that adoption curve as shallow as possible? What are some of the unavoidable bits of complexity that need to be surfaced to the end user? Can you describe the infrastructure and platform design that you are relying on for the Graft product? What are some of the emerging "best practices" around ML/AI that you have been able to build on top of? As new techniques and practices are discovered/introduced how are you thinking about the adoption process and how/when to integrate them into the Graft product? What are some of the new engineering challenges that you have had to tackle as a result of your specific product? Machine learning can be a very data and compute intensive endeavor. How are you thinking about scalability in a multi-tenant system? Different model and data types can be widely divergent in terms of the cost (monetary, time, compute, etc.) required. How are you thinking about amortizing vs. passing through those costs to the end user? Can you describe the adoption/integration process for someone using Graft? Once they are onboarded and they have connected to their various data sources, what is the workflow for someone to apply ML capabilities to their problems? One of the challenges about the current state of ML capabilities and adoption is understanding what is possible and what is impractical. How have you designed Graft to help identify and expose opportunities for applying ML within the organization? What are some of the challenges of customer education and overall messaging that you are working through? What are the most interesting, innovative, or unexpected ways that you have seen Graft used? What are the most interesting, unexpected, or challenging lessons that you have learned while working on Graft? When is Graft the wrong choice? What do you have planned for the future of Graft? Contact Info Brian LinkedIn Adam LinkedIn Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@themachinelearningpodcast.com) with your story. To help other people find the show please leave a review on iTunes and tell your friends and co-workers Links Graft High Energy Particle Physics LHC Cruise Slack Splunk Marvin Minsky Patrick Henry Winston AI Winter Sebastian Thrun DARPA Grand Challenge Higss Boson Supersymmetry Kinematics Transfer Learning Foundation Models ML Embeddings BERT Airflow Dagster Prefect Dask Kubeflow MySQL PostgreSQL Snowflake Redshift S3 Kubernetes Multi-modal models Multi-task models Magic: The Gathering The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/[CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/?utm_source=rss&utm_medium=rss

ai interview building foundation built cruise slack hitman headaches accelerate python ml using ai ludwig snowflakes magic the gathering s3 sql kubernetes cc by sa particle transform your business das k splunk graft mysql lhc postgresql redshift prefect airflow ml ai kinematics foundation models marvin minsky sebastian thrun supersymmetry ai winter dagster darpa grand challenge pql freak fandango orchestra predibase brian calvert horovod

Steven Shwartz: How AI Will Impact Society Over the Next Ten Years

HumAIn

Play Episode Listen Later Jun 12, 2022 34:14

[Audio] Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSSSteve received his PhD from Johns Hopkins University in Cognitive Science where he began his AI research and also taught Statistics at Towson State University. After receiving his PhD in 1979, AI pioneer Roger Schank invited Steve to join the Yale University faculty as a postdoctoral researcher in Computer Science. In 1981, Roger asked Steve to help him start one of the first AI companies, Cognitive Systems, which progressed to a public offering in 1986. Steve then started Esperant, which produced one of the leading Business Intelligence products of the 1990s. During the 1980s, Steve published 35 articles and a book on AI, spoke at many AI conferences, and received two commercial patents on AI. As the AI Winter of the 1990s set in, Steve transitioned into a career as a successful serial software entrepreneur and investor and created several companies that were either acquired or had a public offering. He tries to use his unique perspective as an early AI researcher and statistician to both explain how AI works in simple terms, to explain why people should not worry about intelligent robots taking over the world, and to explain the steps we need to take as a society to minimize the negative impacts of AI and maximize the positive impacts. Please support this podcast by checking out our sponsors:Episode Links: Steven Shwartz LinkedIn: https://www.linkedin.com/in/steveshwartz/ Steven Shwartz Twitter: https://twitter.com/sshwartz Steven Shwartz Website: https://www.device42.com Podcast Details: Podcast website: https://www.humainpodcast.com Apple Podcasts: https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify: https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media: – Check out the sponsors above, it's the best way to support this podcast– Support on Patreon: https://www.patreon.com/humain/creators – Twitter: https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline: Here's the timestamps for the episode: (00:00) – Introduction(09:42) – So most of the things that are taking jobs for example, is conventional software, not AI software.(10:57)- Exactly. And that's automated but it's conventional software. It's not AI. And most of the examples of where computers are replacing people, it's conventional software. It's not AI software.(14:49)- How you get data quality into your AI models and it's what they do that's really interesting. And I hadn't actually focused on it until I talked to this company. There's a big industry to clean data for tools like business intelligence that have been around for a long time. And there are, there are companies that are multi-billion dollar companies that provide data, cleaning tools, data extraction, and so forth.(17:13)- Everybody thought that with AI, you could diagnose illnesses from medical images better than the radiologists. And it's never actually worked out that way. I have friends who are radiologists, who use those AI tools and they say yes, sometimes they find things that I might've missed. But at the same time, they miss things that we would have found.(22:17)- I think we're seeing a lot of the rollout of a specific type of AI supervised learning, which is a type of machine learning. We're seeing it applied in many different areas. I actually have a database I keep before every time I see a new application of supervised learning and it's fascinating. It's being used in almost every area of business, of government, of the nonprofit world. It is fascinating how much application there is. (27:06)- And they're not really going to make sense if you drill down into them. So what's going to be the implication of that. Is it only going to be useful if there's all kinds of search engine optimization where you don't really care If what you're right makes sense. We're going to generate a lot of crap using GPT three and put it out there for search engine optimization purposes.(31:19)- And I think there's a lot of opportunity for companies that are helping develop software and services to help companies build non-biased explainable systems. And then you have a whole issue around when you build a machine learning system, it deteriorates over time. So it might only work for a couple of days and then start to go downhill. It might work for weeks, but you have to monitor those systems and go back and retrain them when the performance goes down. And all of that is a lot of effort. Advertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

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AI Today Podcast: Is the next AI Winter approaching?

AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

Play Episode Listen Later May 19, 2022 42:13

Like all technologies, Artificial Intelligence (AI) is not immune to the waves of obscurity, hyped promotion, plateauing of interest, and decline. In fact, the AI industry has been through two such major waves of interest, hype, plateau, and decline, commonly referred to as the “AI Winters”. Is the next AI Winter approaching? In this episode of the AI Today podcast hosts Kathleen Walch and Ron Schmelzer discuss the 3 main reasons for a winter: decline in investment, interest, and research. Continue reading AI Today Podcast: Is the next AI Winter approaching? at Cognilytica.

ai artificial intelligence ai today ai winter kathleen walch cognilytica ron schmelzer

Bonus: Season 1 Recap

Counting Sand

Play Episode Listen Later Jan 25, 2022 21:29

Angelo begins this episode with reflections on history and what brought us to the AI Winter. Why do we need a balance between research and practice? You don't want to rediscover what has already been discovered or settle for something that could be better if you took the time to research a bit more. In episode 4 we meet Angelo's friend Andy Lee who talks about computer science predicting our biological age. Andy actually met Greg Fahy who talked about longevity. The study focused on injecting the thymus gland with a growth hormone that produced regeneration effects. The effects were measured through the epigenetic clock known as DNA methylation.In Episode 6, Jim Shalaby talks with Angelo about how COVID-19 changed healthcare forever. Patients don't have to wait in waiting rooms, they don't have to find transportation to get there, and the patient has access to the clinicians. The hard problems associated with explainability in artificial neural networks, we talked about in Episode 8. Angelo's friend Nikos explained to us about five classic problems, one of which includes data privacy. Another big issue is developing a machine learning system to create adversarial attacks on the existing system.In episode 7, Angelo's friend Manos shared how complicated it is for people to invoke their right to have their data removed from a system. Typically those systems have to schedule deletions to remove the data through tombstones and a process called compacting.What is on the horizon and what should we be paying attention to? We are going to run against barriers of technology. For instance, Moore's law is coming to an end. What do we do about that? What is happening in the short-term and how do we get past this barrier to the next? And then how do we blow away all those barriers with moonshots like quantum computing?Finally, wrapping up our first season, Angelo wants to reflect on gratitude. Gratitude for you our listeners. Thank you so much for joining us on this journey. We really want to hear about your thoughts. The show is evolving just as the world is and we want to make sure that we're covering topics that you're interested in.We would love for you to follow, rate, and review the show on your favorite podcast platform so that others can find us too. Thank you so much for listening. Our Guests - Thank you!:Nikos Myrtakis on LinkedInManos Athanassoulis on LinkedIn and Boston UniversityJim Shalaby on Twitter and LinkedInAndy Lee on Twitter and LinkedIn About the HostAngelo Kastroulis is an award-winning technologist, inventor, entrepreneur, speaker, data scientist, and author best known for his high-performance computing and Health IT experience. He is the principal consultant, lead architect, and owner of Carrera Group, a consulting firm specializing in software modernization, event streaming (Kafka), big data, analytics (Spark, elastic Search, and Graph), and high-performance software development on many technical stacks (Java, .net, Scala, C++, and Rust). A Data Scientist at heart, trained at the Harvard Data Systems Lab, Angelo enjoys a research-driven approach to creating powerful, massively scalable applications and innovating new methods for superior performance. He loves to educate, discover, then see the knowledge through to practical implementation.Host: Angelo KastroulisExecutive Producer: Kerri Patterson; Producer: Albert Perrotta;Communications Strategist: Albert Perrotta;Audio Engineer: Ryan ThompsonMusic: All Things Grow by Oliver Worth

Five Types of Thinking for High Performing Data Scientists

Data Transformers Podcast

Play Episode Listen Later Aug 19, 2021 30:10

Artificial Intelligence is all the rage currently. But there was a time when AI has gone through ‘AI Winter' when there was not much interest in AI. Dr. Anand Rao has gone through those AI Winters. To avoid AI winter, we need to be cognizant of AI risks. Should be balance between AI innovation and risks. Should not reduce customer risk. In thos episode, Anand talks about five types of thinking that data scientists should focus on to be high performing data scientists. As the Global head of AI at PWC, Anand knows a lot about the customer uptake of AI and (un)surprisingly only 20% of the companies are actually deploying AI. Listen to the episode to find out which functions are adopting AI the most.

ai thinking global artificial intelligence pwc data scientists anand high performing data quality five types ai winter

Five Types of Thinking for High Performing Data Scientists

Data Transformers Podcast

Play Episode Listen Later Aug 19, 2021 30:10

ai thinking global artificial intelligence pwc data scientists anand high performing five types ai winter

#2121 $95B for research / AI Winter / Tesla / Robotaxi / IBMs dataset

AI News

Play Episode Listen Later May 24, 2021 5:10

The US Senate Committee Chairs have worked out a $ 110 billion compromise over five years for basic and advanced technology research and science, and the creation of a White House production manager in the face of increasing competitive pressures from China. https://venturebeat.com/2021/05/08/u-s-senate-committee-revised-a-draft-bill-to-fund-ai-quantum-biotech/ The Department of Defense is in full swing on AI's potential to secure America's competitive advantage over potential adversaries. https://warontherocks.com/2021/05/the-department-of-defenses-looming-ai-winter/ Tesla Inc (TSLA.O) announced to a California regulatory agency that full self-driving technology may not be achieved by the end of this year, a memo from the California Department of Motor Vehicles (DMV) shows. https://www.reuters.com/business/autos-transportation/tesla-tells-regulator-that-full-self-driving-cars-may-not-be-achieved-by-year-2021-05-07/ The next generation Pony.ais Robotaxi is characterized by the fact that it appears to be missing the cone-shaped LIDAR sensor on the roof, which is typical of most autonomous vehicles. https://www.theverge.com/2021/5/10/22424726/pony-ai-luminar-lidar-robotaxi-california-china AI and machine learning systems have become more and more competent in recent years and can not only understand the written word, but also write it. https://finance.yahoo.com/news/ibm-codenet-dataset-can-teach-ai-to-translate-computer-languages-020052618.html Visit www.integratedaisolutions.com

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#2121 $95B for research / AI Winter / Tesla / Robotaxi / IBMs dataset

