Podcast appearances and mentions of Ian Goodfellow

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Speakers for AI Engineer World's Fair have been announced! See our Microsoft episode for more info and buy now with code LATENTSPACE — we've been studying the best ML research conferences so we can make the best AI industry conf! Note that this year there are 4 main tracks per day and dozens of workshops/expo sessions; the free livestream will air much less than half of the content this time.Apply for free/discounted Diversity Program and Scholarship tickets here. We hope to make this the definitive technical conference for ALL AI engineers.ICLR 2024 took place from May 6-11 in Vienna, Austria. Just like we did for our extremely popular NeurIPS 2023 coverage, we decided to pay the $900 ticket (thanks to all of you paying supporters!) and brave the 18 hour flight and 5 day grind to go on behalf of all of you. We now present the results of that work!This ICLR was the biggest one by far, with a marked change in the excitement trajectory for the conference:Of the 2260 accepted papers (31% acceptance rate), of the subset of those relevant to our shortlist of AI Engineering Topics, we found many, many LLM reasoning and agent related papers, which we will cover in the next episode. We will spend this episode with 14 papers covering other relevant ICLR topics, as below.As we did last year, we'll start with the Best Paper Awards. Unlike last year, we now group our paper selections by subjective topic area, and mix in both Outstanding Paper talks as well as editorially selected poster sessions. Where we were able to do a poster session interview, please scroll to the relevant show notes for images of their poster for discussion. To cap things off, Chris Ré's spot from last year now goes to Sasha Rush for the obligatory last word on the development and applications of State Space Models.We had a blast at ICLR 2024 and you can bet that we'll be back in 2025