AI News po polsku

Play Episode Listen Later May 24, 2021 5:22

Podcast jest dostępny także w formie newslettera: https://ainewsletter.integratedaisolutions.com/ Zgodnie z kopią raportu 131- Strona projekt ustawy widziany w piątek przez Reutera. https://venturebeat.com/2021/05/08/u-s-senate-committee-revised-a-draft-bill-to-fund-ai-quantum-biotech/ Departament Obrony jest na wysokim poziomie, jeśli chodzi o potencjał sztucznej inteligencji do zabezpieczenia przewagi konkurencyjnej Ameryki nad potencjalnymi przeciwnikami. https://warontherocks.com/2021/05/the-department-of-defenses-looming-ai-winter/ Tesla Inc (TSLA. https://www.reuters.com/business/autos-transportation/tesla-tells-regulator-that-full-self-driving-cars-may-not-be-achieved-by-year-2021-05-07/ Robotaxi nowej generacji Pony.ai wyróżnia się, ponieważ wydaje się, że brakuje w nim stożkowego czujnika LIDAR umieszczonego na dachu, który jest typowy dla większości pojazdów autonomicznych. https://www.theverge.com/2021/5/10/22424726/pony-ai-luminar-lidar-robotaxi-california-china Sztuczna inteligencja i systemy uczenia maszynowego stały się w ostatnich latach coraz bardziej kompetentne, zdolne nie tylko rozumieć słowo pisane, ale także je pisać. https://finance.yahoo.com/news/ibm-codenet-dataset-can-teach-ai-to-translate-computer-languages-020052618.html Odwiedź www.integratedaisolutions.com

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#2121 $95B for research / AI Winter / Tesla / Robotaxi / IBMs dataset

AI News auf Deutsch

Play Episode Listen Later May 24, 2021 5:45

Die Vorsitzenden des US-Senatsausschusses haben über einen Zeitraum von fünf Jahren einen Kompromiss in Höhe von 110 Milliarden US-Dollar für Grundlagenforschung und fortschrittliche Technologieforschung und -wissenschaft sowie die Schaffung eines Produktionsleiters des Weißen Hauses angesichts des zunehmenden Wettbewerbsdrucks aus China ausgearbeitet. https://venturebeat.com/2021/05/08/u-s-senate-committee-revised-a-draft-bill-to-fund-ai-quantum-biotech/ Das Verteidigungsministerium ist in vollem Gange, was das Potenzial der KI angeht, Amerikas Wettbewerbsvorteil gegenüber potenziellen Gegnern zu sichern. https://warontherocks.com/2021/05/the-department-of-defenses-looming-ai-winter/ Tesla Inc (TSLA.O) teilte einer kalifornischen Aufsichtsbehörde mit, dass bis Ende dieses Jahres möglicherweise keine vollständige Selbstfahrer-Technologie erreicht werden kann, wie ein Memo des kalifornischen Kraftfahrzeugministeriums (DMV) zeigt. https://www.reuters.com/business/autos-transportation/tesla-tells-regulator-that-full-self-driving-cars-may-not-be-achieved-by-year-2021-05-07/ Pony.ais Robotaxi der nächsten Generation zeichnet sich dadurch aus, dass ihm der kegelförmige LIDAR-Sensor auf dem Dach zu fehlen scheint, der für die meisten autonomen Fahrzeuge typisch ist. https://www.theverge.com/2021/5/10/22424726/pony-ai-luminar-lidar-robotaxi-california-china KI- und maschinelle Lernsysteme sind in den letzten Jahren immer kompetenter geworden und können das geschriebene Wort nicht nur verstehen, sondern auch schreiben. https://finance.yahoo.com/news/ibm-codenet-dataset-can-teach-ai-to-translate-computer-languages-020052618.html Visit www.integratedaisolutions.com

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Steve Shwartz: The Truth about Artificial Intelligence

Against All Average

Play Episode Listen Later Apr 9, 2021 49:23

Steve began his AI career as a postdoctoral researcher in the Yale University Artificial Intelligence Lab. Starting in the 1980s, Steve was a founder or cofounder of several AI companies, one of which progressed to a public offering in 1986 and another which created one of the leading business intelligence products of the 1990s. As the AI Winter of the 1990s set in, Steve transitioned into a career as a successful serial software entrepreneur and investor and created several companies that were either acquired or had public offerings. Please welcome to the show Steve Shwartz. Topics CoveredWill AI take over the world or take all our jobs? When will self-driving vehicles dominate our roads and highways? Should governments regulate AI? AI issues concerning privacy, discrimination, and safety. Where Can You Find Steve?Website: www.AIPerspectives.comFacebook: Steve ShwartzTwitter: @sshwartzInstagram: @sshwartz SponsorsFair and Eventwww.fairandevent.com *Where Can You Find Us? * Website: www.againstallaverage.comYoutube: https://www.youtube.com/channel/UCuMcGqduDz9E2mBU5TiH36AFacebook: fb.me/AgainstAllAverageInstagram: (https://instagram.com/againstallaverage)(https://instagram.com/kyletolzman)

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#06 Steve Shwartz

The Judgment Call Podcast

Play Episode Listen Later Dec 9, 2020 91:45

In this episode of the Judgment Call Podcast Steve and I talk about: Will AI be an existential challenge to humanity anytime soon?What progress AI has been making and why it has sped up in the last 10 years so much?Is AI already smarter than teenagers?Is Twitter’s / Facebook AI evil?Why are self-driving cars such bad drivers currently? Will self driving cars be autonomous very soon? Are we inside a simulation and could we create one easily?Has AI already changed the job market to be more short-term?Will AI increase the quality of life?What opportunities are there for entrepreneurs right now? Steve Shwartz began his AI career as a postdoctoral researcher in the Yale University Artificial Intelligence Lab. Starting in the 1980s, Steve was a founder or cofounder of several AI companies, one of which created Esperant, which was one of the leading business intelligence products of the 1990s. As the AI Winter of the 1990s set in, Steve transitioned into a career as a successful serial software entrepreneur and investor and created several companies that were either acquired or had public offerings. He is the author of “Evil Robots, Killer Computers, and Other Myths: The Truth About AI and the Future of Humanity” which will be published on February 9, 2021 by Fast Company Press and maintains a website www.AIPerspectives.com that contains a free, 400-page, online AI 101 textbook. You can reach Steve at LinkedIn. You can find the episode’s transcript here.