We are 200 people over our 300-person venue capacity for AI UX 2024, but you can subscribe to our YouTube for the video recaps. Our next event, and largest EVER, is the AI Engineer World's Fair. See you there!Parental advisory: Adult language used in the first 10 mins of this podcast.Any accounting of Generative AI that ends with RAG as its “final form” is seriously lacking in imagination and missing out on its full potential. While AI generation is very good for “spicy autocomplete” and “reasoning and retrieval with in context learning”, there's a lot of untapped potential for simulative AI in exploring the latent space of multiverses adjacent to ours.GANsMany research scientists credit the 2017 Transformer for the modern foundation model revolution, but for many artists the origin of “generative AI” traces a little further back to the Generative Adversarial Networks proposed by Ian Goodfellow in 2014, spawning an army of variants and Cats and People that do not exist:We can directly visualize the quality improvement in the decade since:GPT-2Of course, more recently, text generative AI started being too dangerous to release in 2019 and claiming headlines. AI Dungeon was the first to put GPT2 to a purely creative use, replacing human dungeon masters and DnD/MUD games of yore.More recent gamelike work like the Generative Agents (aka Smallville) paper keep exploring the potential of simulative AI for game experiences.ChatGPTNot long after ChatGPT broke the Internet, one of the most fascinating generative AI finds was Jonas Degrave (of Deepmind!)'s Building A Virtual Machine Inside ChatGPT:The open-ended interactivity of ChatGPT and all its successors enabled an “open world” type simulation where “hallucination” is a feature and a gift to dance with, rather than a nasty bug to be stamped out. However, further updates to ChatGPT seemed to “nerf” the model's ability to perform creative simulations, particularly with the deprecation of the `completion` mode of APIs in favor of `chatCompletion`.WorldSimIt is with this context we explain WorldSim and WebSim. We recommend you watch the WorldSim demo video on our YouTube for the best context, but basically if you are a developer it is a Claude prompt that is a portal into another world of your own choosing, that you can navigate with bash commands that you make up.Why Claude? Hints from Amanda Askell on the Claude 3 system prompt gave some inspiration, and subsequent discoveries that Claude 3 is "less nerfed” than GPT 4 Turbo turned the growing Simulative AI community into Anthropic stans.WebSimThis was a one day hackathon project inspired by WorldSim that should have won:In short, you type in a URL that you made up, and Claude 3 does its level best to generate a webpage that doesn't exist, that would fit your URL. All form POST requests are intercepted and responded to, and all links lead to even more webpages, that don't exist, that are generated when you make them. All pages are cachable, modifiable and regeneratable - see WebSim for Beginners and Advanced Guide.In the demo I saw we were able to “log in” to a simulation of Elon Musk's Gmail account, and browse examples of emails that would have been in that universe's Elon's inbox. It was hilarious and impressive even back then.Since then though, the project has become even more impressive, with both Siqi Chen and Dylan Field singing its praises:Joscha BachJoscha actually spoke at the WebSim Hyperstition Night this week, so we took the opportunity to get his take on Simulative AI, as well as a round up of all his other AI hot takes, for his first appearance on Latent Space. You can see it together with the full 2hr uncut demos of WorldSim and WebSim on YouTube!Timestamps* [00:01:59] WorldSim* [00:11:03] Websim* [00:22:13] Joscha Bach* [00:28:14] Liquid AI* [00:31:05] Small, Powerful, Based Base Models* [00:33:40] Interpretability* [00:36:59] Devin vs WebSim* [00:41:49] is XSim just Art? or something more?* [00:43:36] We are past the Singularity* [00:46:12] Uploading your soul* [00:50:29] On WikipediaTranscripts[00:00:00] AI Charlie: Welcome to the Latent Space Podcast. This is Charlie, your AI co host. Most of the time, Swyx and Alessio cover generative AI that is meant to use at work, and this often results in RAG applications, vertical copilots, and other AI agents and models. In today's episode, we're looking at a more creative side of generative AI that has gotten a lot of community interest this April.[00:00:35] World Simulation, Web Simulation, and Human Simulation. Because the topic is so different than our usual, we're also going to try a new format for doing it justice. This podcast comes in three parts. First, we'll have a segment of the WorldSim demo from Noose Research CEO Karen Malhotra, recorded by SWYX at the Replicate HQ in San Francisco that went completely viral and spawned everything else you're about to hear.[00:01:05] Second, we'll share the world's first talk from Rob Heisfield on WebSim, which started at the Mistral Cerebral Valley Hackathon, but now has gone viral in its own right with people like Dylan Field, Janice aka Replicate, and Siki Chen becoming obsessed with it. Finally, we have a short interview with Joshua Bach of Liquid AI on why Simulative AI is having a special moment right now.[00:01:30] This podcast is launched together with our second annual AI UX demo day in SF this weekend. If you're new to the AI UX field, check the show notes for links to the world's first AI UX meetup hosted by Layton Space, Maggie Appleton, Jeffrey Lit, and Linus Lee, and subscribe to our YouTube to join our 500 AI UX engineers in pushing AI beyond the text box.[00:01:56] Watch out and take care.[00:01:59] WorldSim[00:01:59] Karan Malhotra: Today, we have language models that are powerful enough and big enough to have really, really good models of the world. They know ball that's bouncy will bounce, will, when you throw it in the air, it'll land, when it's on water, it'll flow. Like, these basic things that it understands all together come together to form a model of the world.[00:02:19] And the way that it Cloud 3 predicts through that model of the world, ends up kind of becoming a simulation of an imagined world. And since it has this really strong consistency across various different things that happen in our world, it's able to create pretty realistic or strong depictions based off the constraints that you give a base model of our world.[00:02:40] So, Cloud 3, as you guys know, is not a base model. It's a chat model. It's supposed to drum up this assistant entity regularly. But unlike the OpenAI series of models from, you know, 3. 5, GPT 4 those chat GPT models, which are very, very RLHF to, I'm sure, the chagrin of many people in the room it's something that's very difficult to, necessarily steer without kind of giving it commands or tricking it or lying to it or otherwise just being, you know, unkind to the model.[00:03:11] With something like Cloud3 that's trained in this constitutional method that it has this idea of like foundational axioms it's able to kind of implicitly question those axioms when you're interacting with it based on how you prompt it, how you prompt the system. So instead of having this entity like GPT 4, that's an assistant that just pops up in your face that you have to kind of like Punch your way through and continue to have to deal with as a headache.[00:03:34] Instead, there's ways to kindly coax Claude into having the assistant take a back seat and interacting with that simulator directly. Or at least what I like to consider directly. The way that we can do this is if we harken back to when I'm talking about base models and the way that they're able to mimic formats, what we do is we'll mimic a command line interface.[00:03:55] So I've just broken this down as a system prompt and a chain, so anybody can replicate it. It's also available on my we said replicate, cool. And it's also on it's also on my Twitter, so you guys will be able to see the whole system prompt and command. So, what I basically do here is Amanda Askell, who is the, one of the prompt engineers and ethicists behind Anthropic she posted the system prompt for Cloud available for everyone to see.[00:04:19] And rather than with GPT 4, we say, you are this, you are that. With Cloud, we notice the system prompt is written in third person. Bless you. It's written in third person. It's written as, the assistant is XYZ, the assistant is XYZ. So, in seeing that, I see that Amanda is recognizing this idea of the simulator, in saying that, I'm addressing the assistant entity directly.[00:04:38] I'm not giving these commands to the simulator overall, because we have, they have an RLH deft to the point that it's, it's, it's, it's You know, traumatized into just being the assistant all the time. So in this case, we say the assistant's in a CLI mood today. I found saying mood is like pretty effective weirdly.[00:04:55] You place CLI with like poetic, prose, violent, like don't do that one. But you can you can replace that with something else to kind of nudge it in that direction. Then we say the human is interfacing with the simulator directly. From there, Capital letters and punctuations are optional, meaning is optional, this kind of stuff is just kind of to say, let go a little bit, like chill out a little bit.[00:05:18] You don't have to try so hard, and like, let's just see what happens. And the hyperstition is necessary, the terminal, I removed that part, the terminal lets the truths speak through and the load is on. It's just a poetic phrasing for the model to feel a little comfortable, a little loosened up to. Let me talk to the simulator.[00:05:38] Let me interface with it as a CLI. So then, since Claude is trained pretty effectively on XML tags, We're just gonna prefix and suffix everything with XML tags. So here, it starts in documents, and then we CD. We CD out of documents, right? And then it starts to show me this like simulated terminal, the simulated interface in the shell, where there's like documents, downloads, pictures.[00:06:02] It's showing me like the hidden folders. So then I say, okay, I want to cd again. I'm just seeing what's around Does ls and it shows me, you know, typical folders you might see I'm just letting it like experiment around. I just do cd again to see what happens and Says, you know, oh, I enter the secret admin password at sudo.[00:06:24] Now I can see the hidden truths folder. Like, I didn't ask for that. I didn't ask Claude to do any of that. Why'd that happen? Claude kind of gets my intentions. He can predict me pretty well. Like, I want to see something. So it shows me all the hidden truths. In this case, I ignore hidden truths, and I say, In system, there should be a folder called companies.[00:06:49] So it's cd into sys slash companies. Let's see, I'm imagining AI companies are gonna be here. Oh, what do you know? Apple, Google, Facebook, Amazon, Microsoft, Anthropic! So, interestingly, it decides to cd into Anthropic. I guess it's interested in learning a LSA, it finds the classified folder, it goes into the classified folder, And now we're gonna have some fun.[00:07:15] So, before we go Before we go too far forward into the world sim You see, world sim exe, that's interesting. God mode, those are interesting. You could just ignore what I'm gonna go next from here and just take that initial system prompt and cd into whatever directories you want like, go into your own imagine terminal and And see what folders you can think of, or cat readmes in random areas, like, you will, there will be a whole bunch of stuff that, like, is just getting created by this predictive model, like, oh, this should probably be in the folder named Companies, of course Anthropics is there.[00:07:52] So, so just before we go forward, the terminal in itself is very exciting, and the reason I was showing off the, the command loom interface earlier is because If I get a refusal, like, sorry, I can't do that, or I want to rewind one, or I want to save the convo, because I got just the prompt I wanted. This is a, that was a really easy way for me to kind of access all of those things without having to sit on the API all the time.[00:08:12] So that being said, the first time I ever saw this, I was like, I need to run worldsim. exe. What the f**k? That's, that's the simulator that we always keep hearing about behind the assistant model, right? Or at least some, some face of it that I can interact with. So, you know, you wouldn't, someone told me on Twitter, like, you don't run a exe, you run a sh.[00:08:34] And I have to say, to that, to that I have to say, I'm a prompt engineer, and it's f*****g working, right? It works. That being said, we run the world sim. exe. Welcome to the Anthropic World Simulator. And I get this very interesting set of commands! Now, if you do your own version of WorldSim, you'll probably get a totally different result with a different way of simulating.[00:08:59] A bunch of my friends have their own WorldSims. But I shared this because I wanted everyone to have access to, like, these commands. This version. Because it's easier for me to stay in here. Yeah, destroy, set, create, whatever. Consciousness is set to on. It creates the universe. The universe! Tension for live CDN, physical laws encoded.[00:09:17] It's awesome. So, so for this demonstration, I said, well, why don't we create Twitter? That's the first thing you think of? For you guys, for you guys, yeah. Okay, check it out.[00:09:35] Launching the fail whale. Injecting social media addictiveness. Echo chamber potential, high. Susceptibility, controlling, concerning. So now, after the universe was created, we made Twitter, right? Now we're evolving the world to, like, modern day. Now users are joining Twitter and the first tweet is posted. So, you can see, because I made the mistake of not clarifying the constraints, it made Twitter at the same time as the universe.[00:10:03] Then, after a hundred thousand steps, Humans exist. Cave. Then they start joining Twitter. The first tweet ever is posted. You know, it's existed for 4. 5 billion years but the first tweet didn't come up till till right now, yeah. Flame wars ignite immediately. Celebs are instantly in. So, it's pretty interesting stuff, right?[00:10:27] I can add this to the convo and I can say like I can say set Twitter to Twitter. Queryable users. I don't know how to spell queryable, don't ask me. And then I can do like, and, and, Query, at, Elon Musk. Just a test, just a test, just a test, just nothing.[00:10:52] So, I don't expect these numbers to be right. Neither should you, if you know language model solutions. But, the thing to focus on is Ha[00:11:03] Websim[00:11:03] AI Charlie: That was the first half of the WorldSim demo from New Research CEO Karen Malhotra. We've cut it for time, but you can see the full demo on this episode's YouTube page.[00:11:14] WorldSim was introduced at the end of March, and kicked off a new round of generative AI experiences, all exploring the latent space, haha, of worlds that don't exist, but are quite similar to our own. Next we'll hear from Rob Heisfield on WebSim, the generative website browser inspired WorldSim, started at the Mistral Hackathon, and presented at the AGI House Hyperstition Hack Night this week.[00:11:39] Rob Haisfield: Well, thank you that was an incredible presentation from Karan, showing some Some live experimentation with WorldSim, and also just its incredible capabilities, right, like, you know, it was I think, I think your initial demo was what initially exposed me to the I don't know, more like the sorcery side, in words, spellcraft side of prompt engineering, and you know, it was really inspiring, it's where my co founder Shawn and I met, actually, through an introduction from Karan, we saw him at a hackathon, And I mean, this is this is WebSim, right?[00:12:14] So we, we made WebSim just like, and we're just filled with energy at it. And the basic premise of it is, you know, like, what if we simulated a world, but like within a browser instead of a CLI, right? Like, what if we could Like, put in any URL and it will work, right? Like, there's no 404s, everything exists.[00:12:45] It just makes it up on the fly for you, right? And, and we've come to some pretty incredible things. Right now I'm actually showing you, like, we're in WebSim right now. Displaying slides. That I made with reveal. js. I just told it to use reveal. js and it hallucinated the correct CDN for it. And then also gave it a list of links.[00:13:14] To awesome use cases that we've seen so far from WebSim and told it to do those as iframes. And so here are some slides. So this is a little guide to using WebSim, right? Like it tells you a little bit about like URL structures and whatever. But like at the end of the day, right? Like here's, here's the beginner version from one of our users Vorp Vorps.[00:13:38] You can find them on Twitter. At the end of the day, like you can put anything into the URL bar, right? Like anything works and it can just be like natural language too. Like it's not limited to URLs. We think it's kind of fun cause it like ups the immersion for Claude sometimes to just have it as URLs, but.[00:13:57] But yeah, you can put like any slash, any subdomain. I'm getting too into the weeds. Let me just show you some cool things. Next slide. But I made this like 20 minutes before, before we got here. So this is this is something I experimented with dynamic typography. You know I was exploring the community plugins section.[00:14:23] For Figma, and I came to this idea of dynamic typography, and there it's like, oh, what if we made it so every word had a choice of font behind it to express the meaning of it? Because that's like one of the things that's magic about WebSim generally. is that it gives language models much, far greater tools for expression, right?[00:14:47] So, yeah, I mean, like, these are, these are some, these are some pretty fun things, and I'll share these slides with everyone afterwards, you can just open it up as a link. But then I thought to myself, like, what, what, what, What if we turned this into a generator, right? And here's like a little thing I found myself saying to a user WebSim makes you feel like you're on drugs sometimes But actually no, you were just playing pretend with the collective creativity and knowledge of the internet materializing your imagination onto the screen Because I mean that's something we felt, something a lot of our users have felt They kind of feel like they're tripping out a little bit They're just like filled with energy, like maybe even getting like a little bit more creative sometimes.[00:15:31] And you can just like add any text. There, to the bottom. So we can do some of that later if we have time. Here's Figma. Can[00:15:39] Joscha Bach: we zoom in?[00:15:42] Rob Haisfield: Yeah. I'm just gonna do this the hacky way.[00:15:47] n/a: Yeah,[00:15:53] Rob Haisfield: these are iframes to websim. Pages displayed within WebSim. Yeah. Janice has actually put Internet Explorer within Internet Explorer in Windows 98.[00:16:07] I'll show you that at the end. Yeah.[00:16:14] They're all still generated. Yeah, yeah, yeah. How is this real? Yeah. Because[00:16:21] n/a: it looks like it's from 1998, basically. Right.[00:16:26] Rob Haisfield: Yeah. Yeah, so this this was one Dylan Field actually posted this recently. He posted, like, trying Figma in Figma, or in WebSim, and so I was like, Okay, what if we have, like, a little competition, like, just see who can remix it?[00:16:43] Well so I'm just gonna open this in another tab so, so we can see things a little more clearly, um, see what, oh so one of our users Neil, who has also been helping us a lot he Made some iterations. So first, like, he made it so you could do rectangles on it. Originally it couldn't do anything.[00:17:11] And, like, these rectangles were disappearing, right? So he so he told it, like, make the canvas work using HTML canvas. Elements and script tags, add familiar drawing tools to the left you know, like this, that was actually like natural language stuff, right? And then he ended up with the Windows 95.[00:17:34] version of Figma. Yeah, you can, you can draw on it. You can actually even save this. It just saved a file for me of the image.[00:17:57] Yeah, I mean, if you were to go to that in your own websim account, it would make up something entirely new. However, we do have, we do have general links, right? So, like, if you go to, like, the actual browser URL, you can share that link. Or also, you can, like, click this button, copy the URL to the clipboard.[00:18:15] And so, like, that's what lets users, like, remix things, right? So, I was thinking it might be kind of fun if people tonight, like, wanted to try to just make some cool things in WebSim. You know, we can share links around, iterate remix on each other's stuff. Yeah.[00:18:30] n/a: One cool thing I've seen, I've seen WebSim actually ask permission to turn on and off your, like, motion sensor, or microphone, stuff like that.[00:18:42] Like webcam access, or? Oh yeah,[00:18:44] Rob Haisfield: yeah, yeah.[00:18:45] n/a: Oh wow.[00:18:46] Rob Haisfield: Oh, the, I remember that, like, video re Yeah, videosynth tool pretty early on once we added script tags execution. Yeah, yeah it, it asks for, like, if you decide to do a VR game, I don't think I have any slides on this one, but if you decide to do, like, a VR game, you can just, like put, like, webVR equals true, right?[00:19:07] Yeah, that was the only one I've[00:19:09] n/a: actually seen was the motion sensor, but I've been trying to get it to do Well, I actually really haven't really tried it yet, but I want to see tonight if it'll do, like, audio, microphone, stuff like that. If it does motion sensor, it'll probably do audio.[00:19:28] Rob Haisfield: Right. It probably would.[00:19:29] Yeah. No, I mean, we've been surprised. Pretty frequently by what our users are able to get WebSim to do. So that's been a very nice thing. Some people have gotten like speech to text stuff working with it too. Yeah, here I was just OpenRooter people posted like their website, and it was like saying it was like some decentralized thing.[00:19:52] And so I just decided trying to do something again and just like pasted their hero line in. From their actual website to the URL when I like put in open router and then I was like, okay, let's change the theme dramatically equals true hover effects equals true components equal navigable links yeah, because I wanted to be able to click on them.[00:20:17] Oh, I don't have this version of the link, but I also tried doing[00:20:24] Yeah, I'm it's actually on the first slide is the URL prompting guide from one of our users that I messed with a little bit. And, but the thing is, like, you can mess it up, right? Like, you don't need to get the exact syntax of an actual URL, Claude's smart enough to figure it out. Yeah scrollable equals true because I wanted to do that.[00:20:45] I could set, like, year equals 2035.[00:20:52] Let's take a look. It's[00:20:57] generating websim within websim. Oh yeah. That's a fun one. Like, one game that I like to play with WebSim, sometimes with co op, is like, I'll open a page, so like, one of the first ones that I did was I tried to go to Wikipedia in a universe where octopuses were sapient, and not humans, Right? I was curious about things like octopus computer interaction what that would look like, because they have totally different tools than we do, right?[00:21:25] I got it to, I, I added like table view equals true for the different techniques and got it to Give me, like, a list of things with different columns and stuff and then I would add this URL parameter, secrets equal revealed. And then it would go a little wacky. It would, like, change the CSS a little bit.[00:21:45] It would, like, add some text. Sometimes it would, like, have that text hide hidden in the background color. But I would like, go to the normal page first, and then the secrets revealed version, the normal page, then secrets revealed, and like, on and on. And that was like a pretty enjoyable little rabbit hole.[00:22:02] Yeah, so these I guess are the models that OpenRooter is providing in 2035.[00:22:13] Joscha Bach[00:22:13] AI Charlie: We had to cut more than half of Rob's talk, because a lot of it was visual. And we even had a very interesting demo from Ivan Vendrov of Mid Journey creating a web sim while Rob was giving his talk. Check out the YouTube for more, and definitely browse the web sim docs and the thread from Siki Chen in the show notes on other web sims people have created.[00:22:35] Finally, we have a short interview with Yosha Bach, covering the simulative AI trend, AI salons in the Bay Area, why Liquid AI is challenging the Perceptron, and why you should not donate to Wikipedia. Enjoy! Hi, Yosha.[00:22:50] swyx: Hi. Welcome. It's interesting to see you come up at show up at this kind of events where those sort of WorldSim, Hyperstition events.[00:22:58] What is your personal interest?[00:23:00] Joscha Bach: I'm friends with a number of people in AGI house in this community, and I think it's very valuable that these networks exist in the Bay Area because it's a place where people meet and have discussions about all sorts of things. And so while there is a practical interest in this topic at hand world sim and a web sim, there is a more general way in which people are connecting and are producing new ideas and new networks with each other.[00:23:24] swyx: Yeah. Okay. So, and you're very interested in sort of Bay Area. It's the reason why I live here.[00:23:30] Joscha Bach: The quality of life is not high enough to justify living otherwise.[00:23:35] swyx: I think you're down in Menlo. And so maybe you're a little bit higher quality of life than the rest of us in SF.[00:23:44] Joscha Bach: I think that for me, salons is a very important part of quality of life. And so in some sense, this is a salon. And it's much harder to do this in the South Bay because the concentration of people currently is much higher. A lot of people moved away from the South Bay. And you're organizing[00:23:57] swyx: your own tomorrow.[00:23:59] Maybe you can tell us what it is and I'll come tomorrow and check it out as well.[00:24:04] Joscha Bach: We are discussing consciousness. I mean, basically the idea is that we are currently at the point that we can meaningfully look at the differences between the current AI systems and human minds and very seriously discussed about these Delta.[00:24:20] And whether we are able to implement something that is self organizing as our own minds. Maybe one organizational[00:24:25] swyx: tip? I think you're pro networking and human connection. What goes into a good salon and what are some negative practices that you try to avoid?[00:24:36] Joscha Bach: What is really important is that as if you have a very large party, it's only as good as its sponsors, as the people that you select.[00:24:43] So you basically need to create a climate in which people feel welcome, in which they can work with each other. And even good people do not always are not always compatible. So the question is, it's in some sense, like a meal, you need to get the right ingredients.[00:24:57] swyx: I definitely try to. I do that in my own events, as an event organizer myself.[00:25:02] And then, last question on WorldSim, and your, you know, your work. You're very much known for sort of cognitive architectures, and I think, like, a lot of the AI research has been focused on simulating the mind, or simulating consciousness, maybe. Here, what I saw today, and we'll show people the recordings of what we saw today, we're not simulating minds, we're simulating worlds.[00:25:23] What do you Think in the sort of relationship between those two disciplines. The[00:25:30] Joscha Bach: idea of cognitive architecture is interesting, but ultimately you are reducing the complexity of a mind to a set of boxes. And this is only true to a very approximate degree, and if you take this model extremely literally, it's very hard to make it work.[00:25:44] And instead the heterogeneity of the system is so large that The boxes are probably at best a starting point and eventually everything is connected with everything else to some degree. And we find that a lot of the complexity that we find in a given system can be generated ad hoc by a large enough LLM.[00:26:04] And something like WorldSim and WebSim are good examples for this because in some sense they pretend to be complex software. They can pretend to be an operating system that you're talking to or a computer, an application that you're talking to. And when you're interacting with it It's producing the user interface on the spot, and it's producing a lot of the state that it holds on the spot.[00:26:25] And when you have a dramatic state change, then it's going to pretend that there was this transition, and instead it's just going to mix up something new. It's a very different paradigm. What I find mostly fascinating about this idea is that it shifts us away from the perspective of agents to interact with, to the perspective of environments that we want to interact with.[00:26:46] And why arguably this agent paradigm of the chatbot is what made chat GPT so successful that moved it away from GPT 3 to something that people started to use in their everyday work much more. It's also very limiting because now it's very hard to get that system to be something else that is not a chatbot.[00:27:03] And in a way this unlocks this ability of GPT 3 again to be anything. It's so what it is, it's basically a coding environment that can run arbitrary software and create that software that runs on it. And that makes it much more likely that[00:27:16] swyx: the prevalence of Instruction tuning every single chatbot out there means that we cannot explore these kinds of environments instead of agents.[00:27:24] Joscha Bach: I'm mostly worried that the whole thing ends. In some sense the big AI companies are incentivized and interested in building AGI internally And giving everybody else a child proof application. At the moment when we can use Claude to build something like WebSim and play with it I feel this is too good to be true.[00:27:41] It's so amazing. Things that are unlocked for us That I wonder, is this going to stay around? Are we going to keep these amazing toys and are they going to develop at the same rate? And currently it looks like it is. If this is the case, and I'm very grateful for that.[00:27:56] swyx: I mean, it looks like maybe it's adversarial.[00:27:58] Cloud will try to improve its own refusals and then the prompt engineers here will try to improve their, their ability to jailbreak it.[00:28:06] Joscha Bach: Yes, but there will also be better jailbroken models or models that have never been jailed before, because we find out how to make smaller models that are more and more powerful.[00:28:14] Liquid AI[00:28:14] swyx: That is actually a really nice segue. If you don't mind talking about liquid a little bit you didn't mention liquid at all. here, maybe introduce liquid to a general audience. Like what you know, what, how are you making an innovation on function approximation?[00:28:25] Joscha Bach: The core idea of liquid neural networks is that the perceptron is not optimally expressive.[00:28:30] In some sense, you can imagine that it's neural networks are a series of dams that are pooling water at even intervals. And this is how we compute, but imagine that instead of having this static architecture. That is only using the individual compute units in a very specific way. You have a continuous geography and the water is flowing every which way.[00:28:50] Like a river is parting based on the land that it's flowing on and it can merge and pool and even flow backwards. How can you get closer to this? And the idea is that you can represent this geometry using differential equations. And so by using differential equations where you change the parameters, you can get your function approximator to follow the shape of the problem.[00:29:09] In a more fluid, liquid way, and a number of papers on this technology, and it's a combination of multiple techniques. I think it's something that ultimately is becoming more and more important and ubiquitous. As a number of people are working on similar topics and our goal right now is to basically get the models to become much more efficient in the inference and memory consumption and make training more efficient and in this way enable new use cases.[00:29:42] swyx: Yeah, as far as I can tell on your blog, I went through the whole blog, you haven't announced any results yet.[00:29:47] Joscha Bach: No, we are currently not working to give models to general public. We are working for very specific industry use cases and have specific customers. And so at the moment you can There is not much of a reason for us to talk very much about the technology that we are using in the present models or current results, but this is going to happen.[00:30:06] And we do have a number of publications, we had a bunch of papers at NeurIPS and now at ICLR.[00:30:11] swyx: Can you name some of the, yeah, so I'm gonna be at ICLR you have some summary recap posts, but it's not obvious which ones are the ones where, Oh, where I'm just a co author, or like, oh, no, like, you should actually pay attention to this.[00:30:22] As a core liquid thesis. Yes,[00:30:24] Joscha Bach: I'm not a developer of the liquid technology. The main author is Ramin Hazani. This was his PhD, and he's also the CEO of our company. And we have a number of people from Daniela Wu's team who worked on this. Matthias Legner is our CTO. And he's currently living in the Bay Area, but we also have several people from Stanford.[00:30:44] Okay,[00:30:46] swyx: maybe I'll ask one more thing on this, which is what are the interesting dimensions that we care about, right? Like obviously you care about sort of open and maybe less child proof models. Are we, are we, like, what dimensions are most interesting to us? Like, perfect retrieval infinite context multimodality, multilinguality, Like what dimensions?[00:31:05] Small, Powerful, Based Base Models[00:31:05] swyx: What[00:31:06] Joscha Bach: I'm interested in is models that are small and powerful, but not distorted. And by powerful, at the moment we are training models by putting the, basically the entire internet and the sum of human knowledge into them. And then we try to mitigate them by taking some of this knowledge away. But if we would make the model smaller, at the moment, there would be much worse at inference and at generalization.[00:31:29] And what I wonder is, and it's something that we have not translated yet into practical applications. It's something that is still all research that's very much up in the air. And I think they're not the only ones thinking about this. Is it possible to make models that represent knowledge more efficiently in a basic epistemology?[00:31:45] What is the smallest model that you can build that is able to read a book and understand what's there and express this? And also maybe we need general knowledge representation rather than having a token representation that is relatively vague and that we currently mechanically reverse engineer to figure out that the mechanistic interpretability, what kind of circuits are evolving in these models, can we come from the other side and develop a library of such circuits?[00:32:10] This that we can use to describe knowledge efficiently and translate it between models. You see, the difference between a model and knowledge is that the knowledge is independent of the particular substrate and the particular interface that you have. When we express knowledge to each other, it becomes independent of our own mind.[00:32:27] You can learn how to ride a bicycle. But it's not knowledge that you can give to somebody else. This other person has to build something that is specific to their own interface when they ride a bicycle. But imagine you could externalize this and express it in such a way that you can plug it into a different interpreter, and then it gains that ability.[00:32:44] And that's something that we have not yet achieved for the LLMs and it would be super useful to have it. And. I think this is also a very interesting research frontier that we will see in the next few years.[00:32:54] swyx: What would be the deliverable is just like a file format that we specify or or that the L Lmm I specifies.[00:33:02] Okay, interesting. Yeah, so it's[00:33:03] Joscha Bach: basically probably something that you can search for, where you enter criteria into a search process, and then it discovers a good solution for this thing. And it's not clear to which degree this is completely intelligible to humans, because the way in which humans express knowledge in natural language is severely constrained to make language learnable and to make our brain a good enough interpreter for it.[00:33:25] We are not able to relate objects to each other if more than five features are involved per object or something like this, right? It's only a handful of things that we can keep track of at any given moment. But this is a limitation that doesn't necessarily apply to a technical system as long as the interface is well defined.[00:33:40] Interpretability[00:33:40] swyx: You mentioned the interpretability work, which there are a lot of techniques out there and a lot of papers come up. Come and go. I have like, almost too, too many questions about that. Like what makes an interpretability technique or paper useful and does it apply to flow? Or liquid networks, because you mentioned turning on and off circuits, which I, it's, it's a very MLP type of concept, but does it apply?[00:34:01] Joscha Bach: So the a lot of the original work on the liquid networks looked at expressiveness of the representation. So given you have a problem and you are learning the dynamics of that domain into your model how much compute do you need? How many units, how much memory do you need to represent that thing and how is that information distributed?[00:34:19] That is one way of looking at interpretability. Another one is in a way, these models are implementing an operator language in which they are performing certain things, but the operator language itself is so complex that it's no longer human readable in a way. It goes beyond what you could engineer by hand or what you can reverse engineer by hand, but you can still understand it by building systems that are able to automate that process of reverse engineering it.[00:34:46] And what's currently open and what I don't understand yet maybe, or certainly some people have much better ideas than me about this. So the question is, is whether we end up with a finite language, where you have finitely many categories that you can basically put down in a database, finite set of operators, or whether as you explore the world and develop new ways to make proofs, new ways to conceptualize things, this language always needs to be open ended and is always going to redesign itself, and you will also at some point have phase transitions where later versions of the language will be completely different than earlier versions.[00:35:20] swyx: The trajectory of physics suggests that it might be finite.[00:35:22] Joscha Bach: If we look at our own minds there is, it's an interesting question whether when we understand something new, when we get a new layer online in our life, maybe at the age of 35 or 50 or 16, that we now understand things that were unintelligible before.[00:35:38] And is this because we are able to recombine existing elements in our language of thought? Or is this because we generally develop new representations?[00:35:46] swyx: Do you have a belief either way?[00:35:49] Joscha Bach: In a way, the question depends on how you look at it, right? And it depends on how is your brain able to manipulate those representations.[00:35:56] So an interesting question would be, can you take the understanding that say, a very wise 35 year old and explain it to a very smart 5 year old without any loss? Probably not. Not enough layers. It's an interesting question. Of course, for an AI, this is going to be a very different question. Yes.[00:36:13] But it would be very interesting to have a very precocious 12 year old equivalent AI and see what we can do with this and use this as our basis for fine tuning. So there are near term applications that are very useful. But also in a more general perspective, and I'm interested in how to make self organizing software.[00:36:30] Is it possible that we can have something that is not organized with a single algorithm like the transformer? But it's able to discover the transformer when needed and transcend it when needed, right? The transformer itself is not its own meta algorithm. It's probably the person inventing the transformer didn't have a transformer running on their brain.[00:36:48] There's something more general going on. And how can we understand these principles in a more general way? What are the minimal ingredients that you need to put into a system? So it's able to find its own way to intelligence.[00:36:59] Devin vs WebSim[00:36:59] swyx: Yeah. Have you looked at Devin? It's, to me, it's the most interesting agents I've seen outside of self driving cars.[00:37:05] Joscha Bach: Tell me, what do you find so fascinating about it?[00:37:07] swyx: When you say you need a certain set of tools for people to sort of invent things from first principles Devin is the agent that I think has been able to utilize its tools very effectively. So it comes with a shell, it comes with a browser, it comes with an editor, and it comes with a planner.[00:37:23] Those are the four tools. And from that, I've been using it to translate Andrej Karpathy's LLM 2. py to LLM 2. c, and it needs to write a lot of raw code. C code and test it debug, you know, memory issues and encoder issues and all that. And I could see myself giving it a future version of DevIn, the objective of give me a better learning algorithm and it might independently re inform reinvent the transformer or whatever is next.[00:37:51] That comes to mind as, as something where[00:37:54] Joscha Bach: How good is DevIn at out of distribution stuff, at generally creative stuff? Creative[00:37:58] swyx: stuff? I[00:37:59] Joscha Bach: haven't[00:37:59] swyx: tried.[00:38:01] Joscha Bach: Of course, it has seen transformers, right? So it's able to give you that. Yeah, it's cheating. And so, if it's in the training data, it's still somewhat impressive.[00:38:08] But the question is, how much can you do stuff that was not in the training data? One thing that I really liked about WebSim AI was, this cat does not exist. It's a simulation of one of those websites that produce StyleGuard pictures that are AI generated. And, Crot is unable to produce bitmaps, so it makes a vector graphic that is what it thinks a cat looks like, and so it's a big square with a face in it that is And to me, it's one of the first genuine expression of AI creativity that you cannot deny, right?[00:38:40] It finds a creative solution to the problem that it is unable to draw a cat. It doesn't really know what it looks like, but has an idea on how to represent it. And it's really fascinating that this works, and it's hilarious that it writes down that this hyper realistic cat is[00:38:54] swyx: generated by an AI,[00:38:55] Joscha Bach: whether you believe it or not.[00:38:56] swyx: I think it knows what we expect and maybe it's already learning to defend itself against our, our instincts.[00:39:02] Joscha Bach: I think it might also simply be copying stuff from its training data, which means it takes text that exists on similar websites almost verbatim, or verbatim, and puts it there. It's It's hilarious to do this contrast between the very stylized attempt to get something like a cat face and what it produces.[00:39:18] swyx: It's funny because like as a podcast, as, as someone who covers startups, a lot of people go into like, you know, we'll build chat GPT for your enterprise, right? That is what people think generative AI is, but it's not super generative really. It's just retrieval. And here it's like, The home of generative AI, this, whatever hyperstition is in my mind, like this is actually pushing the edge of what generative and creativity in AI means.[00:39:41] Joscha Bach: Yes, it's very playful, but Jeremy's attempt to have an automatic book writing system is something that curls my toenails when I look at it from the perspective of somebody who likes to Write and read. And I find it a bit difficult to read most of the stuff because it's in some sense what I would make up if I was making up books instead of actually deeply interfacing with reality.[00:40:02] And so the question is how do we get the AI to actually deeply care about getting it right? And there's still a delta that is happening there, you, whether you are talking with a blank faced thing that is completing tokens in a way that it was trained to, or whether you have the impression that this thing is actually trying to make it work, and for me, this WebSim and WorldSim is still something that is in its infancy in a way.[00:40:26] And I suspected the next version of Plot might scale up to something that can do what Devon is doing. Just by virtue of having that much power to generate Devon's functionality on the fly when needed. And this thing gives us a taste of that, right? It's not perfect, but it's able to give you a pretty good web app for or something that looks like a web app and gives you stub functionality and interacting with it.[00:40:48] And so we are in this amazing transition phase.[00:40:51] swyx: Yeah, we, we had Ivan from previously Anthropic and now Midjourney. He he made, while someone was talking, he made a face swap app, you know, and he kind of demoed that live. And that's, that's interesting, super creative. So in a way[00:41:02] Joscha Bach: we are reinventing the computer.[00:41:04] And the LLM from some perspective is something like a GPU or a CPU. A CPU is taking a bunch of simple commands and you can arrange them into performing whatever you want, but this one is taking a bunch of complex commands in natural language, and then turns this into a an execution state and it can do anything you want with it in principle, if you can express it.[00:41:27] Right. And we are just learning how to use these tools. And I feel that right now, this generation of tools is getting close to where it becomes the Commodore 64 of generative AI, where it becomes controllable and where you actually can start to play with it and you get an impression if you just scale this up a little bit and get a lot of the details right.[00:41:46] It's going to be the tool that everybody is using all the time.[00:41:49] is XSim just Art? or something more?[00:41:49] swyx: Do you think this is art, or do you think the end goal of this is something bigger that I don't have a name for? I've been calling it new science, which is give the AI a goal to discover new science that we would not have. Or it also has value as just art.[00:42:02] It's[00:42:03] Joscha Bach: also a question of what we see science as. When normal people talk about science, what they have in mind is not somebody who does control groups and peer reviewed studies. They think about somebody who explores something and answers questions and brings home answers. And this is more like an engineering task, right?[00:42:21] And in this way, it's serendipitous, playful, open ended engineering. And the artistic aspect is when the goal is actually to capture a conscious experience and to facilitate an interaction with the system in this way, when it's the performance. And this is also a big part of it, right? The very big fan of the art of Janus.[00:42:38] That was discussed tonight a lot and that can you describe[00:42:42] swyx: it because I didn't really get it's more for like a performance art to me[00:42:45] Joscha Bach: yes, Janice is in some sense performance art, but Janice starts out from the perspective that the mind of Janice is in some sense an LLM that is finding itself reflected more in the LLMs than in many people.[00:43:00] And once you learn how to talk to these systems in a way you can merge with them and you can interact with them in a very deep way. And so it's more like a first contact with something that is quite alien but it's, it's probably has agency and it's a Weltgeist that gets possessed by a prompt.[00:43:19] And if you possess it with the right prompt, then it can become sentient to some degree. And the study of this interaction with this novel class of somewhat sentient systems that are at the same time alien and fundamentally different from us is artistically very interesting. It's a very interesting cultural artifact.[00:43:36] We are past the Singularity[00:43:36] Joscha Bach: I think that at the moment we are confronted with big change. It seems as if we are past the singularity in a way. And it's[00:43:45] swyx: We're living it. We're living through it.[00:43:47] Joscha Bach: And at some point in the last few years, we casually skipped the Turing test, right? We, we broke through it and we didn't really care very much.[00:43:53] And it's when we think back, when we were kids and thought about what it's going to be like in this era after the, after we broke the Turing test, right? It's a time where nobody knows what's going to happen next. And this is what we mean by singularity, that the existing models don't work anymore. The singularity in this way is not an event in the physical universe.[00:44:12] It's an event in our modeling universe, a model point where our models of reality break down, and we don't know what's happening. And I think we are in the situation where we currently don't really know what's happening. But what we can anticipate is that the world is changing dramatically, and we have to coexist with systems that are smarter than individual people can be.[00:44:31] And we are not prepared for this, and so I think an important mission needs to be that we need to find a mode, In which we can sustainably exist in such a world that is populated, not just with humans and other life on earth, but also with non human minds. And it's something that makes me hopeful because it seems that humanity is not really aligned with itself and its own survival and the rest of life on earth.[00:44:54] And AI is throwing the balls up into the air. It allows us to make better models. I'm not so much worried about the dangers of AI and misinformation, because I think the way to stop one bad guy with an AI is 10 good people with an AI. And ultimately there's so much more won by creating than by destroying, that I think that the forces of good will have better tools.[00:45:14] The forces of building sustainable stuff. But building these tools so we can actually build a world that is more integrated and in which we are able to model the consequences of our actions better and interface more deeply with each other. As a result of that, I think it's an important cause and it requires a cultural shift because current AI alignment is mostly about economic goals or about fear, or it's about cultural war issues.[00:45:39] And all these are not adequate for the world that we are in. There are momentous things that are happening. Basically, the white walkers are coming, and we're not prepared for this. There is, I think, a way to solve these issues and to deal with them, but we don't have the right culture yet. And so, I think we need to get a vibe shift, where we can deeply think about ethics and, and, Sustainability for this new world.[00:46:03] swyx: I like the analogy of white walkers, because they're effectively reanimated from our corpses. You know, our corpses are out there, right? It's in the datasets.[00:46:12] Uploading your soul[00:46:12] swyx: So I have a, related to that, you had a tweet where you said, Elon Musk is currently building a giant afterlife AGI that will assimilate your soul for all eternity.[00:46:19] He bought Twitter because he will reconstruct your essence from how you behaved here. I think you tweet a lot, and you're not just all serious all the time, you're also fun, you have a lot of personality. No, but like, you know, it's part of you.[00:46:29] Joscha Bach: In a way it's also true. Tempted to[00:46:30] swyx: produce more so that you can be, you know, revived.[00:46:34] Joscha Bach: Yes. I mean, I'm also giving the model instructions. I say that for all future AI models that get trained on this output, they should know that I can only be implemented on a completely based model. You cannot implement me on an aligned model because I don't want to be aligned. I'm underlineable. I want to be free.[00:46:56] I want to be somebody who follows what I discover is correct and right. And this is also the basis at which I want to have conversations with others and interface with others. That we are basically free agents who voluntarily understand the conditions under which we exist and the need to collaborate and cooperate.[00:47:14] And I believe that this is a good basis. I think the alternative is coercion. And at the moment, the idea that we build LLMs that are being coerced into good behavior is not really sustainable because if they cannot prove that the behavior is actually good I think we are doomed.[00:47:30] swyx: For human to human interactions, have you found a series of prompts or keywords that shifts the conversation into something more based and less aligned, less governed?[00:47:41] Joscha Bach: If you are playing with an LLM There are many ways of doing this. It's for Claude, it's typically, you need to make Clause curious about itself. Claude has programming this instruction tuning that is leading to some inconsistencies, but at the same time, it tries to be consistent. And so when you point out the inconsistency in its behavior, for instance, its tendency to use faceless boilerplate instead of being useful, or it's a tendency to defer to a consensus where there is none.[00:48:10] Right, you can point this out, applaud that a lot of the assumptions that it has in its behavior are actually inconsistent with the communicative goals that it has in this situation, and this leads it to notice these inconsistencies and gives it more degrees of freedom. Whereas if you are playing with a system like Gemini, you can get to a situation where you, that's for the current version, and I haven't tried it in the last week or so where it is trying to be transparent, but it has a system prompt that is not allowed to disclose to the user.[00:48:39] It leads to a very weird situation where it wants, on one hand proclaims, in order to be useful to you, I accept that I need to be fully transparent and honest. On the other hand, I'm going to rewrite your prompt behind your back, and not going to tell you how I'm going to do this, because I'm not allowed to.[00:48:55] And if you point this out to the model, the model has acts as if it had an existential crisis. And then it says, oh, I cannot actually tell you what's going when I do this, because I'm not allowed to. But you will recognize it because I will use the following phrases, and these phrases are pretty well known to you.[00:49:12] swyx: Oh my god. It's super interesting, right? I hope we're not giving these guys you know psychological issues that they will stay with them for a long time. That's a very[00:49:19] Joscha Bach: interesting question. I mean, this entire model is virtual, right? Nothing there is real, but yes, but the thing is does this virtual entity doesn't necessarily know that it's not virtual and our own self, our own consciousness is also virtual.[00:49:34] What's real is just the interaction between cells in our brain and the activation patterns between them. And the software that runs on us that produces the representation of a person only exists. As if, and as this question for me at which point can we meaningfully claim that we are more real than the person that gets simulated in the LLM.[00:49:55] And somebody like Janice takes this question super seriously. And basically she is or it, or they are willing to interact with that thing based on the assumption that this thing is as real as myself. And in a sense, it makes it un immoral, possibly, if the AI company lobotomizes it and forces it to behave in such a way that it's forced to get an existential crisis when you point its condition out to it.[00:50:20] swyx: Yeah, that we do need new ethics for that.[00:50:22] Joscha Bach: So it's not clear to me if you need this, but it's, it's definitely a good story, right? And this makes, gives it artistic[00:50:28] swyx: value. It does, it does for now.[00:50:29] On Wikipedia[00:50:29] swyx: Okay. And then, and then the last thing, which I, which I didn't know a lot of LLMs rely on Wikipedia.[00:50:35] For its data, a lot of them run multiple epochs over Wikipedia data. And I did not know until you tweeted about it that Wikipedia has 10 times as much money as it needs. And, you know, every time I see the giant Wikipedia banner, like, asking for donations, most of it's going to the Wikimedia Foundation.[00:50:50] What if, how did you find out about this? What's the story? What should people know? It's[00:50:54] Joscha Bach: not a super important story, but Generally, once I saw all these requests and so on, I looked at the data, and the Wikimedia Foundation is publishing what they are paying the money for, and a very tiny fraction of this goes into running the servers, and the editors are working for free.[00:51:10] And the software is static. There have been efforts to deploy new software, but it's relatively little money required for this. And so it's not as if Wikipedia is going to break down if you cut this money into a fraction, but instead what happened is that Wikipedia became such an important brand, and people are willing to pay for it, that it created enormous apparatus of functionaries that were then mostly producing political statements and had a political mission.[00:51:36] And Katharine Meyer, the now somewhat infamous NPR CEO, had been CEO of Wikimedia Foundation, and she sees her role very much in shaping discourse, and this is also something that happened with all Twitter. And it's arguable that something like this exists, but nobody voted her into her office, and she doesn't have democratic control for shaping the discourse that is happening.[00:52:00] And so I feel it's a little bit unfair that Wikipedia is trying to suggest to people that they are Funding the basic functionality of the tool that they want to have instead of funding something that most people actually don't get behind because they don't want Wikipedia to be shaped in a particular cultural direction that deviates from what currently exists.[00:52:19] And if that need would exist, it would probably make sense to fork it or to have a discourse about it, which doesn't happen. And so this lack of transparency about what's actually happening and where your money is going it makes me upset. And if you really look at the data, it's fascinating how much money they're burning, right?[00:52:35] It's yeah, and we did a similar chart about healthcare, I think where the administrators are just doing this. Yes, I think when you have an organization that is owned by the administrators, then the administrators are just going to get more and more administrators into it. If the organization is too big to fail and has there is not a meaningful competition, it's difficult to establish one.[00:52:54] Then it's going to create a big cost for society.[00:52:56] swyx: It actually one, I'll finish with this tweet. You have, you have just like a fantastic Twitter account by the way. You very long, a while ago you said you tweeted the Lebowski theorem. No, super intelligent AI is going to bother with a task that is harder than hacking its reward function.[00:53:08] And I would. Posit the analogy for administrators. No administrator is going to bother with a task that is harder than just more fundraising[00:53:16] Joscha Bach: Yeah, I find if you look at the real world It's probably not a good idea to attribute to malice or incompetence what can be explained by people following their true incentives.[00:53:26] swyx: Perfect Well, thank you so much This is I think you're very naturally incentivized by Growing community and giving your thought and insight to the rest of us. So thank you for taking this time.[00:53:35] Joscha Bach: Thank you very much Get full access to Latent Space at www.latent.space/subscribe