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Envision AI – Winter Sale 35% OFF, 14-Day Free Trial and a Chance to Win Envision Glasses! Karthik Kannan from Envision Talks OCR, Column Detection, Translating and the Now Available Envision Glasses

Blind Abilities

Play Episode Listen Later Dec 7, 2020 27:45

Karthik Kannan returned to the Blind Abilities Studio to talk about the Winter Sale on the Envision AI App available in the App Store and the Google Play Store. Now, with a new 14 day trial from Envision, anyone can give the Envision AI app a good go-around. Try out the Instant Text, Document and PDF reading features. Check out the color detection, translate and read from over 50 languages right on your smart phone. With the new features added this year, like column detection and the ability to save your OCR documents in the new Library folder, you can import and export the text with just a few taps. Envision AI has much more to offer and you can find out all about the Envision AI app on the Envision web site at LetsEnvision.com The new Envision Glasses are now available and you can order yours today on the web at www.LetsEnvision.com/glasses Everything you can do in the Envision AI app, you can do on the Envision Glasses and more. Join Karthik Kannan along with Angie Fisher and Jeff Thompson in the Blind Abilities Studio and learn all about the new Envision Glasses, the new features in the Envision AI app and how you can get a chance to win a new pair of the Envision Glasses during the Winter Sale. Be sure to stay up on all the latest from Envision by following Envision on Twitter @LetsEnvision. Contact Your State Services If you reside in Minnesota, and you would like to know more about Transition Services from State Services contact Transition Coordinator Sheila Koenig by email or contact her via phone at 651-539-2361. Contact: You can follow us on Twitter @BlindAbilities On the web at www.BlindAbilities.com Send us an email Get the Free Blind Abilities App on the App Storeand Google Play Store. Check out the Blind Abilities Communityon Facebook, the Blind Abilities Page, and the Career Resources for the Blind and Visually Impaired group

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Can Quantum Computing Prevent Another AI Winter?

Impact Quantum: A Podcast for Engineers

Play Episode Listen Later Oct 15, 2020 17:55

Hello and welcome to Impact Quantum, a podcast about quantum computing for developers and engineers. This episode is entitled "Can Quantum Computing Prevent Another AI Winter?" was recorded on a livestream and is rated one Schroedinger. It also contains several movie references, such as World War Z, the Terminator, and Star Wars. Related Links: Frank's original article https://www.linkedin.com/pulse/can-quantum-computing-prevent-another-ai-winter-frank-la-vigne/ YouTube Live: https://www.youtube.com/watch?v=Z6EFXFrCZ3E LinkedIn Live: https://www.linkedin.com/posts/frank-lavigne_can-quantum-computing-prevent-another-ai-activity-6722490021682671616-MEpT Thanks for listening to Impact Quantum. We know you're busy and we appreciate you listening to our podcast. But we have a favor to ask: please rate and review our podcast on iTunes, Stitcher, or wherever you subscribe to us. Support this podcast

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3_43 AI Winter- L'inverno dell'Intelligenza Artificiale

Radio Innovazione

Play Episode Listen Later Jul 18, 2020 22:43

Molti autorevoli commentatori parlano di Inverno dell'Intelligenza Artificiale. Sembra che l'interesse si sia raffreddato negli ultimi tempi ma molti invece segnalano che è un bene che si esca dallo hype per focalizzarsi sulle applicazioni realmente importanti.RADIO INNOVAZIONE TORNA A METÀ SETTEMBRE! BUONE VACANZE

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3_43 AI Winter- L'inverno dell'Intelligenza Artificiale

Radio Innovazione

Play Episode Listen Later Jul 18, 2020 22:43

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[RB] It's cold outside. Let's speak about AI winter (Ep. 111)

Data Science at Home

Play Episode Listen Later Jul 3, 2020 36:54

In this episode I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019. As a matter of fact, the entire field of AI has been inflated by hype and claims that are hard to believe. A lot of the promises made a few years ago have revealed quite hard to achieve, if not impossible. Let's stay grounded and realistic on the potential of this amazing field of research, not to bring disillusion in the near future. Join us to our Discord channel to discuss your favorite episode and propose new ones. This episode is brought to you by Protonmail Click on the link in the description or go to protonmail.com/datascience and get 20% off their annual subscription.

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The IKEA of AR: Making Content Effortlessly, with EON Reality's Dan Lejerskar

XR for Business

Play Episode Listen Later Jan 20, 2020 37:30

It’s been said on this show before; XR doesn’t have a technology problem, it has an adoption problem. In Dan Lejerskar’s experience, everyone from universities to governments see the value of XR — they just lack the content to make it a worthwhile, everyday tool. He and Alan explore how EON Reality is addressing this discrepancy. Alan: Hi, it’s Alan Smithson here. Today we’re speaking with Dan Lejeskar, founder and chairman of EON Reality, a world leader in virtual/augmented reality based knowledge transfer for industry and education. They believe that knowledge is a human right and it’s their goal to make knowledge available, affordable, and accessible for every human on the planet. We’re going to find out how, in the next XR for Business Podcast. Dan, welcome to the show, my friend. Dan: Thank you so much. Alan: I’m really, really excited. I know you guys have been working– well, you specifically have been working in the 3D virtual space for many years now. How did you get involved in VR and learning? Dan: In my past, I used to work with simulators — big aircraft simulators, etc. — and I got really excited about seeing the effect it has on pilots and soldiers, and I always thought that it would be useful to do the same, but for normal people, nurses, etc. But obviously these people couldn’t afford a $50-million simulator. So I had to be patient and wait until the computers follow Moore’s Law; become cheaper, faster, better. And by ’99, the hardware was there, so you can start running this on PCs. So we were very early adopters of virtual reality already in that period. Alan: We’re talking 20 years. Most people know VR and AR as if kind of something in the last five years. But what was it like kind of going through these growing pains of going from a million dollar simulator — millions of dollars simulator — to now we can buy an Oculus Quest for 500 bucks? Dan: It’s been an interesting journey, with a lot of ups and downs. And very much VR has been like AI. I’m sure you’ve read about the “AI Winter”, when things didn’t go that well. We’ve had quite a few ups and downs in virtual reality. ’99 was fantastic, because that was the era of dot-coms. And we started with something called Web3D, so you can do 3D on the web. It had actually millions of users. Then we had a hard landing 2001. Remember when dot-com crashed? And we had to move our business from industry and education to defence because we had September 11th. So that was kind of what saved our business, doing homeland security centers and the like. And then slowly and surely, we picked up the business up to 2007, 2008. And during this period, there were several iterations. There was something called people avatars and virtual worlds, that was very popular around 2007. That raised and crashed also, pretty tough. But we managed to navigate those water until I would say 2011, 2012, when the hardware became available for mobile devices. So this was before Oculus. Already then we could see where the industry was going. Alan: Oh, you guys, you never lost your path. You’ve veered a little bit from military, to industry and education, back to military, and then back to industry and education. Obviously, the passion is in the industry, knowledge transfer and education. What are some of the projects that you guys have done in the last few years that really just made you go, “Wow, this really is something that, quote unquote, normal people can use?” Dan: So, you’re right. We realized quickly that the biggest value has to do with knowledge