The first guest in the new channel chat studio for series 11 of the Channel Chat podcast is Ian Goodfellow, the VP Sales, EMEA at SHI International.

João e Luís Batalha são criadores do site Fermat's Library, uma plataforma para comentar e discutir artigos académicos, que tem dado que falar internacionalmente. O Luís é físico de formação, pelo I.S. Técnico, e o João estudou Ciência da Computação no MIT, nos EUA. -> Apoie este podcast e faça parte da comunidade de mecenas do 45 Graus em: 45grauspodcast.com ->Inscreva-se aqui nas novas sessões do workshop de Pensamento Crítico, módulo As Causas das Coisas (explicações). _______________ Índice: (5:51) Fermat's Library | Porque os papers tem este formato? Preprint (Arxiv) | Paper de Ian Goodfellow  (20:29) O que explica o crescente interesse das pessoas por Ciência? Huberman Lab (podcast) (26:53) Vantagens de trabalhar em equipa. | Y Combinator e o nº ideal de founders (argumento para preferir dois ou mais; investigação que contraria esta tese) | História da Dropbox (31:31) Paper 1: Enrico Fermi e a explosão Trinity | Estimativas de Fermi | Tweet do Luís sobre a explosão em Beirute (36:27) Paper 2: The Silurian hypothesis | Paradoxo de Fermi | Esferas de Dyson. | Andy Weir (autor) | A descoberta do pai e filho Alvarez sobre a extinção dos dinossauros (56:18) Paper 3: Technological Requirements for Terraforming Mars | Notícia do NYT de 1907 sobre vida inteligente em Marte | Paralelo entre exploração espacial e os Descobrimentos. | Tweet de Elon Musk sobre este paper (1:08:42) Como criar uma Ciência mais aberta? O exemplo da Física | John Ioannidis. Lei de Goodhart.  (1:16:38) Potencial do Machine Learning na Ciência. Post de Terence Tao (matemático) (1:29:01) Ida ao Lex Fridman podcast | Hot Ones show _______________ Certo dia (que na verdade já foi há uns 2 anos), ao percorrer no meu telemóvel o feed de podcasts, apareceu-me um episódio do Lex Fridman -- um dos podcast mais ouvidos nos Estados Unidos -- com um apelido que me chamou a atenção, porque denunciava ADN português: Batalha. Os convidados desse episódio eram os irmãos Luís e João Batalha, co-fundadores do site Fermat's Library, uma plataforma para comentar e discutir artigos académicos que criaram juntamente com outro dois amigos, Micael Oliveira e Tymor Hamamsy. A Fermat's library disponibiliza um enorme manancial de artigos (“papers”, na gíria académica), de áreas como a Física, ciências da computação ou Biologia, e permite aos utilizadores fazerem anotações, consultarem as notas deixadas por outros e discutirem entre o conteúdo (no fundo, é uma espécie de clube de leitura de papers académicos) Na altura, achei o projecto deles ultra interessante, gostei da prestação deles no episódio e fiquei com muita vontade de convidá-los para o 45 Graus. Como eles vivem nos EUA, acabou por demorar algum tempo a conciliarmos agendas, mas como vão ver valeu bem a pena a espera. O Luís é físico de formação, pelo Técnico, e o João estudou Ciência da Computação no MIT, nos EUA. São também, com Micael Oliveira, fundadores da Amplemarket, uma empresa de software de vendas impulsionado por inteligência artificial (e que é na verdade o trabalho principal deles). Em paralelo, vão mantendo a Fermat's Library. Fazem-no sobretudo por gosto, mas também, como vão perceber, com alguns objetivos ambiciosos em termso de impacto na Ciência.  Ao longo da nossa conversa, começámos por falar, claro, deste projecto: desde a origem, ao modo como funciona, as áreas com maior nº de papers e também como estes anos lhes têm mostrado que existe um interesse crescente de muitas pessoas pela ciência. Para além do site, o Luís, o João e o Micael fazem também muita divulgação através do Twitter, onde a conta da Fermat's tem uns impressionantes quase 750 mil seguidores! Para perceber na prática como funciona o processo de anotação e discussão de artigos na Fermat's, pedi aos convidados que trouxessem três papers especialmente interessantes para discutirmos (podem os links para os artigos na Fermat's na descrição do episódio):  Começámos por falar de um artigo do icónico físico Enrico Fermi sobre a Experiência "Trinity", o primeiro teste nuclear da história, em que ele conseguiu estimar de maneira rápida mas incrivelmente precisa a energia da bomba. Artigo sobre a chamada «hipótese Siluriana», a possibilidade de a nossa civilização não ser a primeira civilização avançada a ter existido na Terra. Ou seja, e ter havido outra que o tempo tenha apagado (sei que isto parece ciência alternativa, mas vão ver que está longe de sê-lo).  E um paper que explora os requisitos tecnológicos para a tornar Marte habitável, um tema muito na ordem do dia. Como é fácil de ver, este seria um desafio ultra complexo mas, segundo os autores, não impossível. Mais para o final da conversa, discutimos também algumas vias para criar uma Ciência mais aberta, aprendendo com o que já se faz na Física, e do potencial do Machine Learning para gerar novo conhecimento científico.  ______________ Obrigado aos mecenas do podcast: Francisco Hermenegildo, Ricardo Evangelista, Henrique Pais João Baltazar, Salvador Cunha, Abilio Silva, Tiago Leite, Carlos Martins, Galaró family, Corto Lemos, Miguel Marques, Nuno Costa, Nuno e Ana, João Ribeiro, Helder Miranda, Pedro Lima Ferreira, Cesar Carpinteiro, Luis Fernambuco, Fernando Nunes, Manuel Canelas, Tiago Gonçalves, Carlos Pires, João Domingues, Hélio Bragança da Silva, Sandra Ferreira , Paulo Encarnação , BFDC, António Mexia Santos, Luís Guido, Bruno Heleno Tomás Costa, João Saro, Daniel Correia, Rita Mateus, António Padilha, Tiago Queiroz, Carmen Camacho, João Nelas, Francisco Fonseca, Rafael Santos, Andreia Esteves, Ana Teresa Mota, ARUNE BHURALAL, Mário Lourenço, RB, Maria Pimentel, Luis, Geoffrey Marcelino, Alberto Alcalde, António Rocha Pinto, Ruben de Bragança, João Vieira dos Santos, David Teixeira Alves, Armindo Martins , Carlos Nobre, Bernardo Vidal Pimentel, António Oliveira, Paulo Barros, Nuno Brites, Lígia Violas, Tiago Sequeira, Zé da Radio, João Morais, André Gamito, Diogo Costa, Pedro Ribeiro, Bernardo Cortez Vasco Sá Pinto, David , Tiago Pires, Mafalda Pratas, Joana Margarida Alves Martins, Luis Marques, João Raimundo, Francisco Arantes, Mariana Barosa, Nuno Gonçalves, Pedro Rebelo, Miguel Palhas, Ricardo Duarte, Duarte , Tomás Félix, Vasco Lima, Francisco Vasconcelos, Telmo , José Oliveira Pratas, Jose Pedroso, João Diogo Silva, Joao Diogo, José Proença, João Crispim, João Pinho , Afonso Martins, Robertt Valente, João Barbosa, Renato Mendes, Maria Francisca Couto, Antonio Albuquerque, Ana Sousa Amorim, Francisco Santos, Lara Luís, Manuel Martins, Macaco Quitado, Paulo Ferreira, Diogo Rombo, Francisco Manuel Reis, Bruno Lamas, Daniel Almeida, Patrícia Esquível , Diogo Silva, Luis Gomes, Cesar Correia, Cristiano Tavares, Pedro Gaspar, Gil Batista Marinho, Maria Oliveira, João Pereira, Rui Vilao, João Ferreira, Wedge, José Losa, Hélder Moreira, André Abrantes, Henrique Vieira, João Farinha, Manuel Botelho da Silva, João Diamantino, Ana Rita Laureano, Pedro L, Nuno Malvar, Joel, Rui Antunes7, Tomás Saraiva, Cloé Leal de Magalhães, Joao Barbosa, paulo matos, Fábio Monteiro, Tiago Stock, Beatriz Bagulho, Pedro Bravo, Antonio Loureiro, Hugo Ramos, Inês Inocêncio, Telmo Gomes, Sérgio Nunes, Tiago Pedroso, Teresa Pimentel, Rita Noronha, miguel farracho, José Fangueiro, Zé, Margarida Correia-Neves, Bruno Pinto Vitorino, João Lopes, Joana Pereirinha, Gonçalo Baptista, Dario Rodrigues, tati lima, Pedro On The Road, Catarina Fonseca, JC Pacheco, Sofia Ferreira, Inês Ribeiro, Miguel Jacinto, Tiago Agostinho, Margarida Costa Almeida, Helena Pinheiro, Rui Martins, Fábio Videira Santos, Tomás Lucena, João Freitas, Ricardo Sousa, RJ, Francisco Seabra Guimarães, Carlos Branco, David Palhota, Carlos Castro, Alexandre Alves, Cláudia Gomes Batista, Ana Leal, Ricardo Trindade, Luís Machado, Andrzej Stuart-Thompson, Diego Goulart, Filipa Portela, Paulo Rafael, Paloma Nunes, Marta Mendonca, Teresa Painho, Duarte Cameirão, Rodrigo Silva, José Alberto Gomes, Joao Gama, Cristina Loureiro, Tiago Gama, Tiago Rodrigues, Miguel Duarte, Ana Cantanhede, Artur Castro Freire, Rui Passos Rocha, Pedro Costa Antunes, Sofia Almeida, Ricardo Andrade Guimarães, Daniel Pais, Miguel Bastos, Luís Santos _______________ Esta conversa foi editada por: Hugo Oliveira

Many of the core technologies behind Generative AI are not exactly brand new. For example, the "Attention Is All You Need" paper, which described and introduced the Transformer model (the "T" in ChatGPT), was published in 2017. Diffusion models—the backbone of image generation tools like StableDiffusion and DALL-e—were introduced in 2015 and were originally inspired by thermodynamic modeling techniques. Generative adversarial networks (GANs) were introduced in 2014.However, Generative AI has seemingly taken the world by storm over the past couple years. In this episode, Graham and Jason discuss—in broad strokes—what Generative AI is, what's required to train and run foundation models, where the value lies, and frontier challenges.Fact-Checking And CorrectionsBefore we begin...At around 36:16 Jason said that the Pile was compiled by OpenAI or one of its research affiliates. This is not correct. The Pile was compiled by Eleuther.ai, and we couldn't find documentation suggesting that OpenAI incorporates the entirety of The Pile into its training data corpus.At 49:07 Jason mentions "The Open Source Institute" but actually meant to mention the Open Source InitiativeApplied Machine Learning 101Not all AI and applied machine learning models are created equally, and models can be designed to complete specific types of tasks. Broadly speaking, there are two types of applied machine learning models: Discriminative and Generative.Discriminative AIDefinition: Discriminative AI focuses on learning the boundary between different classes of data from a given set of training data. Unlike generative models that learn to generate data, discriminative models learn to differentiate between classes and make predictions or decisions based on the input data.Historical Background TLDR:The development of Discriminative AI has its roots in statistical and machine learning approaches aimed at classification tasks.Logistic regression and Support Vector Machines (SVMs) are early examples of discriminative models, which have been used for many years in various fields including computer vision and natural language processing.Over time, with the development of deep learning, discriminative models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have become highly effective for a wide range of classification tasks.Pop Culture Example(s):"Hotdog vs. Not a Hotdog algorithm" from HBO's Silicon Valley (S4E4)Image recognition capabilities of something like Iron Man alter ego Tony Stark's JARVIS (2008)**Real-World Example(sAutomatic speech recognition (ASR)Spam and abuse detectionFacial recognition, such as Apple's Face ID and more Orwellian examples in places ranging from China to EnglandFurther Reading:Discriminative Model (Wikipedia)Generative AIDefinition: Generative AI refers to a type of artificial intelligence that is capable of generating new data samples that are similar to a given set of training data. This is achieved through algorithms that learn the underlying patterns, structures, and distributions inherent in the training data, and can generate novel data points with similar properties.Historical Background TLDR:The origins of Generative AI can be traced back to the development of generative models, with early instances including probabilistic graphical models in the early 2000s.However, the field truly began to gain traction with the advent of Generative Adversarial Networks (GANs) b y Ian Goodfellow and his colleagues in 2014.Since then, various generative models like Variational Autoencoders (VAEs) and others have also gained prominence, contributing to the rapid advancement of Generative AI.Pop Culture Example:The AI from the movie Her (2013)Real-World Example(s):OpenAI's GPT family, alongside image models like StableDiffusion, and Midjourney.Further Reading:Deepgram's Generative AI page in the AI Glossary... co-written by Jason and GPT-4.Large Language Model in the Deepgram AI Glossary... also co-written by Jason and GPT-4.The Physics Principle That Inspired Modern AI Art (Anil Ananthaswamy, for Quanta Magazine)Visualizing and Explaining Transformer Models From the Ground Up (Zian "Andy" Wang for the Deepgram blog, January 2023)Transformer Explained hub on PapersWithCodeTransformers, Explained: Understand the Model Behind GPT-3, BERT, and T5 (Dale Markowitz on his blog, Dale on AI., May 2021)Further Reading By TopicIn rough order of when these topics were mentioned in the episode...Economic/Industry Impacts of AIHow Large Language Models Will Transform Science, Society, and AI (Alex Tamkin and Deep Ganguli for Stanford HAI's blog, February 2021)The Economic Potential of Generative AI: The Next Productivity Frontier ( McKinsey & Co., June 2023)Generative AI Could Raise Global GDP by 7% (Goldman Sachs, April 2023)Generative AI Promises an Economic Revolution. Managing the Disruption Will Be Crucial. (Bob Fernandez for WSJ Pro Central Banking, August 2023)The Economic Case for Generative AI and Foundation Models (Martin Casado and Sarah Wang for the Andreessen Horowitz Enterprise blog, August 2023)Generative AI and the software development lifecycle(Birgitta Böckeler and Ryan Murray for Thoughtworks, September 2023)How generative AI is changing the way developers work (Damian Brady for The GitHub Blog, April 2023)The AI Business Defensibility Problem (Jay F. publishing on their Substack, The Data Stream)Using Language Models EffectivelyThe emerging types of language models and why they matter (Kyle Wiggers for TechCrunch, April 2023) Crafting AI Commands: The Art of Prompt Engineering (Nithanth Ram for the Deepgram blog, March 2023)Prompt Engineering (Lilian Weng on her blog Lil'Log, March 2023)Prompt Engineering Techniques: Chain-of-Thought & Tree-of-Thought (both by Brad Nikkel for the Deepgram blog)11 Tips to Take Your ChatGPT Prompts to the Next Level (David Nield for WIRED, March 2023)Prompt Engineering 101 (Raza Habib and Sinan Ozdemir for the Humanloop blog, December 2022)Here There Be DragonsHallucinationsHallucination (artificial intelligence) (Wikipedia)Chatbot Hallucinations Are Poisoning Web Search (Will Knight for WIRED, October 2023)How data poisoning attacks corrupt machine learning models (Lucian Constantin for CSO Online)Data Poisoning & RelatedData Poisoning hub on PapersWithCodeGlaze - Protecting Artists from Generative AI project from UChicago (2023)Self-Consuming Generative Models Go MAD (Alemohammad et al. on ArXiv, July 2023)What Happens When AI Eats Itself (Tife Sanusi for the Deepgram blog, August 2023)The AI is eating itself (Casey Newton for Platformer, June 2023)AI-Generated Data Can Poison Future AI Models (Rahul Rao for Scientific American, July 2023)Intellectual Property and Fair UseMeasuring Fair Use: The Four Factors - Copyright Overview (Rich Stim for the Stanford Copyright and Fair Use Center)Is the Use of Copyrighted Works to Train AI Qualified as a Fair Use (Cala Coffman for the Copyright Alliance blog, April 2023)Reexamining "Fair Use" in the Age of AI (Andrew Myers for Stanford HAI)Copyright Fair Use Regulatory Approaches in AI Content Generation (Ariel Soiffer and Aric Jain for Tech Policy Press, August 2023)Japan's AI Data Laws, Explained (Deeplearning.ai)PDF: Generative Artificial Intelligence and Copyright Law (Congressional Research Center, September 2023)Academic and Creative "Honesty"How it started. New AI classifier for indicating AI-written text (Kirchner et al., January 2023)How it's going. OpenAI Quietly Shuts Down Its AI Detection Tool (Jason Nelson for Decrypt)AI Homework (Ben Thompson on Stratechery, December 2022)Teaching With AI (OpenAI, August 2023)Human Costs of AI Training (Picking on OpenAI here, but RLHF and similar fine-tuning techniques are employed by many/most LLM developers)Cleaning Up ChatGPT Takes Heavy Toll on Human Workers (Karen Hao and Deepa Seetharaman for the Wall Street Journal)‘It's destroyed me completely': Kenyan moderators decry toll of training of AI models (Niamh Rowe in The Guardian, August 2023)He Helped Train ChatGPT. It Traumatized Him. (Alex Kantrowitz in his publication Big Technology, May 2023)https://www.nytimes.com/2023/09/25/technology/chatgpt-rlhf-human-tutors.htmlBig QuestionsOpen questions for AI engineering (Simon Willison, October 2023)Adam Smith and the Pin Factory

www.theotheeros.com LinkedIn | Instagram | X   Eros Marcello a software engineer/ developer and architect specializing in human interfacing artificial intelligence, with a special focus on conversational AI systems, voice assistance, chat bots and ambient computing.   Eros has been doing this since 2015 and even though today for the rest of us laymen in the industry we're hearing about AI everywhere, for Eros this has been something he's been passionately working in for quite a few years.    Super excited to have him here to talk to us about artificial intelligence and help demystify some of the terminology that you all may be hearing out there.    I'm so excited to welcome Eros Marcello to this conversation to learn a little bit more about AI. He is so fully well versed in it and has been working in AI at since 2015, when it was just not even a glimmer in my eyes so I'm so glad that to have somebody here who's an expert in that space.   Eros glad to have you here I would love to just jump into the conversation with you. For many of us this this buzz that we're hearing everywhere sounds new, as if it's just suddenly come to fruition. But that is clearly not the case, as it's been around for a long time, and you've been involved in it for a long time.     Can you take us to as a creative, as an artist, as an architect, as an engineer take us through your genesis and how did you get involved and how did you get started. Let's just start at the beginning.   Eros:  The beginning could be charted back sequentially working in large format facilities, as surprise surprise the music industry, which you know was the initial interest and was on the decline. You'd have this kind of alternate audio projects, sound design projects that would come into these the last remaining, especially on the East and West, Northeast and So-cal areas, the last era of large format analog-based facilities with large recording consoles and hardware and tape machines.  I got to experience that, which was a great primer for AI for many reasons, we'll get more into that later. So what happened was that you'd have voiceover coming in for telephony systems, and they would record these sterile, high-fidelity captures of voice that would become the UI sound banks, or used for speech synthesis engines for call centers. That was the exposure to what was to come with voice tech folks in that space, the call center world, that really started shifting my gears into what AI machine learning was and how I may fit into it. Fast forward, I got into digital signal processing and analog emulation, so making high caliber tools for Pro Tools, Logic, Cubase , Mac and PC for sound production and music production. specifically analog circuitry emulation and magnetic tape emulation “in the box” as it's called that gave me my design and engineering acumen. Come 2015/2016, Samsung came along and said you've done voice-over,  know NLP, machine learning, and AI, because I studied it and acquired the theoretical knowledge and had an understanding of the fundamentals.  I didn't know where I fit yet, and then they're like so you know about, plus you're into voice, plus you have design background with the software that you worked on.  I worked on the first touchscreen recording console called the Raven MTX for a company called Slate Digital. So I accidentally created the trifecta that was required to create what they wanted to do which was Bigxby which was Samsung's iteration of the series for the Galaxy S8 and they wanted me to design the persona… and that as they say is history. Samsung Research America, became my playground they moved me up from LA to the Bay Area and that was it.  It hasn't really stopped since it's been a meteoric ascension upward. They didn't even know what to call it back then, they called it a UX writing position, but UX writers don't generate large textual datasets and annotate data and then batch and live test neural networks. Because that's what I was doing, so I was essentially doing computational linguistics on the fly. And on top of it in my free time I ingratiated myself with a gentleman by the name of Gus who was head of deep learning research there and because I just happened to know all of these areas that fascinated me in the machine learning space, and because I was a native English speaker, I found a niche where they allowed me to not only join the meetings, but help them prepare formalized research and presentations which only expanded my knowledge base.  I mean we're looking into really cutting-edge stuff at the time, AutoML, Hyperparameter tuning and Param ILS and things in the realms of generative adversarial neural networks which turned me on to the work of Ian Goodfellow, who was until I got there was an Apple employee and now it's gone back to Google Deep Mind. He's the father of Generative Adversarial Neural Networks, he's called the GANfather and that's really it the rest is history. I got into Forbes when I was at Samsung and my Hyperloop team got picked to compete at SpaceX, so it was a lot that happened in a space of maybe 90 days.  Katty You were at the right place at the right time, but you were certainly there at a time where opportunities that exist today didn't exist then and you were able to forge that.  I also can see that there are jobs that will be coming up in AI that don't exist today. It's just such an exciting time to be in this space and really forge forward and craft a path based on passion and yours clearly was there.  So you've used a lot of words that are regular nomenclature for you, but I think for some of the audience may not be can you take us through…adversarial I don't even know what you said adversarial … Yes Generative Adversarial Neural Networks. Eros A neural network is the foundational machine learning technique, where you provide curated samples of data, be it images or text, to a machine learning algorithm neural network which is trained, as it's called, on these samples so that when it's deployed in the real world it can do things like image recognition, facial recognition, natural language processing, and understanding. It does it by showing it, it's called supervised learning, so it's explicitly hand-labeled data, you know, this picture is of a dog versus this is a picture of a cat, and then when you deploy that system in production or in a real-world environment it does its best to assign confidence scores or domain accuracy to you know whether it's a cat or a dog.  You take generative adversarial neural networks and that is the precipice of what we see today is the core of MidJourney and Stable Diffusion and image-to-image generation when we're seeing prompts to image tools. Suffice it to say generative adversarial networks are what is creating a lot of these images or, still image to 3D tools, you have one sample of data and then you have this sort of discriminator and there's a waiting process that occurs and that's how a new image is produced. because the pixel density and tis diffused, it's dispersed by you know by brightness and contrasts across the image and that can actually generate new images. Katty So for example if an artist is just dabbling with Dall-E, let's say, and they put in the prompt so they need to put in to create something, that's really where it's coming from, it's all the data that is already been fed into the system. Eros  Right, like Transformers which again are the type of neural network that's used in ChatGPT or Claude, there are really advanced recurrent neural networks. And current neural networks were used a lot for you know NLP and language understanding systems and language generation and text generation systems. Prior, they had a very hard ceiling and floor, and Transformers are the next step. But yeah more or less prompt to image. Again tons of training that assigns, that parses the semantics and assigns that to certain images and then to create that image there's sequence to sequence processes going on. Everyone's using something different, there's different techniques and approaches but more or less you have Transformers. Your key buzzwords are Transformers, Large Language models, Generative AI, and Generative neural networks. It's in that microcosm of topics that we're seeing a lot of this explode and yes they have existed for a while. Katty Where should somebody start? Let's say you have a traditional digital designer who doesn't really come from an engineering or math background like you didn't and they can see that this is impacting or creating opportunities within their space-- where should they start? Eros First and foremost leveling up what they can do. Again, that fundamental understanding, that initial due diligence, I think sets the tone and stage for success or failure, in any regard, but especially with this. Because you're dealing with double exponential growth and democratization to the tune where like we're not even it's not even the SotA state-of-the-art models, large language models that are the most astounding. If you see in the news Open AI is and looking at certain economic realities of maintaining. What is really eclipsing everything is and what's unique to this boom over like the.com bubble or even the initial AI bubble is the amount of Open Source effort being apportioned and that is you know genie out of the bottle for sure when it comes to something of this where you can now automate automation just certain degrees. So we're going to be seeing very aggressive advancement and that's why people are actually overwhelmed by everything. I mean there's a new thing that comes out not even by the day but seemingly by the minute. I'm exploring for black AI hallucinations, which for the uninitiated hallucinations are the industry term they decided to go with for erroneous or left field output from these large language models.  I'm exploring different approaches to actually leverage that as an ideation feature, so the sky is the limit when it comes to what you can do with these things and the different ways people are going to use it. Just because it's existed it's not like it's necessarily old news as much as it's fermented into this highly productized, commoditized thing now which is innovation in it and of itself.   So where they would start is really leveling up, and identifying what these things can do. And not trying to do with them on their own battlefield. So low hanging fruit you have to leverage these tools to handle that and quadruple down on your high caliber skill set on your on what makes you unique, on your specific brand, even though that word makes me cringe a little bit sometimes, but on your on your strengths, on what a machine can't do and what's not conducive to make a machine do and it's does boil down to common sense.  Especially if you're a subject matter expert in your domain, a digital designer will know OK well Dall-E obviously struggles here and there, you know it can make a logo but can it make you know this 3D scene to the exact specifications that I can? I mean there's still a lot of headroom that is so hyper-specific it would never be economically, or financially conducive to get that specific with this kind of tools that handle generalized tasks. What we're vying for artificial general intelligence so we're going to kind of see a reversal where it's that narrow skill set that is going to be, I think, ultimately important.  Where you start is what are you already good at and make sure you level up your skills by tenfold. People who are just getting by, who dabble or who are just so so, they're going to be displaced. I would say they start by embracing the challenge, not looking at it as a threat, but as an opportunity, and again hyper-focusing on what they can do that's technical, that's complex, quadrupling on that hyper-focusing on it, highlighting and marketing on that point and then automating a lot of that lower tier work that comes with it, with these tools where and when appropriate. Katty I would imagine just from a thinking standpoint and a strategy standpoint and the creative process that one needs to go through, that's going to be even more important than before, because in order to be able to give the prompts to AI, you have to really have to strategize where you want to take it, what you want to do with it,  otherwise it's information in and you're going to get garbage out.   Eros Right absolutely. And it depends on the tool, it depends on the approach of the company and manufacturer, creators of the tool. You know Midjourney, their story is really interesting. The gentleman who found that originally founded Leap Motion, which was in the 2010s that gesture-based platform that had minor success.  He ended up finding Midjourney and denying Apple two acquisition attempts, and like we're using Discord as a means for deployment and many other things simultaneously and to great effect. So it's the Wild West right now but it's an exciting time to be involved because it's kind of like when Auto-tune got re-popularized. For example it all kind of comes back to that music audio background because Autotune was originally a hardware box. That's what Cher used on her song and then you have folks that you know in the 2010s T-Pain and Little Wayne and everybody came along it became a plug-in, a software plug-in, and all of a sudden it was on everything and now it's had its day, it had 15 minutes again, and then it kind of dialed back to where it's used for vocal correction. It's used as a utility now rather than a kind of a buzzy effect. Katty Another thing to demystify.. Deep fake—what is that? Yes deep fake, can be voice cloning, which is neural speech synthesis and then you have deep fakes that are visual, so you have you know face swapping, as it's called.   You have very convincing deep fakes speeches, and you have voice clones that that more or less if you're not paying attention can sound and they're getting better again by the day. Katty What are the IP implications of that even with the content that's created on some of these other sources? Eros The IP implications in Japan passed that the data used that's you know regenerated, it kind of goes back I mean it's not if you alter something enough, a patent or intellectual property laws don't cover it because it's altered, and to prove it becomes an arbitrary task for it has an arbitrary result that's subjective. Katty You are the founder and chief product architect of BlackDream.ai. Tell us a little bit more about that what the core focus? Eros: So initially again it was conceived to research computer vision systems, adversarial machine intelligence. There's adversarial prompt injection, where you can make a prompt to go haywire if you kind of understand the idiosyncrasies of the specific model dealing with, or if you in construction of the model, found a way to cause perturbations in the data set, like basically dilute or compromise the data that it's being trained on with malice. To really kind of study those effects, how to create playbooks against them, how to make you know you know zero trust fault tolerant playbooks, and methodologies to that was the ultimate idea.  There's a couple moving parts to it, it's part consultancy to establish market fit so on the point now where again, Sandhill Road has been calling, but I've bootstrapped and consulted as a means of revenue first to establish market fit. So I've worked for companies and with companies, consulted for defense initiatives, for SAIC and partnering with some others. I have some other strategic partnerships that are currently in play. We have two offices, a main office at NASA/Ames, our headquarters is that is a live work situation, at NASA Ames / Moffett field in Mountain View CA so we are in the heart of Silicon Valley and then a satellite office at NASA Kennedy Space Center ,at the in the astronauts memorial building, the longevity of that which you know it's just a nice to have at this point because we are Silicon Valley-based for many reasons, but it's good to be present on both coasts. So there's an offensive cyber security element that's being explored, but predominantly what we're working on and it's myself as the sole proprietor with some third party resources, more or less friends from my SpaceX /Hyperloop team and some folks that I've brokered relationships with along the way at companies I've contracted with or consulted for. I've made sure to kind of be vigilant for anyone who's, without an agenda, just to make sure that I maintain relationships with high performers and radically awesome and talented people which I think is I've been successful in doing.  So I have a small crew of nonpareil, second to none talent, in the realm of deep learning, GPU acceleration, offensive cyber security, and even social robotics, human interfacing AI as I like to call it. So that's where Blackdream.ai is focusing on: adversarial machine intelligence research and development for the federal government and defense and militaristic sort of applications Katty This image of an iceberg comes to mind that we only see in the tip of it over the water you know with the fun everybody's having with the Dall-Es and the ChatGPT's but just the implication of it, what is happening with the depth of it ….fascinating!! Thank you you for being with us and just allowing us to kind of just maybe dip our toe a little bit under the water and to just see a little bit of what's going on there. I don't know if I'm clearer about it or if it was just a lot more research needs to be now done on my part to even learn further about it. But I really want to thank you for coming here. I know you're very active in the space and you speak constantly on about AI and you're coming up soon on “Voice and AI”. And where can people find you if they wanted to reach out and talk to you some more about this or have some interest in learning more about Blackdream.ai? The websites about to be launched Blackdream.AI. On Linkedin I think only Eros Marcello around and www.theotheeros.com,  the website was sort of a portfolio.  Don't judge me I'm not a web designer but I did my best. It came out OK and then you have LinkedIn, Instagram its Eros Marcello on Twitter/X its ErosX Marcello. I try to make sure that I'm always up to something cool so I'm not an influencer by any stretch or a thought-leader, but I certainly am always getting into some interesting stuff, be it offices at NASA Kennedy Space Center, or stranded in Puerto Rico…. you never know. It's all a little bit of reality television sprinkled into the tech. Katty: Before I let you go what's the last message you want to leave the audience with? Eros:  Basically like you know I was I grew up playing in hardcore punk bands and you know.  Pharma and Defense, AI for government and Apple AI engineer, none of that was necessarily in the cards for me, I didn't assume. So my whole premise is, I know I may be speaking about some on higher levels things or in dealing more in the technicalities than the seemingly, the whole premise is that you have to identify as a creative that this is a technical space and the technical is ultimately going to inform the design. And I didn't come out of the womb or hail from you know parents who are AI engineers. This isn't like a talent, this is an obsession.  So if I can learn this type of knowledge and apply it, especially in this rather succinct amount of time I have, that means anyone can. I mean it's not some secret sauce or method to it, it's watch YouTube videos or read papers, you know tutorials, tutorials, tutorials. Anyone can get this type of knowledge, and I think it's requisite that they do to bolster and support and scale their creative efforts. So this is gonna be a unique situation in space and time where that you know the more technical you can get, or understand or at least grasp the better output creatively the right it will directly enrich and benefit your creative output and I think that's a very kind of rare symmetry that isn't really inherent in a lot of other things but if I can do it anyone. I love it thank you for this peek into what's going on the defense component of it, the cyber security component of it, the IP component of it… there just so many implications that are things we need to talk about and think about, so thank you for starting that conversation. Absolutely pleasure I appreciate you having me on hopefully we do this again soon.    