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The IKEA of AR: Making Content Effortlessly, with EON Reality’s Dan Lejerskar

XR for Business

Play Episode Listen Later Jan 20, 2020 37:30

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It's cold outside. Let's speak about AI winter (Ep. 93)

Data Science at Home

Play Episode Listen Later Dec 31, 2019 36:48

In the last episode of 2019 I speak with Filip Piekniewski about some of the most worth noting findings in AI and machine learning in 2019. As a matter of fact, the entire field of AI has been inflated by hype and claims that are hard to believe. A lot of the promises made a few years ago have revealed quite hard to achieve, if not impossible. Let's stay grounded and realistic on the potential of this amazing field of research, not to bring disillusion in the near future. Join us to our Discord channel to discuss your favorite episode and propose new ones. I would like to thank all of you for supporting and inspiring us. I wish you a wonderful 2020! Francesco and the team of Data Science at Home

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Prioritizing training data, model interpretability, and dodging an AI Winter

The Banana Data Podcast

Play Episode Listen Later Aug 16, 2019 27:19

This episode, Triveni and Will tackle the value, ethics, and methods for good labeled data, while also weighing the need for model interpretability and the possibility of an impending AI winter. Triveni will also take us through a step-by-step of the decisions made by a Random Forest algorith As always, be sure to rate and subscribe! Be sure to check out the articles we mentioned this week: The Side of Machine Learning You're Undervaluing and How to Fix it by Matt Wilder (LabelBox) The Hidden Costs of Automated Thinking by Jonathan Zittrain (The New Yorker) Another AI Winter Could Usher in a Dark Period for Artificial Intelligence by Eleanor Cummins (PopSci)

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Episode 267: HPE to Acquire Cray / Lenovo Outlines Transform 3.0 Vision

This Week in HPC

Play Episode Listen Later Jun 4, 2019 21:06

Addison Snell, Tiffany Trader, and Doug Black analyze HPE's purchase of Cray and the reasons behind the $1.3 billion purchase price; plus, Lenovo Transform 3.0 and "AI Winter."

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Max Sklar: Chapter 3: The possibility of an AI winter and the challenges of multi-language NLP

Machine Yearning

Play Episode Listen Later Sep 24, 2018 10:23

This week is a rocket. And the rocket has a name. Max Sklar. Max is machine learning engineer at Foursquare and is a fountain of smart, concise thinking on privacy, social media and whether we’re looking at an imminent AI winter. In this chapter from the longer podcast, Assist’s Shane Mac got so much goodness from his fellow podcaster. We think this is so packed with goodness you’ll want to give it a few listens and DEFINITELY share it with friends. Then…! Make sure you check out Max’s pod - The Local Maximum. It’s awesome.

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Episode 47: Are you ready for AI winter? [Rebroadcast]

Data Science at Home

Play Episode Listen Later Sep 11, 2018 56:55

Today I am having a conversation with Filip Piękniewski, researcher working on computer vision and AI at Koh Young Research America. His adventure with AI started in the 90s and since then a long list of experiences at the intersection of computer science and physics, led him to the conclusion that deep learning might not be sufficient nor appropriate to solve the problem of intelligence, specifically artificial intelligence. I read some of his publications and got familiar with some of his ideas. Honestly, I have been attracted by the fact that Filip does not buy the hype around AI and deep learning in particular. He doesn't seem to share the vision of folks like Elon Musk who claimed that we are going to see an exponential improvement in self driving cars among other things (he actually said that before a Tesla drove over a pedestrian).

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Interview with Andrey Kurnekov, founder of Skynet Today blog

Ask AI Podcast

Play Episode Listen Later Aug 7, 2018 25:30

Interview with Andrey Kurnekov, founder of Skynet Today blog. The media is having a field day with stories, both good and bad, about artificial intelligence and its potential impact on humanity. But who is covering that coverage? This episode of the Ask AI podcast features an interview with Andrey Kurenkov, a founder of the Skynet Today blog. Tune in and hear about Andrey’s work to dispel and correct the misunderstandings and myths within AI, get his views on today’s so-called “AI services”, and how we can prevent the next AI Winter. Enjoy! Stream, download, and subscribe to the Ask AI Podcast here: http://askai.org/podcast This interview and a wealth of content and resources are available in the Ask AI Chabot (type “podcast”): http://askai.org/askai-chatbot This episode was sponsored by Electric Brain: https://www.electricbrain.io For sponsorship, content, and volunteering opportunities, please email: info@askai.org EPISODE HIGHLIGHTS: What exactly is Skynet and what is its role in reviewing media coverage of artificial intelligence? 3 mins 25 secs The one thing that Andey feels is THE most understood fact about artificial intelligence: 5 min 37 secs Why you should be wary of the AI tools and products being released by companies like Facebook and Google 7 min 32 secs What Andrey feels needs to happen in order to avoid another “AI Winter”: 13 mins 15 secs The relationship between AI and robotics: 16 mins 20 secs Andrey’s views on those omnipresent Boston Robotics robo-dogs: EPISODE LINKS: Andrey Kurnekov website: http://www.andreykurenkov.com/ Andrey Kurnekov Twitter: https://twitter.com/andrey_kurenkov Andrey Kurnekov LinkedIn: https://www.linkedin.com/in/andreykurenkov Skynet Today blog: https://www.skynettoday.com Skynet Today on Twitter: https://twitter.com/skynet_today Stanford University AI Lab: http://ai.stanford.edu/ EPISODE CREDITS: Senior Producer: Mike Letourneau Interview recorded by: Robyn Edgar Executive Producer: Chris McLellan

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AI Winter

Linear Digressions

Play Episode Listen Later Jul 15, 2018 19:02

Artificial Intelligence has been widely lauded as a solution to almost any problem. But as we justapose the hype in the field against the real-world benefits we see, it raises the question: Are we coming up on an AI winter

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Episode 56: AI Winter is Coming?

Artificially Intelligent

Play Episode Listen Later Jun 26, 2018 44:21

The AI research community has weathered two "AI winters" - periods where the funding stops flowing to AI research, companies, and products - since the 1950's. More people these days are beginning to speculate that another winter is on the horizon as the market seems to be in a bubble with massive salaries and valuations to everyone associated with the new technology. We take a look at a pair of articles and try to chart a middle-road between the sky is falling and eternal enthusiasm for the prospects of the field. Links: AI Winter is Well on its Way When the Bubble Bursts... Previous episodes mentioned: Episode 54: The Local Maximum Episode 32: The Magic Leap of AI Investment Follow us and leave us a rating! iTunes Homepage Twitter @artlyintelly Facebook artificiallyintelligent1@gmail.com

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Episode 34: Get ready for AI winter

Data Science at Home

Play Episode Listen Later Jun 22, 2018 59:04

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AI Winter (Ale)

PhDrinking

Play Episode Listen Later May 28, 2018 38:28

Machine learning is quite a hot-topic of research right now, with many different offshoots linking it to other fields of research. Scott Cambo, today's guest, studies the intersection of machine learning and human-computer interaction. In particular, he's interested in how mobile self-tracking (think FitBit) user design can increase healthy behaviors and how those apps can use better machine learning algorithms to provide more useful feedback. Suggested Reading: Course Scott is putting together on Human-Centered Machine Learning: https://scottofthescience.github.io/hcml_course/ The website for Northwestern's AI Journal Club: https://aijcnu.github.io/ Scott's lab (CollabLab): http://collablab.northwestern.edu/ Follow me: PhDrinking@gmail.com, @PhDrinking, @SadieWit, www.facebook.com/PhDrinking/ Follow Scott Cambo: @scottOfTheSci , www.ScottAllenCambo.com Thanks to www.bensound.com/ for the intro/outro Thanks to @TylerDamme for audio editing

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Tuesday 5/1 - GDPR kills a startup, stop thinking you're an insomniac, AI winter

Hacker Daily

Play Episode Listen Later May 1, 2018 16:35

Today we're talking about streetlend.com (http://streetlend.com/) shuts down in response to GDPR Why thinking you're an insomniac is part of the problem Watson, Tesla and another AI winter Stripe launches an easy way to start an LLC And finally, Podswap - a tool to grow your podcast audience, for free

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Westworld: extreme theme parks, first computer virus, and sensory processing

Decipher SciFi : the show about how and why

Play Episode Listen Later Apr 24, 2018 42:21

Disney Westworld is some Disneyworld-level theme park logistics. Star Wars Galaxy’s Edge. Westworld ticket prices. The Wild West The mythos. Cowboys shootin’ and rootin- tootin’. The time period. The expansion of settlement exceeding governmental reach and the perception of lawlessness. TV vs Movie Deep questions about consciousness and self and where they really really aren’t. AI in the 70s Depictions of artificial general intelligence in the period and how awesome 2001: A Space Odyssey was/is. The “AI Winter.” The Lighthill Debate on Artificial Intelligence. Heuristics. Teaching our machines to learn. Neural net black boxes. Machines created by other machines. Smart AI/dumb AI Collections of very convincing modules, sans consciousness, vs AGI. In what way can a computer system “understand” language? Sensory processing How long it takes humans and machines to adapt to moving in their environments. The speed of artificial neural networks in learning and human evolution of millennia. Computer vision in 1973! Face tracking. Human attention to human attention. Robotics! The difficulty in developing human-like robots. Our subconscious alertness to any oddness in our human-to–human interactions. The people-watching your brain does when you’re not watching. Computer viruses Coinage. Von Nuemann’s “self-replicating automata.” The Lighthill Debate on Artificial Intelligence [1973]: YouTube Westworld (the series): iTunesAmazon Support the show!

AI Today Podcast #005: The AI Winters

AI Today Podcast: Artificial Intelligence Insights, Experts, and Opinion

Play Episode Listen Later Oct 4, 2017 26:20

Show Notes: Like all technologies, Artificial Intelligence (AI) is not immune to the waves of obscurity, hyped promotion, plateauing of interest, and decline. In fact, the AI industry has been through two such major waves of interest, hype, plateau, and decline, commonly referred to as the “AI Winters”. Now that we’re on our third major wave of AI interest and hype, will we enter a new renaissance where AI becomes an entrenched reality, or will be face another AI Winter? Read more ...

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Episode 103: AI is no longer limited by the garbage that is UNIX