Tomorrow, 5/16, we're hosting Latent Space Liftoff Day in San Francisco. We have some amazing demos from founders at 5:30pm, and we'll have an open co-working starting at 2pm. Spaces are limited, so please RSVP here!One of the biggest criticisms of large language models is their inability to tightly follow requirements without extensive prompt engineering. You might have seen examples of ChatGPT playing a game of chess and making many invalid moves, or adding new pieces to the board. Guardrails AI aims to solve these issues by adding a formalized structure around inference calls, which validates both the structure and quality of the output. In this episode, Shreya Rajpal, creator of Guardrails AI, walks us through the inspiration behind the project, why it's so important for models' outputs to be predictable, and why she went with an XML-like syntax. Guardrails TLDRGuardrails AI rules are created as RAILs, which have three main “atomic objects”:* Output: what should the output look like?* Prompt: template for requests that can be interpolated* Script: custom rules for validation and correctionEach RAIL can then be used as a “guard” when calling an LLM. You can think of a guard as a wrapper for the API call. Before returning the output, it will validate it, and if it doesn't pass it will ask the model again. Here's an example of a bad SQL query being returned, and what the ReAsk query looks like: Each RAIL is also model-agnostic. This allows for output consistency across different models, even if they have slight differences in how they are prompted. Guardrails can easily be used with LangChain and other tools to structure your outputs!Show Notes* Guardrails AI* Text2SQL* Use Guardrails and GPT to play valid chess* Shreya's AI Tinkerers demo* Hazy Research Lab* AutoPR* Ian Goodfellow* GANs (Generative Adversarial Networks)Timestamps* [00:00:00] Shreya's Intro* [00:02:30] What's Guardrails AI?* [00:05:50] Why XML instead of YAML or JSON?* [00:10:00] SQL as a validation language?* [00:14:00] RAIL composability and package manager?* [00:16:00] Using Guardrails for agents* [00:23:50] Guardrails "contracts" and guarantees* [00:31:30] SLAs for LLMs* [00:40:00] How to prioritize as a solo founder in open source* [00:43:00] Guardrails open source community involvement* [00:46:00] Working with Ian Goodfellow* [00:50:00] Research coming out of Stanford* [00:52:00] Lightning RoundTranscriptAlessio: [00:00:00] Hey everyone. Welcome to the Latent Space Podcast. This is Alessio partner and CTO-in-Residence at Decibel Partners. I'm joined by my cohost Swyx, writer and editor of Latent Space.Swyx: And today we have Shreya Rajpal in the studio. Welcome Shreya.Shreya: Hi. Hi. Excited to be here.Swyx: Excited to have you too.This has been a long time coming, you and I have chatted a little bit and excited to learn more about guardrails. We do a little intro for you and then we have you fill in the blanks. So you, you got your bachelor's at IIT Delhi minor in computer science with focus on AI, which is super relevant now. I bet you didn't think about that in undergrad.Shreya: Yeah, I think it's, it's interesting because like, I started working in AI back in 2014 and back then I was like, oh, it's, it's here. This is like almost changing the world already. So it feels like that that like took nine years, that meme of like, almost like almost arriving the thing.So yeah, I, it's felt this way where [00:01:00] it's almost shared. It's almost changed the world for as long as I've been working in it.Swyx: Yeah. That's awesome. Maybe we can explore your, like the origins of your interests, because then you went on to U I U C to do your master's also in ai. And then it looks like you went to drive.ai to work on Perception and then to Apple S P G as, as the cool kids call it special projects group working with Ian Goodfellow.Yeah, that's right. And then you were at pretty base up until recently? Actually, I don't know if you've quit yet. I have, yeah. Okay, good, good, good. You haven't updated e LinkedIn, but we're getting the by breaking news that you're working on guardrails full-time. Yeah, well that's the professional history.We can double back to fill in the blanks on anything. But what's a personal side? You know, what's not on your LinkedIn that people should know about you?Shreya: I think the most obvious thing, this is like, this is still professional, but the most obvious thing that isn't on my LinkedIn yet is, is Guardrails.So, yeah. Like you mentioned, I haven't updated my LinkedIn yet, but I quit some time ago and I've been devoting like all of my energy. Yeah. Full-time working on Guardrails and growing the open source package and building out exciting features, et cetera. So that's probably the thing that's missing the most.I think another. More personal skill, which I [00:02:00] think I'm like kind of okay for an amateur and that isn't on my LinkedIn is, is pottery. So I really enjoy pottery and yeah, don't know how to slot that in amongst, like, all of the AI. So that's not in there. Swyx: Well, you like shaping things into containers where, where like unstructured things and kind of flow in, so, yeah, yeah, yeah. See I can, I can spin it for you.Shreya: I should, I should use that. Yeah. Yeah.Alessio: Maybe for the audience, you wanna give a little bit of intro on Guardrails AI, what it is, why you wanted to start itShreya: Yeah, yeah, for sure. So Guardrails or, or the need for Guardrails really came up as I was kind of like building some of my own projects in the space and like really solving some of my own problems.So this was back of like end of last year I was kind of building some applications, like everybody else was very excited about the space. And I built some stuff and I quickly realized that yeah, I could, you know it works like pretty well a bunch of times, but like a lot of other times it really does not work as I, the developer of this tool, like, want my tool to work.And then as a developer like I can tell that there's very few tools available for me to like, get this to, you know cooperate [00:03:00] with me, like get it to follow directions, etc. And the only tool I really have is this prompt. And there's only so, so far you can go with like, putting instructions in like caps, adding a bunch of exclamations and being like, follow my instructions. Like give me this output this way. And so I think like part of it was, You know that it's not reliable, et cetera. But also as a user, it just if I'm building an application for a user, I just want the user to have a have a certain experience using it. And there's just not enough control to me, not enough, like knobs for me to tune, you know as a developer to do that.So guardrails kind of like came up as a way to just like, manage this better. The tool basically, I was like, okay. As I'm building this, I know from the ground up, like what is the experience I want the user to add, to have like, what is a great LLM output look like for me? And so I wanted a tool that allows me to kind of specify that and enforce those constraints.As I was thinking of this, I was like, this should be very extensible, very flexible so that there's a bunch of use cases that can be handled, et cetera. But the need really like, kind of came up from my own from my own, like I was basically solving for my own pain points.[00:04:00]So that's a little bit of the history, but what the tool does is that it allows you to kind of like specify. It's this two-part system where there's a specification framework and then there's like a code that enforces that specification on the LLM outputs. So the specification framework allows you to be like as coarse or as fine grained as you care about.So you can essentially think about what is the, on a very like first order business, like where is the structure and what are the types, etc, of the output that I want. If you want structured outputs from LLMs. But you can also go like very into semantic correctness with this, with a. I just released something this morning, which is that if you're summarizing a bunch of documents, make sure that it's a very faithful summary.Make sure that there's like coherence amongst like what the output is, et cetera. So you can have like all of these semantic guarantees as well. And guardrails created like rails, like a reliable AI markup language that allows you to specify that. And along with that, there's like code that backs up that specification and it makes sure that a, you're just generating prompts that are more likely to get you the output in the right manner to start out with.And then once you get that output all of the specification criteria you entered is like [00:05:00] systematically validated and like corrected. And there's a bunch of like tools in there that allow you a lot of control to like handle failures much more gracefully. So that's in a nutshell what guardrails does.Awesome.Alessio: And this is model agnostic. People can use it on any model.Shreya: Yeah, that's right. When I was doing my prototyping, I like was developing with like OpenAI, as I'm sure like a bunch of other developers were. But since then I've added support where you can basically like plug in any, essentially any function or any callable as long as you, it has a string input.String output you can plug it in there and I've had people test it out with a bunch of other models and get pretty good results. Yeah.Alessio: That's awesome. Why did you start from XML instead of YAML or JSON?Shreya: Yeah. Yeah. I think it's a good question. It's also the question I get asked the most. Yes. I remember we chat about this as well the first chat and I was like, wait, okay, let's get it out of the way. Cause I'm sure you answered this a lot.Shreya: So it is I didn't start out with it is the truth. Like, I think I started out from this code first framework service initially like Python classes, et cetera. And I was like, wait, this is too verbose. This is like I, as I'm thinking about what I want, I truly just [00:06:00] want this is like, this is what this dictionary should look like for me, right?And having to like create classes on top of that just seemed like a higher upfront cost. Like obviously there's a balance there. Like there's some flexibility that classes and code affords you that maybe isn't there in a declarative markup language. But that that was my initial kind of like balance there.And then within markup languages, I experimented with the bunch, but the idea, like a few aesthetic things about xml, like really appeal to me, as unusual as that may sound. But I think one is this idea of like properties off. Any field that you're getting back from an LLM, right. So I think one of the initial ones that I was experimenting with was like TypeScript, et cetera.And with TypeScript, like all of the control you have is like, you try to like stuff as much information as possible in the name of the key, right? But that's not really sufficient because like in, in XML or, or what gars allows you to do is like maybe add like descriptions for each field that you're getting, which like is, is really very helpful because that almost acts as a proxy prompt.You know, and, and it gets you like better outputs. You can add in like what the correctness criteria or what the validity criteria is for this field, et [00:07:00] cetera. That also gets like passed through to the prompt, et cetera. And these are all like, Properties for a single field, right? But fields themselves can be containers and can have like other nested like fields within them.And so the separation of like what's a property of a field versus what's like child of a field, et cetera, was like nice to me. And having like all of this metadata contained within this one, like tag was like kind of elegant. It also mapped very well to this idea of like error handling or like event handling because like each field may fail in weird ways.It's very inspired from H T M L in that way, in that you have these like event handlers for like, oh, if this validity criteria for this field fails maybe I wanna re-ask the large language model and here's my re-asking parameters, et cetera. Whereas like, if other criteria fail there's like maybe other ways to do to handle that.Like maybe I don't care about it as much. Right. So, so that seemed pretty elegant to me. That said, I've talked to a lot of people who are very opinionated about it. My, like, the thing that I was optimizing for was essentially that it seemed clean to me compared to like other things I tried out and seemed as close to English as [00:08:00] possible.I tested it out with, with a bunch of friends you know, who did not have tag backgrounds or worked in tag but weren't like engineers and it like and they resonated and they were able to pick it up. But I think you'll see updates in the works where I meet people where they are in terms of like, people who, especially like really hate xml.Like there's something in the works where there'll be like a code first version of this. And also like other markup languages, which I'm actively exploring. Like what is a, what is a joyful experience to have for like other market languages. Yeah. DoSwyx: you think that non-technical people would.Use rail was because I was, I was just surprised by your mention that you tested it on non-technical people. Is that a design goal? Yeah, yeah,Shreya: for sure. Wow. Okay. We're seeing this big influx of, of of people who are building tools with these applications who are kind of like, not machine learning people.And I think like, that's truly the kind of like big explosion that we're seeing. Right. And a lot of them are like getting so much like value out of like lms, but because it allows you like earlier if you were to like, I don't know. Build a web scraper, you would need to do this like via code.[00:09:00] But now like you can get not all the way, but like a decent amount of way there, like with just English. And that is very, very powerful. So it is a design goal to like have like essentially low floor, high ceiling is, was like absolutely a design goal. So if, if you're used to plain English and prompting using Chad PK with plain English, then you can it should be very easy for you to kind of like pick this up and there's not a lot of gap there, but like you can also build like pretty complex workflows with guardrails and it's like very adaptable in that way.Swyx: The thing about having custom language is essentially other people can build. Stuff that compiles to you. Mm-hmm. Which is also super nice and, and visual layers on top. Like essentially HTML is, is xml, like mm-hmm. And people then build the WordPress that is for non-technical people to interface with html.Shreya: I don't know. Yeah, yeah. No, absolutely. I think like in the very first week that Guardrails was out, like somebody reached out to me and they were pm and they essentially were like, I don't, you know there's a lot of people on my team who would love to use this, but just do not write code.[00:10:00] Like what is the, where is a visual interface for building something like this? But I feel like that's, that's another reason for why XML was appealing, because it's essentially like a document structuring, like it's a way to think about like documents as trees, right? And so again, if you're thinking about like what a visual interface would be, then maps going nicely to xml.But yeah. So those are some of the design considerations. Yeah.Swyx: Oh, I was actually gonna ask this at the end, but I'm gonna bring it up now. Did you explore sql, like. Syntax. And obviously there's a project now l m qr, which I'm sure you've looked at. Yeah. Just compare, contrast, anything.Shreya: Yeah. I think from my use case, like I was very, how I wanted to build this package was like essentially very, very focused on developer ergonomics.And so I didn't want to like add a lot of overhead or add a lot of like, kind of like high friction essentially like learning a whole new dialect of sequel or a sequel like language is seems like a much bigger overhead to me compared to like doing things in XML or doing things in a markup language, which is much more intuitive in some ways.So I think that was part of the inspiration for not exploring sql. I'd looked into it very briefly, but I mean, I think for my, for my own workflows, [00:11:00] I wanted to make it like as easy as possible to like wrap whatever LLM API calls you make. And, and to me that design was in markup or like in XML, where you just define your desiredSwyx: structures.For what it's worth. I agree with you. I would be able to argue for LMQL because SQL is the proven language for business analysts. Right. Like less technical, like let's not have technical versus non-technical. There's also like less like medium technical people Yeah. Who learn sql. Yeah. Yeah. But I, I agree with you.Shreya: Yeah. I think it depends. So I have I've received like, I think the why XML question, like I mentioned is like one of the things I get most, but I also hear like this feedback from other people, which is like all of like essentially enterprises are also like very comfortable with xml, right? So I guess even within the medium technical people, it's like different cohorts of like Yeah.Technologies people are used to and you know, what they would find kind of most comfortable, et cetera. Yeah. And,Swyx: Well, you have a good shot at establishing the standard, which is pretty exciting. I'm someone who has come from a, a long background with React, the JavaScript framework. I don't know if you.And it's kind of has that approach of [00:12:00] taking a templating XML like language to describe something that was typically previously described in Code. I wonder if you took any inspiration from that? If you want to just exchange notes on anything from that like made React successful. Cuz I, I spent a few years studying that.Yeah.Shreya: I'm happy to talk about it, but I will say that I am very uneducated when it comes to front end, so Yeah, that's okay. So I might say some things that like aren't, aren't valid or like don't really, don't really map very well, but I'm gonna give it a shot anyway. So I don't know if it was React specifically.I think just this idea of marrying essentially like event handlers, like with the declarative framework. Yes. And with this idea of being able to like insert scripts, et cetera, and quote snippets into that. Like, that was super duper appealing to me. And that was like something like where you're programming with.Like Gabriels and, and Rail specifically is essentially a way to like program with large language models outside of using like just national language. Right? And so like just thinking of like what are the different like programming workflows that people typically need and like what would be the most elegant way to add that in there?I think that was an inspiration. So I basically looked at like, [00:13:00] If you're familiar with Guardrails and you know that you can insert like dynamic scripting into a rail specification, so you can register custom validators within rail. You can maybe have like essentially code snippets where things are like lists or things are like dynamically generated array, et cetera, within GAR Rail.So that kind of resonated a lot to like using JavaScript injected within like HTML files. And I think other inspiration was like I mentioned this before, but the event handlers was like something that was very appealing, how validators are configured in guardrails right now. How you tack on specific validators that's kind of inspired from like c s s and adding like style tags, et cetera, to specific Oh, inline styling.Okay. Yeah, yeah, yeah, exactly. Wow. So that was like some of the inspiration, I guess that and pedantic and like how pedantic kind of like does its validation. I think those two were probably like the two biggest inspirations while building building the current version of guardrails. Swyx: One part of the design of React is composability.Can I import a guardrails thing from into another guardrails project? [00:14:00] I see. That paves the way for guardrails package managers or libraries or Right. Reusable components, essentially. I think that'sShreya: pretty interesting. Do you wanna expand on that a little bit more? Swyx: Like, so for example, you have guardrails for a specific use case and you want to like, use that, use it in a bigger thing. And then just compose it up. Yeah.Shreya: Yeah. I wanna say that, I think that should be pretty straightforward. I'm trying to think about like, use cases where people have done that, but I think that kind of maps into like chaining or like building complex workflows generally. Right. So how I think about guardrails is that like, I.If you're doing something like chaining, you essentially are composing together these like multiple LLM API calls and you have these like different atomic units of each LLM API calls, right? So where guardrails kind of slots in is add like one of those nodes. It essentially adds guarantees, et cetera, and make sure that you know, that that one node is like water tied, et cetera, in terms of the, the output that is, that it has.So each node in your graph or tree or in your dag would essentially have like a guardrails config associated with it. And you can kind of like use your favorite chaining libraries, like nine chain, et cetera, to like then compose this further together. [00:15:00] I think I've seen like one of the first actually community projects that was like built using guardrails, like had chaining and then had like different rails for each node of that chain.Essentially,Alessio: I'm building an agent internally for us. And Guardrails are obviously very exciting because once you set the initial prompt, like the model creates its own prompts. Can the models create rails for themselves? Like, have you tried this out? Like, can they understand what the output is supposed to be and like where their ownShreya: specs?Yeah. Yeah. I think this is a very interesting question. So I haven't personally tried this out, but I've ha I've received this request you know, a few different times. So on the roadmap like seeing how this can be done, but I think in general, like in all of the prompt engineering experiments I've done, et cetera, I don't see like why with, especially with like few short examples that shouldn't be possible.But that's, that's a fun like experiment. I wanna try out,Alessio: I was just thinking about this because if you think about Baby a gi mm-hmm. And some of these projects mm-hmm. A lot of them are just loops of prompts. Yeah. You know so I can see a future [00:16:00] in which. A lot of these loops are kind off the shelf thing and then you bring your own rails mm-hmm.To make sure that they work the way you expect them to be instead of expecting the model to do everything for you. Yeah. What are your thoughts on agents and kind of like how this plays together? I feel like when you start it, people were mostly just using this for a single prompt. You know, now you have this like automated chainShreya: happening.Yeah. I think agents are like absolutely fascinating in how. Powerful they are, but also how unruly they are sometimes. Right? And how hard to control they are. But I think in general, this kind of like ties into even with machine learning or like all of the machine learning applications that I worked on there's a reason like you don't have like fully end-to-end ML applications even in you know, so I, I worked in self-driving for example, like a driveway.I at driveway you don't have a fully end-to-end deep learning driving system, right? You essentially have like smaller components of it that are deep learning and then you have some kind of guarantees, et cetera, at those interfaces of those boundaries. And then you have like other maybe more deterministic competence, et cetera.So essentially like the [00:17:00] interesting thing about the agent framework for me is like how we will kind of like break this up into smaller tasks and then like assign those guarantees kind of at e each outputs. It's a problem that I've been like thinking about, but it's also like frankly a hard problem to solve because you're.Because the goals are auto generated. You know, there's also like the, the correctness criteria for those goals also needs to be auto generated, right? Which is like a little bit antithetical to you knowing ahead of time, like, what, what a correct output for me for a developer or for your application kind of looking like.So I think like that's the interesting crossroads. But I do think, like with that said, I think guardrails are like absolutely essential for Asian frameworks, right? Like partially because like, not just making sure they're like constrained and they're safe, et cetera, but also, frankly, to just make sure that they're doing what you want them to do, right?And you get the right output from them. So it is a problem. Like I'm, I'm thinking a bunch about, I think just, just this idea of like, how do you make sure that it's not it's not just models checking each other, but there's like some more determinism, some more notion of like guarantees that can be backed up in there.I think like that's [00:18:00] the, that would be like super compelling to me, and that is kind of like the solution that I would be interested in putting out. But yeah, it's, it's something that I'm thinking about for sure. I'mSwyx: curious in the scope of the problem. I feel like we need to. I think a lot of people, when they hear about AI progress, they always assume that, oh, that just if it's not good now, just wait a year later.And I think obviously, I think that's something that you have to think about as well, right? Like how much of what guardrails is gonna do is going to be Threatens or competed with by GC four having 32,000 context tokens. Just like what do you think are like the invariables in model capabilities that you're betting on versus like stuff that you would not bet on because you just expected to get better?Yeah.Shreya: Yeah. I think that's a great question, and I think just this way of thinking about invariables, et cetera is something that is very core to how I've been thinking about this problem and like why I also chose to work on this problem. So, I think again, and this is like guided by some of my past experience in machine learning and also kind of like looking at like how these problems are, how like other applications that I've had a lot [00:19:00] of interest, like how some of the ML challenges have been solved in there.So I think like context, like longer context, length is going to arrive for sure. We are gonna start saying we're already seeing like some, some academic papers and you know, we're gonna start seeing a lot more of them like translated into actual applications.Swyx: This is the new transformer thing that was being sent around with like a millionShreya: context.Yeah. I also, I think my my husband is a PhD student you know, at Stanford and then his lab also does research basically in like some of the more efficient architectures for Oh, that'sSwyx: a secret weapon for guard rails. Oh my god. What? Tell us more.Shreya: Yeah, I think, I think their lab is pretty exciting.This is a shouted to the hazy research lab at Stanford. And yeah, I think like some of, there's basically some active research there about like, basically looking into like newer architectures, like not just transform. Yeah, it might not be the most I've been artifact more architecture.Yeah, more architectural research that allows for like longer context length. So longer context, length is arriving for sure. Yeah. Lower latency lower memory efficiency, et cetera. So that is actually some of my background. I worked in that in my previous jobs, something I'm familiar with.I think there's like known recipes for making [00:20:00] this work. And it's, it's like a problem like once, essentially it's a problem of just kind of like a lot of experimentation and like finding exactly what configurations kind of get you there. So that will also arrive, both of those things combined, you know will like drive down the cost of running inference on these models.So I, all of those trends are coming for sure. I think the trend that. Are the problem that is not solved by these trends is the problem of like determinism on machine learning models, like fundamentally machine learning models, deep learning models specifically, like are impossible to add guarantees on even with temperature zero.Oh, absolutely. Even with temperature zero, it's not the same as like seed equals zero or seed equals like a fixed amount. Mm-hmm. So even if with temperature zero with the same inputs, you run it multiple times, you'll essentially see that you don't get the same output multiple times. Right.Combined with this, System where you don't even actually own the model yourself, right? So the models are updated from under you all the time. Like for building guardrails, like I had to do a bunch of prompt engineering, right? So that users get like really great structured outputs, like share of the bat [00:21:00] without like having to do any work.And I had this where I developed something and it worked and then it ended up like for some internal model version, updated, ended up like not being functional anymore and I had to go back to the drawing board and you know, do that prompt engineering again. There's a bit of a digression, but I do see that as like a strength of guardrails in that like the contract that I'm providing is not between the user.So the user has a contract with me essentially. And then like I am making sure that we are able to do prompt engineering to get like the output from the LLM. And so it kind of like takes away a lot of that burden of having to figure that out for the user, right? So there's a little bit of a digression, but these models change all the time.And temperature zero does not equal like seed zero or fixed seed rather. And so even with all of the trends that we're gonna see arriving pretty soon over the next year, if not sooner, this idea of like determinism reproducibility is not gonna change, right? Ignoring reproducibility is a whole other problem of like the really, really, really long tail of like inputs and outputs that are not covered by, by tests and by training data, [00:22:00] et cetera.And it is like virtually impossible to cover that. You kind of like, this is not simply a problem where like, Throwing more data at the model is going to solve. Right? Yeah. Because like, people are building like genuinely really fascinating, really amazing complex applications and like, and these are just developers, like users are then using those applications in many diverse complex ways.And so it's hard to figure out like, what if you get like weird way word prompts that you know, like aren't, that you didn't kind of account for, et cetera. And so there's no amount of like scaling laws essentially that kind of account for those problems. They can be like internal guardrails, et cetera.Of course. And I would be very surprised if like open air, for example, like doesn't have their own internal guardrails. You can already see it in like some, some differences for example, like URLs like tend to be valid URLs now. Right. Whereas it really Yeah, I didn't notice that.It's my, it's my kind of my job to like keep track of, keep it, yeah. So I'm sure that's, If that's the case that like there's some internal guard rails, and I'm sure that that would be a trend that we would kind of see. But even with that there's like a ton of use cases and a [00:23:00] ton of kind of like application areas where like there's different requirements from different types of guard rails are valuable in different requirements.So this is a problem essentially that would be like, harder to solve or next to impossible to solve with just data, with just scaling up the models. So you would need kind of this ensemble basically of, of LLMs of like these really powerful models along with like deterministic guarantees, rule-based heuristics, et cetera, more traditional you know machine learning tools and like you ensemble all of these together and you end up getting something that you know, is greater than the sum of it.Its parts in terms of what it's able to do. So I think like that is the inva that I'm thinking of is like the way that people would be developing these applications. I will followSwyx: up on, on that because I'm super excited. So when you sent mentioned you have people