Software Defined Talk

Play Episode Listen Later Aug 17, 2017 57:15

AWS plods on with new capabilities, this time with an AI and enterprise app migration focus, plus, AI: is it actually a thing? We also discuss Microsoft acquiring Cycle Computing and how HPC fits into cloud, also what exactly HPC is and how you measure vibrations passing through a human torso. But most importantly, we’re joined by Andrew Clay Shafer (https://twitter.com/littleidea) in this episode, standing in for Brandon. Removing rebel-slaver memorials Good job (https://mobile.nytimes.com/2017/08/16/us/baltimore-confederate-statues.html), Old Bay land. There’s more cities too (https://www.axios.com/what-other-states-are-doing-with-confederate-era-statues-2472806400.html) on the case too. You like white papers? We got white papers Four new Pivotal white papers (https://content.pivotal.io/white-papers/running-microservices-on-pivotal-cloud-foundry): CI/CD, microservices, PCI (wake up! wake up!), and The Scary Clam (BOSH). We discuss them with the co-author of all of them on this week’s Pivotal Conversations. (https://soundcloud.com/pivotalconversations/pci-bosh-cicd-and-microservices-whitepapers-galore-with-jared-ruckle) Also, check out the Members only podcast if you like white papers, which you probably do, because you’re listening to this bullshit. Amazon Summit NYC There was some Amazon event this week. Anything happen (https://aws.amazon.com/blogs/aws/aws-summit-new-york-summary-of-announcements/)? Machine learning (http://www.barrons.com/articles/amazon-has-largest-a-i-platform-in-the-world-its-machine-learning-guru-boasts-1502735878), and such. Deep dive blog post (https://aws.amazon.com/blogs/aws/launch-amazon-macie-securing-your-s3-buckets/). Interview with Amazon exec (http://www.barrons.com/articles/amazon-has-largest-a-i-platform-in-the-world-its-machine-learning-guru-boasts-1502735878), Matt Wood. AI Winter (https://en.wikipedia.org/wiki/AI_winter#Overview). Maths (https://en.wikipedia.org/wiki/Exponential_smoothing). Also, on various industry CEOs strategamizing around Amazon (https://digiday.com/marketing/amazon-effect-echoes-across-industry/). “Alexa, what’s ‘anti-trust’?” (https://twitter.com/hhoover/status/897489269069107200) Building out Azure HPC Microsoft acquiring Cycle Computing. A market ready for some cash, both for HPC and analytics (https://blogs.the451group.com/techdeals/ma/microsoft-tones-its-hpc-cloud-with-cycle-computing/?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+InorganicGrowth+%28Inorganic+Growth%29): “[a]ccording to 451 Research’s Voice of the Enterprise Cloud Transformation survey, 21% of data and analytics workloads will move to public clouds in the next two years” What about the GreenButton (https://techcrunch.com/2014/05/01/microsoft-acquires-high-performance-cloud-computing-company-greenbutton/) acquisition in 2014? Peep the long piece on that from 2014 (https://www.cio.co.nz/article/544160/latest_tech_merger_microsoft_acquires_kiwi_cloud_computing_company_greenbutton/). Excellent chart (https://blogs.the451group.com/techdeals/files/2017/08/Vote-for-KBI.png) showing migrating COTS to SaaS, etc. https://d2mxuefqeaa7sj.cloudfront.net/s_2B8493BBB6C86E07A5CDF46F818EAD2B22A9BA45EDBC764A7613A8A9E0E13576_1502894906368_image.png BONUS LINKS! Not covered in show Docker raising more cash-money, container land items Lizette Chapman & Eric Newcomer, Bloomberg (https://www.bloomberg.com/news/articles/2017-08-09/docker-is-said-to-be-raising-funding-at-1-3-billion-valuation): “HPC is about three to five years behind enterprise computing when it comes to new technology adoption – the applications are generally more sophisticated, and engineers are conservative…. Business software company Docker Inc. (https://www.bloomberg.com/quote/1041069D:US) is raising fresh funds, valuing the company at $1.3 billion, according to people familiar with the matter.” Also, check out this ADP using Docker case, moderated by Alex Williams (https://thenewstack.io/adp-adopted-container-mindset/), pretty good: 1,000 containers in Nov 2016 to 3,771 in April 2017 (I think these were across dev and prod). MIPS rule everything around me (https://www.geekwire.com/2017/new-version-docker-enterprise-edition-adds-new-admin-features-support-old-reliable-mainframes/). Docker Enterprise feature matrix: https://d2mxuefqeaa7sj.cloudfront.net/s_2B8493BBB6C86E07A5CDF46F818EAD2B22A9BA45EDBC764A7613A8A9E0E13576_1502889970877_file.jpeg Also, putting Oracle in a container (https://www.infoq.com/news/2017/08/containers-core-banking), over there in European banking. Hold my beer platforms (https://thenewstack.io/github-goes-kubernetes-tells/) - It’s easy, just build out all the platform things you need yourself. Yaml all the things! Also, Bash, puppet, terraform, go for log draining(!) and more! Bare-metal, what’s the deal? Oracle got it (http://www.datacenterknowledge.com/archives/2017/08/14/oracle-supercharges-cloud-database-bare-metal-servers/). What’s Twitter got to say (https://twitter.com/cote/status/897477974496342016)? “You get my deck? Let me check Outlook. Who’s doing meeting notes in Word?” https://d2mxuefqeaa7sj.cloudfront.net/s_2B8493BBB6C86E07A5CDF46F818EAD2B22A9BA45EDBC764A7613A8A9E0E13576_1502811233384_image.png Cloud’s cool, but PowerPoint is the shit (https://www.geekwire.com/2017/new-numbers-show-microsofts-biggest-businesses-really-cloud-era/?utm_content=bufferb54e8&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer): “$25.4 billion in revenue in Microsoft’s 2017 fiscal year, an increase of 7 percent from the previous year” Hot Dog Watch Ever vigilant, we’re keeping an eye on the future. The future is stiching together videos for 360 panorama things (http://mashable.com/2017/08/14/snapchat-crowd-surf-concerts-our-stories/#PqhbXnU0cSqO). See the underside of The Hot Dog. Meta, follow-up, etc. Patreon (https://www.patreon.com/sdt) - like anyone who starts these things, I have no idea WTF it is, if it’s a good idea, or if I should be ashamed. Need some product/market fit. Check out the Software Defined Talk Members Only White-Paper Exiguous podcast over there. Join us all in the SDT Slack (http://www.softwaredefinedtalk.com/slack). Mid-roll Get $50 off Casper mattresses with the code: horraymattray NEW DISCOUNT! DevOpsDays Nashville (https://www.devopsdays.org/events/2017-nashville/), $25 off with the code 2017NashDevOpsDays - Coté will be keynoting (https://www.devopsdays.org/events/2017-nashville/speakers/michael-cote/) - October 17th and 18th, 2017. NEW DISCOUNT! DevOpsDays Kansas City (https://www.devopsdays.org/events/2017-kansascity/welcome/), September 21st and 22nd. Use the code SDT2017 when you register (https://www.eventbrite.com/e/devopsdays-kansas-city-2017-tickets-31754843592?aff=ado). PLUS we have one free ticket to give away. So, we need to figure out how to do that. Coté speaking at DevOps Riga (https://www.devopsdays.org/events/2017-riga/welcome/), also will be at DevOpsDays London and Devoxx Belgium. Coté also speaking at Austin OpenStack Meetup (https://www.meetup.com/OpenStack-Austin/events/241908089/), August 17th, 2017. See slides (https://www.slideshare.net/cote/the-cloudnative-enterprise-architect-how-devops-changes-eas-role). The Register’s conference, Continuous Lifecycle (https://continuouslifecycle.london/), in London (May 2018) has it’s CFP open, closed October 20th - submit something (https://continuouslifecycle.london/call-for-papers/)! SpringOne Platform registration open (https://2017.springoneplatform.io/ehome/s1p/registration), Dec 4th to 5th. Use the code S1P200_Cote for $200 off registration (https://2017.springoneplatform.io/ehome/s1p/registration). Matt’s on the Road! August 22nd - Sydney Cloud Native Meetup (https://www.meetup.com/Sydney-Cloud-Native-Meetup/events/241712226/) August 23rd - AWS Sydney North User Group (https://www.meetup.com/en-AU/Amazon-Web-Services-Sydney-North-User-Group/events/240951267/) August 30th - AWS Australian Public Sector Summit (https://aws.amazon.com/summits/canberra-public-sector/) September 12 - Perth MS Cloud Computing User Group (https://www.meetup.com/en-AU/Perth-Cloud/events/241297999/) September 15-16 - DevOpsDays Bangalore (https://www.devopsdays.org/events/2017-bangalore/) September 20 - Azure Sydney Meetup (https://www.meetup.com/Azure-Sydney-User-Group/events/242374004/) October 3-4 - DevOpsDays New Zealand (https://www.devopsdays.org/events/2017-auckland/) October 11th - Brisbane Azure User Group (https://www.meetup.com/Brisbane-Azure-User-Group/events/240477415/) Andrew will be at DevOpsDays Singapore, and a few other places. He doesn’t want to make platinum. # Recommendations Andrew: SLOs, three chapters from the Google SRE (https://landing.google.com/sre/book.html) book (https://landing.google.com/sre/book.html). Matt Ray: Run Bootcamp Windows 10 on a USB Stick (https://hackernoon.com/how-to-run-bootcamp-windows-10-on-a-usb3-86551dc3def8) The secret rhythm in Radiohead’s Videotape (https://www.vox.com/videos/2017/8/4/16092184/videotape-radiohead-secret-rhythm-earworm) Coté: bacon grease in a mug by the stove, that’s how we was livin’ (https://www.youtube.com/watch?v=d0s0XHVUGF0). Speaking of saving bacon grease: Spyderco ParaMilitary 2 G-10 Plain Edge Knife (http://amzn.to/2fImmaR); works well for camping; I got a good deal. WOCStock (https://www.flickr.com/photos/wocintechchat/) - mix up them pasty white-boy slides. Outro from Angela Rye (https://twitter.com/angela_rye), on (http://www.wbur.org/hereandnow/2017/08/16/august-16-2017-hn-two) Here & Now (http://www.wbur.org/hereandnow/2017/08/16/august-16-2017-hn-two), August 16th, 2017 (http://www.wbur.org/hereandnow/2017/08/16/august-16-2017-hn-two). Special Guest: Andrew Clay Shafer.