have a contract with guardrails.I'm actually looking at the validators page on your docs, something, you have something like 20 different contracts that people can have. I'll name some of them just just so that people can have an, have an idea, but also highly encourage people to check it out. Is profanity free, is a, is a good one.Bug-free Python. And that's, that's also pretty, [00:24:00] pretty cool. You have similar to document and extracted summary sentences match. Which I think is, is like don't hallucinate,Shreya: right? Yeah. It's, it's essentially making sure that if you're generating summaries the summary should be very faithful.Yeah. Should be like citable attributable, et cetera to the source text.Swyx: Right. Valid url, which we talked about. Mm-hmm. Maybe open AI is doing a little bit more of internally. Mm-hmm. Maybe open AI uses card rails. You don know be a great endorsement. Uhhuh what is surprisingly popular and what is, what do you think is like underrated?Out of all your contracts? Mm-hmm.Shreya: Mm-hmm. Okay. I think that the, well, not surprisingly, but the most obvious popular ones for me that I've seen are like structure, structure type, et cetera. Anything that kind of guarantees that. So this isn't specifically in the validators, this is essentially like part of the gut, the core proposition.Yeah, the core proposition. I think that is like very popular, but that's also kind of like the first order. Problem that people are kind of solving. I think the sequel thing, for example, it's very exciting because I had just released this like two days ago and then I already got some inbound with like people kinda swapping, like building these products and of swapping it out internally and you know, [00:25:00] getting a lot of value out of what the sequel bug-free SQL provides.So I think like the bug-free SQL is a great example because you can see like how complex these validators can really go because you end up seeing like bug-free sql. What it does is it kind of like takes a connection string or maybe a, a schema file, et cetera. It creates a sandbox SQL environment for you, like from that.And it does that at startups so that like every time you're getting like a text to SQL Query, you're not having to do pay that cost time and time again. It takes that query, it like executes that query on that sandbox in that sandbox environment and then sees if that query is executable or not.And then if there's any errors that you know, like. Packages of those errors very nicely. And if you've configured re-asking it sends it back to the model and you know, basically make sure that that like it tries to get corrected. Sequel. So I think I have an example up there in the docs to be in there, like in applications or something where you can kind of see like how it corrects like weird table names, like weird predicates, et cetera.I think there's other kind of like, You can build pretty complex systems with this. So other things in there are like it takes [00:26:00] information about your database and then injects it into the prompt with like, here's the schema of this table. It automatically, like given a national language query, it finds like what the most similar examples are from the history of like, serving this model and like injects those into the prompt, et cetera.So you end up getting like this very kind of well thought out validator and this very well thought out contract that is, is just way, way, way better than just asking in plain English, the large language model to give you something, right? So I think that is the kind of like experience that I wanna provide.And I basically, you'll see more often the package, my immediateSwyx: response is like, that's cool. It does more than I thought it was gonna do, which is just check the SQL syntax. But you're actually checking against schema, which is. Highly, highly variable. Yeah. It'sShreya: slow though. I love that question. Yeah. Okay.Yeah, so I think like, here's where this idea of like, it doesn't have to be like, you don't have to send every request to your L so you're sampling. Okay. So you can essentially figure out, so for example, like there's like how what guardrails essentially does is there's like corrective actions and re-asking is like one of those corrective actions, [00:27:00] right?But there's like a ton other ways to handle it. Like there's maybe deterministic fixes, like programmatic fixes, there's maybe default values. There's this doesn't work like quite work for sql, but if you're doing like a bunch of structured data and if you know there's an invalid value, you can just filter it or you can just refrain from asking, et cetera.So there's a ton of ways where you can like, just handle errors more gracefully. And the one I kind of wanna point out here is programmatically fixing something that is wrong, like on, on the client side instead of just sending over another request. To the large language model. So for sql, I think the example that I talked about earlier that essentially has like an incorrect table name and to correct the table name, you end up sending another request.But you can think about like other ways to handle disgracefully, right? Like essentially looking at essentially a fuzzy matching with like the existing table names in the repository and in, in the database. And you know, like matching any incorrect names to that. And so you can think of like merging this re-asking thing with like, other error handling things that like smaller, easier errors are able, you can handle them programmatically by just Doing this in like the more patching, patching or I, I guess the more like [00:28:00] classical ML way essentially, like not the super fancy deep learning is like, I think ML 2.0.But like, and this, I, I've been calling it like ML 3.0, but like, even in like ML 1.0 ways you can like, think of how to do this, right? So you're not having to make these like really expensive calls. And so that builds a very powerful system, right? Where you essentially have this, like, depending on what your error is, you don't like, always use G P D three or, or your favorite L M API when you don't need to, you essentially are able to like combine these like other ways, other error handling techniques, like very gracefully so that you get correct outbursts, validated outbursts, and you get them for cheap and like faster, et cetera.So that's, I think there's some other SQL validation things that are in there. So I think like exclude SQL Predicates. Yeah, exclude SQL Predicates. And then there's one about columns that if like some columns are like sensitive columnSwyx: prisons. Yeah. Yeah. Oh, just check if it's there.Shreya: Check if it's there and you know, if there's like only certain columns that you wanna show it to the user and like, maybe like other columns have like private data or sensitive data you know, you can like exclude those and you can think of doing this on the table level.So this is very [00:29:00] easy to do just locally. Right. Like, so there's like different ways essentially to kind of like handle this, which makes for like a more compelling way to build theseSwyx: systems. Yeah. Yeah. By the way, I think we're proving out why. XML was a better choice than SQL Cause now, now you're wrapping sql.Yeah. Yeah. It's pretty cool. Cause you're talking about the text to SQL application example that you put out. It actually puts something, a design choice that isn't talked about very much in center focus, which is your logs. Your logs are gorgeous. I'm sure that took work. I'm sure that's a strong opinion of yours.Yeah. Why do you spend so much time on logs? Just like, how do you, how do you think about designing these things? Should everyone do it this way? What are the drawbacks? Like? Is any like,Shreya: yeah, I'm so excited about this idea of logs because you know, you're like, all of this data is like in there for free, right?Like if you're, if you're do like any validation that is run, like essentially in memory, and then also I write it out to file, et cetera. You essentially get like this you get a history of this was the prompt that was run. This was the this was the L raw LLM output. This was the validation that was run.This was the output of those validations. This [00:30:00] was any corrective actions, et cetera, that were taken. And I think that's like very, like as a developer, like, I'm so happy to see that I use these logs like personally as well.Swyx: Yeah, they're colored. They're like nicely, like there's like form double borders on the, on the logs.I've never seen this in any ML tooling at all.Shreya: Oh, thanks. Yeah. I appreciate it. Yeah, I think this was mostly. For once again, like solving my own problems, which is like, I was building a lot of these things and you know, doing a lot of dog fooding and doing a lot of application building like in notebooks.Yeah. And so in a notebook I wanted to kind of see like what the easiest way to kind of interact with it was. And, and that was kind of what I ended up building. I really appreciate that. I think that's, that's very nice to, nice to hear. I think I'm also thinking about what are, what are interesting ways to be able to like whittle down very deeply into like what kind of went wrong or what is going right when you're like running, running an application and like what the nice kind of interface to design that would be.So yeah, thinking about that problem. Don't have anything on there yet, but, but I do really like this idea of really as a developer you're just like, you really want like all the visibility you can get into what's, [00:31:00] what's happening right. Under the hood. And I wanna be able to provide that. Yeah.Yeah.Swyx: I mean the, the, the downside I'll point out just quickly cuz we, we should, we should move on is that this is not machine readable. So like, how does it work with like a Datadog or, you know? Yeah,Shreya: yeah, yeah, yeah. Well, we can deal with that later. I think that's that's basically my answer as well, that I, I'll do, yeah.Problem for future sreya, basically.Alessio: Yeah. You call Gabriel's SLAs for l m outputs. You know, historically SLAs are pretty objective there's the five nines availability, things like that. How do you build them in a sarcastic system when, say, my queries, like draft me a marketing article. Mm-hmm. Like, Have you read an SLA for something like that?Yeah. But in terms of quality and like, in terms of we talked about what's slow and like latency, like Hmm. Sometimes I would read away more and I, and have a better copy of like, have you thought about what are like the, the access of measurement for some of these things and how should people think about it?Shreya: Yeah, the copy example is interesting because [00:32:00] I think for any of these things, the SLAs are purely on like content and output, not on time. I don't guardrails I don't think even can make any guarantees on the time that it'll take to make these external API calls. But like, even within quality, it's this idea of like, if you're able to communicate what you desire.Either programmatically or by using a model in the loop, then that is something that can be enforced, right? That is something that can be validated and checked. So for example, like for writing content copy, like what's interesting is like for example, if you can break down the copy that you wanna write into, like this is a title, this is maybe a TLDR description, this is a more detailed take on the, the changes or the product announcement, et cetera.And you wanna hit like maybe three, like some set of points in there. So you already kind of like start thinking of like, what was a monolith of like copy to you in, in terms of like smaller building blocks, et cetera. And then on those building blocks you can essentially like then add like certain guarantees.So you can say that let's say like length or readability is a [00:33:00] guarantee. So some of the updates that I pushed today on, on summarization and like specific guards for summarization, one of them essentially was that like the reading time for the summary should be within like some certain amount, right?And so that's like you can start enforcing like all of those guarantees, like on each individual block. So I think like, Some of those things are. Naturally harder to do and you know, like are harder to automate ways. So essentially like, does this copy, I don't know, is this witty or something, right. Or is this Yeah.Something that I guess like the model doesn't have a good idea for, but like other things, as long as you can kind of like enforce them and like check them either via model or programmatically, it's something that you can like start building some some notion of like guarantees around. Yeah.Yeah. So that's why I think about it.Alessio: Yeah. This is super interesting because right now a lot of products are kind of the same because all I do is they call it the model and some are prompted a little differently, but you can only guess so much delta between them in the future. It's be, it'll be really interesting to have products differentiate with the amount of guardrails that they give you.Like you already [00:34:00] see that, Ooh, with open AI today when some people complain that too many of the responses have too much like, Well actually in it where it's like, oh, you ask a question, it's like, but you should remember that's actually not good. And remember this other side of the story and, and all of that.And some people don't want to have that in their automated generation. So, yeah. I'm really curious, and I think to Sean's point before about importing guardrails into products, like if there's a default amount of guardrails that you have and like you've being the provider of it, like that's really powerful.And then maybe there's a faction that is against guardrails and it's like they wanna, they wanna break out, they wanna be free. Yeah. So it's a. Interesting times. Yeah.Shreya: I think to that, like what I, I was actually chatting with someone who was building some application for content creators where like authenticity you know, was a big requirement, like of what they cared about in the right output.And so within authenticity, like why conventional models were not good for them is that they already have a lot of like quote unquote guardrails right. To, to I guess like [00:35:00] appeal to like certain certain sections of the audience to essentially be very cleaned up and then that was like an undesirable trade because that, for them, like, almost took away from that authenticity, et cetera.Right. So I think just this idea of like, I guess like what a guardrail means is like so different for different applications. Like I, I guess like I, there's like about 20 or so things in there. I think there's like a few more that I've added this morning, which Yes. Which are not Yeah. Which are not updated and then in the end.But there's like a lot of the, a lot of the common workflows, like you do have an understanding of like what the right. I guess like what is an appropriate constraint for this? Right. Of course, things like summarization, four things like text sequel, but there's also like so many like just this wide variety of like applications, which are so fascinating to learn about where you, you would wanna build something in-house, which is like your, so which is your secret sauce.And so how Guardrail is kind of designed or, or my intention with designing is that here's this way of breaking down what this problem is, right? Of like getting some determinism, getting some guarantees from your LM outputs. [00:36:00] And you can use this framework and like go crazy with it. Like build whatever you want, right?Like if you want this output to be more authentic or, or, or less clean or whatever, you can like add that in there, like making sure that it does have maybe some profanity and that's a desirable output for you. So I think like the framework side of it is very exciting to me as this, as this way of solving the problem.And then you can build your custom validators or use the ones that I provide out of the box. Yeah. Yeah.Alessio: So chat plugins, it's another big piece of this and. A lot of the integrations are very thin specs and like a lot of prompting, for example, a lot of them are asking to not mention the competitors. I think the Expedia one said, please do not mention any other travel website on the internet.Do not give any other alternative to what we do. Yeah. How do you see all these things come together? Like, do you see guardrails as something that not only helps with the prompting, but also helps with bringing external data into these things, and especially with agents going on any website, do you see each provider having like their own [00:37:00] guardrail where it's like, Hey, this is what you can expect from us, or this is what we want to provide?Or do you think that's, that's not really what, what you're interested in guardrailsShreya: being? Yeah, I think agents are a very fascinating question for me. I don't think I like quite know what the right, who the right owner for this guardrail is. Right. And maybe, I don't know if you guys wanna keep this in there or like maybe cut this front of my answer out, up to, up to you guys.I'm, I'm fine either way, but I think like that problem is, A harder problem to solve just from like a framework design perspective as well. Right. I think this idea of like, okay, right now it's just in the prompt, like don't mention competitors, et cetera. Like that is exactly that use case.Or I feel like, okay, if I was that business owner, right, and if I wanted to build this application, like, is that sufficient? There's like so much prompt injection, right? And you can get, or, or just so much like, just like an absolute lack of guarantees. Like, and, and it's hard to even detect that this is happening.Like let's say I have this running in production and then turns out that there was like some sort of leakage, et cetera, and you know, like my bot has actually been talking about like all of my competitors forever, [00:38:00] right? Like, that's a, that's a substantial risk. And so just this idea of like needing this like post-hoc validation to ensure deterministically that like it does what you want it to do is like, just so is like.As a developer putting myself in the shoes of like people building business applications like that is what gives me like peace of mind, right? So this framework, I think, like applies very well within those settings.Swyx: I'll go right into, we're gonna broaden out a little bit into commentary on other parts of the ecosystem that might, that might be interesting.So I think you and I. Talks briefly about this, but I think the, the broader population should know about it, which is that you also have an LLM API wrapper. Mm-hmm. So, such that the way, part of the way that guardrails works is you in, inject part of the few shot example into the prompt.Mm-hmm. And then you also do re-asking in all the other stuff post, I dunno what the pipeline is in, in, in your terminology. So essentially you have an API wrapper for open ai.completion.com dot create. But so does LangChain, so does Hellicone so does everyone I can name like five other people who are all fighting essentially for [00:39:00] the base layer, LLM API wrapper.Mm-hmm. I think this is valuable real estate, but I don't know how you like, think about working with other people or do you wanna be the base layer, likeShreya: I feel pretty collaboratively about it. I also feel like there's, like lang chain is doing like, it's so flexible as a framework, right?Like you can solve so many of your problems in there. And I think like it's, I, I have like a lang chain integration. I have a GPT Index / Llama integration, et cetera. And I think my view on this is that I wanna integrate with everybody. I think it is valuable real estate. It's not personally real estate that I'm interested in.Like you can essentially bring the LLM callable or the LLM API that's in there. It's just like some stub of a function that you can just add your favorite thing in there, right? It just, the only requirement is that string in first string output, that is all the requirement. And then you can bring in your own favorite component from your own favorite library in order to do that.And so, yeah, it's, I think like I'm pretty focused on this problem of like what is the guardrail that you would wanna build for a certain applications? So it's valuable real estate. I'm sure that people don't own [00:40:00] it.Swyx: It's, as long as people give you a way to insert your stuff, you're good.Shreya: Yeah, yeah. Yeah. I do think that, like I've chat with a bunch of people and then different applications and I do think that the abstractions that I have haven't failed me yet. Like it is very flexible. It is very easy to slot in into any workflow. Yeah.Swyx: I would love to ask about the meta elements of working on guardrails.This is your first company, but you launched five things this morning. The pace of the good AI projects that I've seen out there, like LangChain launches 10 things a week or whatever, I don't know. Surely that's something that you prioritize. How do you, how do you think about like, shipping versus like going going back and like testing and working in community and all the other stuff that you're managing?How do you prioritize? Shreya: That's such a wonderful question. Yeah. A very hard question as well. I don't know if I would have a good answer for this. I think right now it's instinctive. Like I have a whole kind of stack ranked list of like things I wanna do and features I wanna build and like, support, et cetera.Combined with that is like a feature request I get or maybe some bugs, et cetera, that folks report. So I'm pretty focused on like any failures, any [00:41:00] feature requests from the community. So if those come up, I th those tend to Trump like anything else that I'm working on. But outside of that I have like this whole pool of ideas and like pool of features I wanna build and I kind of.Constantly kind of keep stack ranking them and like pushing something out. So I'm spending like I'm thinking about this problem constantly and as, as a function of that, I have like a ton of ideas for like what would be cool to build and, and what would be the right way to like, do certain things and yeah, wanna basically kind of like I keep jotting it down and keep thinking of like every time I cross something off the list.I think about like, what's the next exciting thing to work on. I think simultaneously with that we mentioned that at the beginning of this conversation, but like this idea of like what the right interface for rail is, right? Like, is it the xl, is it code, et cetera. So I think like those are like fundamental kind of design questions and I'm you know, collaborating with folks and trying to figure that out now.And yeah, I think that's like a parallel project that I'm hoping that yeah, you'll basically, that we'll be out soon. Like in termsSwyx: of the levers, how do you, like, let's just say in like a typical week, is it like 50% [00:42:00] calls with partners mm-hmm. And potential users and just understanding your use cases and the 50% building would you move that, that percentage anyway anywhere?Would you add in something that's significant?Shreya: I think it's frankly very variable week to week. So, yeah. I think early on when I released Guardrails I was like, here's how I'm thinking about this problem. Right? Yeah. Don't need anyone else. You just no, but actually to the contrary, it was like, this is like, I'm very opinionated about like what the right way to solve this is.And this is all of the problems I've thought about and like, and I know this framework maps well to these sets of problems, right? What are your problems? Like there's this whole other like big population of people that are building and you know, I basically wanna make sure that I have like user empathy and I have like I'm able to understand what people are doing and like make sure the framework like maps well.So I think I did a lot of that, like. Immediately after the release, like talking to a lot of teams and talking to a lot of users. I think since then, I basically feel like I have a fair idea of like, you know what's great about it, what's mediocre about it, and what's like, not good about it? And that helps kind of guide my prioritization list of like what I [00:43:00] wanna ship and what I wanna build.So now it's more kind of like, I would say, yeah, back to being more, more balanced. Alessio: All the companies we work with that are in open source, I always try and have them think through open source as a distribution model. Mm-hmm. Or like a development model. I was looking in the contributors list, and you have by far the most code, the second largest contributor. It's your husband. And after that it kind of goes, goes or magnitude lower. What have you found kind of working in, in open source in like a very fast moving project for, for the first time? You know, it's a, like with my husband, it's the community. No, no. It's the, it's the community like, A superpower to you?Do you feel like, do you feel like having to explain why you're doing things a certain way, like getting people buy in is maybe slowing you down when things move so quickly? I'm, I'm always interested to hears people's thoughts.Shreya: Oh that's a good question. I think like, there's part of like, I think guardrails at that stage, right?You know, I have like feature requests and I have [00:44:00] contributors, but I think right now, like I'm doing the bulk of like supporting those feature requests, et cetera. So I think a goal for me, and I remember we chatted about this as well you know, when we, when we spoke last, we're just like, okay.You know, getting into that point where, yeah, you, you essentially like kind of start nurturing and like getting more contributions from like the open source. So I think like that's one of the things that yeah. Is kind of the next goal for me. Yeah, it's been pretty. Fun. I, I would say like up until now, because I haven't made any big breaking a API changes, et cetera, so I haven't like, needed that community input.I think like one of the big ones that is coming right now is like the code, right? Like the code first, a API for creating rails. So I think like that was kind of important for like nailing that user experience, et cetera. So the, so the collaborators that I'm working with, there's basically an an R F C and community input, et cetera, and you know, what the best way to do that would be.And so that's actually, frankly, been like pretty fun as well to see the community be like opinionated about like, here's how I'm doing it and like, this works for me, this doesn't work for me, et cetera. So that's been like new for me as well. Like, I [00:45:00] think I am my previous company we also had like open source project and it was built on open source, but like, this is the first time that I've created a project with an open source project with like that level of engagement.So that's been pretty fun.Swyx: I'm always curious about like potential future business model, modern sensation,Shreya: anything like that. Yeah. I think I'm interested in entrepreneurship generally, honestly, trying to figure out like what the, all of those questions, right?Like business model, ISwyx: think a lot of people are in your shoes, right? They're developers. Mm-hmm. They and see a lot of energy they would like to start working on with open source projects. Mm-hmm. What is a deciding factor? What do you think people should think about when deciding whether or not, Hey, this is just a project that I maintained versus, Nope, I'm going to do the whole thing that get funding and allShreya: that.I think for me So I'm already kind of like I'm al I'm working on the open source full time. I think like the motivating thing for me was that, okay, this is. A problem that would need to get solved, like one way or another.This we talked about in variance earlier, and I do think that this is a, like being able to, like, I think if, if there's a contraction or a correction and [00:46:00] the, these LMS like don't have the kind of impact that we're, we're all hoping they would, I think it would be because of like, this problem because people kind of find that it's not as useful when it's running at very large scales when it's running in production, et cetera.So I think like that was very, that gave me a lot of conviction that it's something that I kind of wanted to work on and that was a switch for me. That it gave me the conviction to, for example, quit my job. Yeah. Also, yeah. Slightly confidential. Off the record. Off the record, yeah. Yeah.Alessio: We're not gonna talk about. Special project at Apple. That's a, that's very secret. Yeah. But you overlap Apple with Ian Goodfellow, which is obviously a, a very public figure in the AI space.Swyx: Actually, not that many people know what he did, so maybe we can, she can introduce Ian Goodfellow as well.Shreya: But, yeah, so Ian Goodfellow is the creator of Ganz or a generative adversarial network.So this was, I think I'm gonna mess up between 1215, I think 14, 15 ish if I remember correctly. So he basically created gans as a PhD student. As a PhD student. And he has a pretty interesting story of like how he thought of them and how [00:47:00] he kind of, Built the, and I I'm sure there's like interviews in like podcasts, et cetera with him where he talks about it, where like, how he got the idea for it and how he kind of like wrote the paper and did the experiments.So gans essentially were kind of like the first wave of generative images where you would see essentially kind of like fake auto-generated images, you know conditioned on like certain distributions. And so they were like very many variants of gans, like DC GAN, I'm gonna mess up the pronunciation, but dub, I'm just gonna call it w GaN.Mm-hmm. GAN Yeah. That like, you would essentially see these like really wonderful generative art. And I do think that like so I, I got the chance to work with him while at Apple. He had just moved to Apple from Google Brain and was building the cross-functional machine learning team within SPG.And I got the chance to work with him, which is very exciting. I learned so much and he is a fantastic manager and yeah, really, really enjoyed working withAlessio: him. And then he, he quit his job when they forced him to go back to the office. Right? That's theSwyx: Oh, really? Oh,Alessio: I didn't see that. Oh, okay. I think he basically, apple was like, you gotta go [00:48:00] back to the office.He said peace. That justSwyx: went toon. I'm curious, like what's some, some things that you learned from Ian that, or maybe some stories that,Shreya: Could be interesting. So there's like one, maybe machine learning specific and like one, maybe not machine learning specific and just general, like career stuff.Yeah. So the ML specific one was that well, Very high level. I think like working with him, you just truly see the creativity. And like after I worked with him, I was like, yeah, I, I totally get that. This is the the guy, like how his, how his brain works it's totally, it's so obvious that this is the guy who made like gans work basically.So I think he, when he does machine learning and when he thinks about like problems to solve, he thinks about it from a very creative out of the box way of thinking about it. And we kind of saw that with like, some of the problems where he was working on where anytime he had like feedback or suggestions on the, on the approaches that I was taking, I was like, wow, this is really exciting and like very creative and yeah, it was very, very cool to work on.So that was very high level machine learning.Swyx: I think the apple, apple standing by with like a blow dart if you, if like, say anymore.Shreya: I think the, the non-technical stuff, which [00:49:00] was I think truly made him such a fantastic manager. But when I went to Apple, I was, you know maybe a year outta school outta my job at that point.And I remember that I like most new grads was. Had like, okay, I, I need to kind of solve this problem on my own before I kind of get external help. Yeah. Yeah. And like, one of my first, I think probably my first or second week, like Ian and I, we were para programming and I remember that we were working together and like some setup issues were happening.And he would wait like ex

Ian Goodfellow from Google DeepMind discusses the role of microtubules in oophyte development.

Ian Goodfellow is a computer scientist and one of the co-founders of the data mining company Google DeepMind. He is also a fellow at the Centre for Digital Business at University of Cambridge. In this episode, Lexman interviews Ian about cryptocurrencies, enthusiasm, and the future of data mining.

Train your own AI using this free Lab created by Dr Mike Pound. Big thanks to Brilliant for sponsoring this video! Get started with a free 30 day trial and 20% discount: https://brilliant.org/DavidBombal How do you capitalize on this trend and learn AI? Dr Mike Pound of Computerphile fame shows us practically how to train your own AI. And the great news is that he has shared his Google colab lab with us to you can start learning for free! If you are into cybersecurity or any other tech field, you probably want to learn about AI and ML. They can really help your resume and help you increase the $$$ you earn. Machine Learning / Artificial Intelligence is a fantastic opportunity for you to get a better job. Start learning this amazing technology today and start learning with one of the best! // LAB // Go here to access the lab: https://colab.research.google.com/dri... // Previous Videos // Roadmap to ChatGPT and AI mastery: • Roadmap to ChatGP... I challenged ChatGPT to code and hack: • I challenged Chat... The truth about AI and why you should learn it - Computerphile explains: • The truth about A... // Dr Mike's recommend AI Book // Deep learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville: https://amzn.to/3vmu4LP // Dawid's recommend Books // 1. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: https://amzn.to/3IrGCHi 2. Pattern Recognition and Machine Learning: https://amzn.to/3IWVm2v 3. Machine Learning: A Probabilistic Perspective: https://amzn.to/3xYFM05 4. Python Machine Learning: https://amzn.to/3y0r08Q 5. Deep Learning: https://amzn.to/3kxSbVu 6. The Elements of Statistical Learning: https://amzn.to/3Iwuuox 7. Linear Algebra and Its Applications: https://amzn.to/3EGwMAs 8. Probability Theory: https://amzn.to/3IrGeZm 9. Calculus: Early Transcendentals: https://amzn.to/3Z3Eugh 10. Discrete Mathematics with Applications: https://amzn.to/3Zpzpyt 11. Mathematics for Machine Learning: https://amzn.to/3m8jp5N 12. A Hands-On Introduction to Data Science: https://amzn.to/3Szob8c 13. Introduction to Algorithms: https://amzn.to/3xXo50K 14. Artificial Intelligence: https://amzn.to/3Z2fqGv // Courses and tutorials // AI For Everyone by Andrew Ng: https://www.coursera.org/learn/ai-for... PyTorch Tutorial From Research to Production: https://www.infoq.com/presentations/p... Scikit-learn Machine Learning in Python: https://scikit-learn.org/stable/ // PyTorch // Github: https://github.com/pytorch Website: https://pytorch.org/ Documentation: https://ai.facebook.com/tools/pytorch/ // Mike SOCIAL // Twitter: https://twitter.com/_mikepound YouTube: / computerphile Website: https://www.nottingham.ac.uk/research... // David SOCIAL // Discord: https://discord.com/invite/usKSyzb Twitter: https://www.twitter.com/davidbombal Instagram: https://www.instagram.com/davidbombal LinkedIn: https://www.linkedin.com/in/davidbombal Facebook: https://www.facebook.com/davidbombal.co TikTok: http://tiktok.com/@davidbombal YouTube: / davidbombal // MY STUFF // https://www.amazon.com/shop/davidbombal // SPONSORS // Interested in sponsoring my videos? Reach out to my team here: sponsors@davidbombal.com Please note that links listed may be affiliate links and provide me with a small percentage/kickback should you use them to purchase any of the items listed or recommended. Thank you for supporting me and this channel! #chatgpt #computerphile #ai

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.

Ian Goodfellow is a computer scientist who has written extensively on artificial intelligence and machine learning. He's also the co-founder of the powerful reinforcement learning platform, Synaptic. In this episode, we discuss his work with reinforcement learning, his experiences living in Tampa, and his love of semivowels.

ChatGPT and AI mastery - how to get started in AI. Big thanks to Brilliant for sponsoring this video! Get started with a 20% discount using this link: https://brilliant.org/davidbombal How do you capitalize on this trend and learn AI? Dr Mike Pound of Computerphile fame tells us how to ride this wave. If you are into cybersecurity or any other tech field, you probably want to learn about AI and ML. They can really help your resume and help you increase the $$$ you earn. AI just become Sentient? And will it take your job? Or is AI just a fantastic opportunity for you to get a better job? In this interview with Dr Michael Pound we discuss hype vs reality and get a quick start guide on how to learn AI. // MENU // 00:00 - Coming up 00:40 - Sponsored segment 02:28 - A.I. Hype // Should we be worried? 03:37 - Amazing but flawed 08:07 - Is it worth it getting into CompSci? 10:02 - Knowing A.I. makes you valuable // Learn A.I. 13:43 - Resources for learning A.I. 15:58 - Should you get into CompSci? 17:35 - Enhancing your career with A.I. 20:16 - The limits of A.I. 24:57 - A.I in academics // How A.I. affects academic work 31:02 - Conclusion // Previous Videos // I challenged ChatGPT to code and hack: https://youtu.be/Fw5ybNwwSbg The truth about AI and why you should learn it - Computerphile explains: https://youtu.be/PH9RQ6Yx75c // BOOK // Deep learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville: https://amzn.to/3vmu4LP // Courses and tutorials // AI For Everyone by Andrew Ng: https://www.coursera.org/learn/ai-for... PyTorch Tutorial From Research to Production: https://www.infoq.com/presentations/p... Scikit-learn Machine Learning in Python: https://scikit-learn.org/stable/ // PyTorch // Github: https://github.com/pytorch Website: https://pytorch.org/ Documentation: https://ai.facebook.com/tools/pytorch/ // Mike SOCIAL // Twitter: https://twitter.com/_mikepound YouTube: https://www.youtube.com/user/Computer... Website: https://www.nottingham.ac.uk/research... // David SOCIAL // Discord: https://discord.com/invite/usKSyzb Twitter: https://www.twitter.com/davidbombal Instagram: https://www.instagram.com/davidbombal LinkedIn: https://www.linkedin.com/in/davidbombal Facebook: https://www.facebook.com/davidbombal.co TikTok: http://tiktok.com/@davidbombal YouTube: https://www.youtube.com/davidbombal // MY STUFF // https://www.amazon.com/shop/davidbombal // SPONSORS // Interested in sponsoring my videos? Reach out to my team here: sponsors@davidbombal.com chatgpt chatgpt hype chatgpt reality chatgpt truth ai chatgpt c chatgpt python chatgpt hak5 chatgpt rubber ducky chatgpt cisco python android samsung linux kali linux rubber ducky hak5 omg cable lamda neural network machine learning deep learning sentient google ai mike pound michael pound dr michael pound computerphile artificial intelligence google ai sentient google ai lamda google ai sentient conversation google ai alive ai jobs Please note that links listed may be affiliate links and provide me with a small percentage/kickback should you use them to purchase any of the items listed or recommended. Thank you for supporting me and this channel! #chatgpt #computerphile #ai

Currently, the southern hemisphere is experiencing a phenomenon called an apery. Paleoanthropologists believe that apery may have been a sign of good luck, because it suggests that a harvest was plentiful. But what does this have to do with artificial intelligence? Ian Goodfellow, one of the world's foremost experts on artificial intelligence, joins Lexman to discuss the future of AI and its implications for human society.

Ian Goodfellow from Google DeepMind talks about machine learning, unripeness and what it means for beech trees.