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MLG 002 What is AI, ML, DS

Machine Learning Guide

Play Episode Listen Later Feb 9, 2017 64:10

Show notes at ocdevel.com/mlg/2 Updated! Skip to [00:29:36] for Data Science (new content) if you've already heard this episode. What is artificial intelligence, machine learning, and data science? What are their differences? AI history. Hierarchical breakdown: DS(AI(ML)). Data science: any profession dealing with data (including AI & ML). Artificial intelligence is simulated intellectual tasks. Machine Learning is algorithms trained on data to learn patterns to make predictions. Artificial Intelligence (AI) - Wikipedia Oxford Languages: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. AlphaGo Movie, very good! Sub-disciplines Reasoning, problem solving Knowledge representation Planning Learning Natural language processing Perception Motion and manipulation Social intelligence General intelligence Applications Autonomous vehicles (drones, self-driving cars) Medical diagnosis Creating art (such as poetry) Proving mathematical theorems Playing games (such as Chess or Go) Search engines Online assistants (such as Siri) Image recognition in photographs Spam filtering Prediction of judicial decisions Targeting online advertisements Machine Learning (ML) - Wikipedia Oxford Languages: the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data. Data Science (DS) - Wikipedia Wikipedia: Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data. History Greek mythology, Golums First attempt: Ramon Lull, 13th century Davinci's walking animals Descartes, Leibniz 1700s-1800s: Statistics & Mathematical decision making Thomas Bayes: reasoning about the probability of events George Boole: logical reasoning / binary algebra Gottlob Frege: Propositional logic 1832: Charles Babbage & Ada Byron / Lovelace: designed Analytical Engine (1832), programmable mechanical calculating machines 1936: Universal Turing Machine Computing Machinery and Intelligence - explored AI! 1946: John von Neumann Universal Computing Machine 1943: Warren McCulloch & Walter Pitts: cogsci rep of neuron; Frank Rosemblatt uses to create Perceptron (-> neural networks by way of MLP) 50s-70s: "AI" coined @Dartmouth workshop 1956 - goal to simulate all aspects of intelligence. John McCarthy, Marvin Minksy, Arthur Samuel, Oliver Selfridge, Ray Solomonoff, Allen Newell, Herbert Simon Newell & Simon: Hueristics -> Logic Theories, General Problem Solver Slefridge: Computer Vision NLP Stanford Research Institute: Shakey Feigenbaum: Expert systems GOFAI / symbolism: operations research / management science; logic-based; knowledge-based / expert systems 70s: Lighthill report (James Lighthill), big promises -> AI Winter 90s: Data, Computation, Practical Application -> AI back (90s) Connectionism optimizations: Geoffrey Hinton: 2006, optimized back propagation Bloomberg, 2015 was whopper for AI in industry AlphaGo & DeepMind

Episode 84: Dick Gabriel on Lisp

Software Engineering Radio - The Podcast for Professional Software Developers

Play Episode Listen Later Jan 22, 2008 60:07

In this Episode we're talking with Dick Gabriel on Lisp. We started by looking at artificial intelligence as the historic context of Lisp, the goals AI tried to reach, and how Lisp was supposed to help reach those. We then discussed the language itself, starting with the Data As Program / Program As Data concept that is a foundation for Lisp. Then we discussed adding a meta-circular interpreter, programming as language development, and the blurred boundary between language and frameworks (because everything uses the same syntax). We then talked about Lisp's type system and the importance of macros to extend the language. The next section concerned CLOS, the Common Lisp Object System and its important concepts: generic functions, multimethods, mixins, and method combination. We also briefly looked at the meta-object protocol but agreed this is a topic for a separate episode. After a discussion about the various dialects of Lisp and Scheme, we concluded the Lisp discussion by explaining why Lisp did not really catch on ("AI Winter") and Lisp's role in today's industry. We ended the episode with a couple of details about Dick's other life as a poet and his Poem a Day effort. Make sure you listen till the end, where we have added a song about Lisp (courtesy of Prometheus Music.)

Episode 84: Dick Gabriel on Lisp

Software Engineering Radio - The Podcast for Professional Software Developers

Play Episode Listen Later Jan 22, 2008 60:08

Episode 84: Dick Gabriel on Lisp

Software Engineering Radio - The Podcast for Professional Software Developers

Play Episode Listen Later Jan 22, 2008 60:08

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