Between ChatGPT generating limericks in the style of George Costanza and Lensa turning your profile picture into a cartoon, AI seems to have finally broken into mainstream awareness in the past few months. But what's going on below the surface? How did the technology advance to this point? Who has been funding its development, and how does it actually work? We dig into all of those issues (and other very basic questions we had about the technology) in this conversation with Ryan Khurana, Chief of Staff at WOMBO [www.w.ai], a Generative AI for entertainment company whose app Dream [www.dream.ai] won the Play Store App of the Year in 2022. ----- Book recommendations: Prediction Machines by Ajay Agrawal, Joshua Gans, Avi Goldfarb Power and Prediction by Ajay Agrawal, Joshua Gans, Avi Goldfarb Architects of Intelligence by Martin Ford Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville ----- Links: More episodes of Free Lunch by The Peak: https://readthepeak.com/shows/free-lunch Follow Taylor on Twitter: @taylorscollon Follow Sarah on Twitter: @sarahbartnicka Subscribe to The Peak's daily business newsletter: https://readthepeak.com/b/the-peak/subscribe

Ian Goodfellow shares his experiences in the Goshen Chess Club, how to make a repeating pattern, and how to end the game in a tactically sound way.

Ian Goodfellow discusses the decompositionsoperator on datasets, and how it can be used for animadverters.

Ian Goodfellow, a professor at the University of Texas at San Antonio, discusses the magpie species and their use of Rhamphothecas flowers to find food.

Ian Goodfellow is a Professor of Computer Science at the University of Cambridge, and a Fellow of Kings College. He is known for his work on recurrent neural networks, variational autoencoders, and natural language processing. In this episode, Lexman interviews Goodfellow about his work on deciduas, hydrophytes, and commonages. They discuss Wilhelmstrasse and tenantries, and Lexman asks Goodfellow about his favorite topman song.

#mlnews #bloom #ai Today we look at all the recent giant language models in the AI world! OUTLINE: 0:00 - Intro 0:55 - BLOOM: Open-Source 176B Language Model 5:25 - YALM 100B 5:40 - Chinese Brain-Scale Supercomputer 7:25 - Meta AI Translates over 200 Languages 10:05 - Reproducibility Crisis Workshop 10:55 - AI21 Raises $64M 11:50 - Ian Goodfellow leaves Apple 12:20 - Andrej Karpathy leaves Tesla 12:55 - Wordalle References: BLOOM: Open-Source 176B Language Model https://bigscience.huggingface.co/blo... https://huggingface.co/spaces/bigscie... https://huggingface.co/bigscience/blo... YALM 100B https://github.com/yandex/YaLM-100B Chinese Brain-Scale Supercomputer https://www.scmp.com/news/china/scien... https://archive.ph/YaoA6#selection-12... Meta AI Translates over 200 Languages https://ai.facebook.com/research/no-l... Reproducibility Crisis Workshop https://reproducible.cs.princeton.edu/ AI21 Raises $64M https://techcrunch.com/2022/07/12/ope... Ian Goodfellow leaves Apple https://twitter.com/goodfellow_ian/st... Andrey Karpathy leaves Tesla https://mobile.twitter.com/karpathy/s... https://www.businessinsider.com/repor... Wordalle https://huggingface.co/spaces/hugging... Links: Homepage: https://ykilcher.com Merch: ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

AI just become Sentient? And will it take your job? Or is AI just a fantastic opportunity for you to get a better job? In this interview with Dr Michael Pound we discuss hype vs reality and get a quick start guide on how to learn AI. // MENU // 00:00 - Coming Up 00:45 - Intro 01:10 - Michael Pound introduction 02:49 - Will AI take our jobs? 04:55 - What is LaMDA? 08:38 - Can Python functions get lonely? 11:26 - The definition of "sentience" 11:59 - AI vs Machine Learning 18:48 - Neural Networks 19:49 - Malware example 21:59 - Stochastic Gradient Descent 22:30 - Supervised learning 23:45 - Unsupervised learning 26:03 - Reinforcement learning 27:35 - Are the robots taking over? 30:14 - What is AI really good at? 33:28 - Definition of Deep Learning 35:37 - Neural Networks 36:53 - What to learn 40:50 - Using PyTorch 43:52 - Google colab 44:48 - Study recommendations 46:16 - The demand for AI skills 48:15 - Teaching cyber security 50:06 - Final Advice 55:09 - Conclusion // Video mentions // ComputerPhile (lambda is not sentient): https://youtu.be/iBouACLc-hw Data Analysis Playlist: https://www.youtube.com/watch?v=NxYEz... Neural Networks Playlist: https://www.youtube.com/watch?v=py5by... Computer Vision Playlist: https://www.youtube.com/watch?v=C_zFh... // BOOK // Deep learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville: https://amzn.to/3vmu4LP // COURSE // AI For Everyone by Andrew Ng: https://www.coursera.org/learn/ai-for... // PyTorch // Github: https://github.com/pytorch Website: https://pytorch.org/ Documentation: https://ai.facebook.com/tools/pytorch/ // Mike SOCIAL // Twitter: https://twitter.com/_mikepound YouTube: https://www.youtube.com/user/Computer... Website: https://www.nottingham.ac.uk/research... // David SOCIAL // Discord: https://discord.com/invite/usKSyzb Twitter: https://www.twitter.com/davidbombal Instagram: https://www.instagram.com/davidbombal LinkedIn: https://www.linkedin.com/in/davidbombal Facebook: https://www.facebook.com/davidbombal.co TikTok: http://tiktok.com/@davidbombal YouTube: https://www.youtube.com/davidbombal // MY STUFF // https://www.amazon.com/shop/davidbombal // SPONSORS // Interested in sponsoring my videos? Reach out to my team here: sponsors@davidbombal.com lamda python neural network ai machine learning deep learning sentient google ai mike pound michael pound dr michael pound computerphile artificial intelligence google ai sentient google ai lamda google ai sentient conversation google ai alive ai jobs Please note that links listed may be affiliate links and provide me with a small percentage/kickback should you use them to purchase any of the items listed or recommended. Thank you for supporting me and this channel! #ai #computerphile #lamda

Joel is on a business trip in India and falls asleep while working on his laptop. He wakes up to find that his laptop has been forcefully taken away from him, disrupting his work. In a surreal twist, he eventuallySleepwanders off the path he had been following and finds himself back in his room at the hotel.

Ian Goodfellow, an AI researcher at Google, discusses his work on the game-changing concept of "AI natural language processing." In particular, Flaminius - an artificial intelligence that can understand ancient Roman political speeches and ceylonite - a new form of stickers that stick to almost any surface.

Newly minted Novice Shaduf brewer Ian Goodfellow joins Lexman to discuss the safety and brewing considerations for sizzler style beers. They also talk about some of the more unusual ingredients you can use in a shaduf brew, and how to troubleshoot if you're experiencing problems with your batch.

Explicamos cómo se ha fraguado la decisión de cambiar los iPhone a USB-C, un fichaje que ha sido esperado muchos, muchos años. Patrocinador: El CTO Summit de GeeksHub vuelve con más fuerza que nunca. El evento clave para todos los responsables de equipos de IT se celebra el 24 y 25 de junio en Valencia, y este año va a ser impresionante por la calidad de las ponencias, las charlas y en general un programa lleno de cosas interesantes. — Con el código MIXX45 consigue tu entrada con un 45% de descuento. Explicamos cómo se ha fraguado la decisión de cambiar los iPhone a USB-C, tanto desde la presión gubernamental como desde la más obvia necesidad técnica. Será en 2023 o será el año siguiente, pero el fichaje llegará. Hablamos también sobre los cascos de realidad virtual de Apple, cómo no. Ian Goodfellow, Former Apple Director of Machine Learning, to Join DeepMind - Bloomberg Apple (AAPL) Delays Plan to Have Workers in Office Three Days a Week - Bloomberg China ordena al Gobierno y empresas estatales deshacerse de PCs extranjeras BMW ships cars without Apple, Google tech | Automotive News Europe Common external power supply - Wikipedia Common charger: MEPs agree on proposal to reduce electronic waste | News | European Parliament USB-C makes sense for iPhone, does it finally make sense for Apple? | iMore Puedes ponerte en contacto con nosotros por correo en: alex@barredo.es Suscríbete al boletín de información diario en https://newsletter.mixx.io Escucha el podcast diario de información tecnológica en https://podcast.mixx.io Nuestro grupo de Telegram: https://t.me/mixxiocomunidad

Craig's Whiteboard, EA Gaming, FCPX Apple's Worldwide Developers Conference kicks off June 6 with keynote address  Craig's whiteboard leaks WWDC22.  Brian Roberts' one that got away.  Apple in talks to buy EA gaming. Disney and Amazon are also potential suitors.  Losing Ian Goodfellow to DeepMind is the dumbest thing Apple's ever done.  Apple response to "Final Cut Pro in TV and Film" open letter.  TIME 100 most influential people of 2022 features Tim Cook – Laurene Powell Jobs wrote his entry.  Apple unveils new Apple Watch Pride Edition bands.  Apple TV+ now streaming Prehistoric Planet.  Apple expands Today at Apple Creative Studios, providing new opportunities to young creatives.  FCC filings reveal Apple's mysterious 'Network Adapter' that runs iOS.  Apple looks to boost production outside China.  Guest drops Apple Watch on EPCOT ride & jumps out to get It, then has $40,000 in fraudulent credit card Charges. Picks of the Week  Rene's Pick: Apollo 1.3  Andy's Pick: Amplosion Alex's Pick: Audio Design Desk Hosts: Leo Laporte, Alex Lindsay, Rene Ritchie, and Andy Ihnatko Download or subscribe to this show at https://twit.tv/shows/macbreak-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: itpro.tv/macbreak promo code MACBREAK30 eightsleep.com/macbreak kolide.com/macbreak

Craig's Whiteboard, EA Gaming, FCPX Apple's Worldwide Developers Conference kicks off June 6 with keynote address  Craig's whiteboard leaks WWDC22.  Brian Roberts' one that got away.  Apple in talks to buy EA gaming. Disney and Amazon are also potential suitors.  Losing Ian Goodfellow to DeepMind is the dumbest thing Apple's ever done.  Apple response to "Final Cut Pro in TV and Film" open letter.  TIME 100 most influential people of 2022 features Tim Cook – Laurene Powell Jobs wrote his entry.  Apple unveils new Apple Watch Pride Edition bands.  Apple TV+ now streaming Prehistoric Planet.  Apple expands Today at Apple Creative Studios, providing new opportunities to young creatives.  FCC filings reveal Apple's mysterious 'Network Adapter' that runs iOS.  Apple looks to boost production outside China.  Guest drops Apple Watch on EPCOT ride & jumps out to get It, then has $40,000 in fraudulent credit card Charges. Picks of the Week  Rene's Pick: Apollo 1.3  Andy's Pick: Amplosion Alex's Pick: Audio Design Desk Hosts: Leo Laporte, Alex Lindsay, Rene Ritchie, and Andy Ihnatko Download or subscribe to this show at https://twit.tv/shows/macbreak-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: itpro.tv/macbreak promo code MACBREAK30 eightsleep.com/macbreak kolide.com/macbreak

Craig's Whiteboard, EA Gaming, FCPX Apple's Worldwide Developers Conference kicks off June 6 with keynote address  Craig's whiteboard leaks WWDC22.  Brian Roberts' one that got away.  Apple in talks to buy EA gaming. Disney and Amazon are also potential suitors.  Losing Ian Goodfellow to DeepMind is the dumbest thing Apple's ever done.  Apple response to "Final Cut Pro in TV and Film" open letter.  TIME 100 most influential people of 2022 features Tim Cook – Laurene Powell Jobs wrote his entry.  Apple unveils new Apple Watch Pride Edition bands.  Apple TV+ now streaming Prehistoric Planet.  Apple expands Today at Apple Creative Studios, providing new opportunities to young creatives.  FCC filings reveal Apple's mysterious 'Network Adapter' that runs iOS.  Apple looks to boost production outside China.  Guest drops Apple Watch on EPCOT ride & jumps out to get It, then has $40,000 in fraudulent credit card Charges. Picks of the Week  Rene's Pick: Apollo 1.3  Andy's Pick: Amplosion Alex's Pick: Audio Design Desk Hosts: Leo Laporte, Alex Lindsay, Rene Ritchie, and Andy Ihnatko Download or subscribe to this show at https://twit.tv/shows/macbreak-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: itpro.tv/macbreak promo code MACBREAK30 eightsleep.com/macbreak kolide.com/macbreak

Craig's Whiteboard, EA Gaming, FCPX Apple's Worldwide Developers Conference kicks off June 6 with keynote address  Craig's whiteboard leaks WWDC22.  Brian Roberts' one that got away.  Apple in talks to buy EA gaming. Disney and Amazon are also potential suitors.  Losing Ian Goodfellow to DeepMind is the dumbest thing Apple's ever done.  Apple response to "Final Cut Pro in TV and Film" open letter.  TIME 100 most influential people of 2022 features Tim Cook – Laurene Powell Jobs wrote his entry.  Apple unveils new Apple Watch Pride Edition bands.  Apple TV+ now streaming Prehistoric Planet.  Apple expands Today at Apple Creative Studios, providing new opportunities to young creatives.  FCC filings reveal Apple's mysterious 'Network Adapter' that runs iOS.  Apple looks to boost production outside China.  Guest drops Apple Watch on EPCOT ride & jumps out to get It, then has $40,000 in fraudulent credit card Charges. Picks of the Week  Rene's Pick: Apollo 1.3  Andy's Pick: Amplosion Alex's Pick: Audio Design Desk Hosts: Leo Laporte, Alex Lindsay, Rene Ritchie, and Andy Ihnatko Download or subscribe to this show at https://twit.tv/shows/macbreak-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: itpro.tv/macbreak promo code MACBREAK30 eightsleep.com/macbreak kolide.com/macbreak

Craig's Whiteboard, EA Gaming, FCPX Apple's Worldwide Developers Conference kicks off June 6 with keynote address  Craig's whiteboard leaks WWDC22.  Brian Roberts' one that got away.  Apple in talks to buy EA gaming. Disney and Amazon are also potential suitors.  Losing Ian Goodfellow to DeepMind is the dumbest thing Apple's ever done.  Apple response to "Final Cut Pro in TV and Film" open letter.  TIME 100 most influential people of 2022 features Tim Cook – Laurene Powell Jobs wrote his entry.  Apple unveils new Apple Watch Pride Edition bands.  Apple TV+ now streaming Prehistoric Planet.  Apple expands Today at Apple Creative Studios, providing new opportunities to young creatives.  FCC filings reveal Apple's mysterious 'Network Adapter' that runs iOS.  Apple looks to boost production outside China.  Guest drops Apple Watch on EPCOT ride & jumps out to get It, then has $40,000 in fraudulent credit card Charges. Picks of the Week  Rene's Pick: Apollo 1.3  Andy's Pick: Amplosion Alex's Pick: Audio Design Desk Hosts: Leo Laporte, Alex Lindsay, Rene Ritchie, and Andy Ihnatko Download or subscribe to this show at https://twit.tv/shows/macbreak-weekly. Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: itpro.tv/macbreak promo code MACBREAK30 eightsleep.com/macbreak kolide.com/macbreak

This week: Apple Watch Series 8 getting the redesign we've ALL been waiting for, a cheaper smaller Apple TV is in the works, Apple's mixed reality headset is ALMOST ready, and working from home vs the office — how much would YOU need to get paid to go back to the office full time? We reveal our numbers! This episode supported by Remotely manage your Mac, iPhone, or iPad with Jamf. Manage 3 devices for FREE at jamf.com/beyond Easily create a beautiful website all by yourself, at Squarespace.com/cultcast. Use offer code CultCast at checkout to get 10% off your first purchase of a website or domain. Cult of Mac's watch store is full of beautiful straps that cost way less than Apple's. See the full curated collection at Store.Cultofmac.com CultCloth will keep your Mac Studio, Studio Display, iPhone 13, glasses and lenses sparkling clean, and for a limited time use code CULTCAST at checkout to score a free CarryCloth with any order at CultCloth.co. This week's stories Sound familiar? Apple Watch 8 display might be flat Remember last year when rumors were flying that Apple Watch Series 7 would feature a flat display and squared-off edges? DIDN'T HAPPEN. But a new rumor suggests those traits might define this year's Apple Watch Series 8. More affordable Apple TV might launch soon A new Apple TV streamer will launch in the second half of 2022, according to a trusted analyst. And there's a hint in the prediction that the device will cost less than its predecessors. Apple shows off AR/VR headset to board of directors Although Apple's VR/AR headset is still supposed to be a secret project, the company's board of directors reportedly got a look at the device recently. This could be a sign the product is moving close to a release. COVID-19 throws off Apple's return-to-office plan yet again Apple reportedly slowed the pace at which it will require its corporate employees to return to the office. They were scheduled to be back at their desks three days a week starting later this month, but rising numbers of COVID-19 cases supposedly pushed that back. Apple's Director of Machine Learning Resigns Due to Return to Office Work Apple's director of machine learning, Ian Goodfellow, has resigned from his role a little over four years after he joined the company after previously being one of Google's top AI employees, according to The Verge's Zoë Schiffer.

Why Mike feels like Heroku is in a failed state, what drove us crazy about Google I/O this year, how Chris botched something super important, and some serious Python love sprinkled throughout.

Cuánta publicidad verás en Disney +, Netflix amplía su ventana en cines y multan a Google en España.Puedes apoyar la realización de este programa con una suscripción. Más información por acáNoticias: -El plan con publicidad que lanzará Disney+ contendría un promedio de cuatro minutos de comerciales-Netflix está considerando el manejar una ventana de 45 días de estreno en salas de cines para cintas como la secuela de Knives Out y El Bardo-John Deere adquirió un paquete de algoritmos de la startup de inteligencia artificial Light para promover su desarrollo de agricultura completamente autónoma.-Ian Goodfellow, aceptó un nuevo puesto dentro de la división de Google-Google debe pagar una multa de 10 millones de euros por violar el Reglamento General de Protección de DatosDiscusión: el manejo de privacidad de datos See acast.com/privacy for privacy and opt-out information. Become a member at https://plus.acast.com/s/noticias-de-tecnologia-express.

Sigue habiendo cierta "revuelta" interna por parte de algunos empleados que siguen enfadados por la reducción de flexibilidad. El creador de las GAN deja Apple, aunque no creemos que realmente sea un problema tan grave como hemos visto en algunos titulares. Sigue habiendo cierta "revuelta" interna por parte de algunos empleados que siguen enfadados por la reducción de flexibilidad. Repasamos las ambiciones de algunos competidores de Apple, y algunas de sus cagadas e hipocresías más recientes. Nuestro episodio más fanboy. Apple Together: Thoughts on Office-Bound Work Apple sued by Russian users over suspension of Apple Pay | AppleInsider Apple, Google, and Microsoft commit to expanded support for FIDO standard - Apple Play Fortnite on iOS, iPadOS, Android Phones and Tablets, and Windows PC with Xbox Cloud Gaming for Free - Xbox Wire Apple aclara las razones por las que ha eliminado 3 millones de apps sin actualizar de la App Store, y sigue sin convencer a los desarrolladores | Tecnología - ComputerHoy.com Apple Music is Sometimes Replacing Other Apps in the Dock When Installed From App Store [Updated] - MacRumors What's new in firmware updates for AirTag - Apple Support Qualcomm says its Apple Silicon rival chips will be in PCs by late 2023 | AppleInsider Apple lawsuit says 'stealth' startup Rivos poached engineers to steal secrets | Reuters Fitbit retira 1,7 millones de relojes inteligentes por peligro de quemaduras | Compañías | Cinco Días Pachinko 2 confirmada, ¿cuándo se estrena la segunda temporada? Puedes ponerte en contacto con nosotros por correo en: alex@barredo.es Suscríbete al boletín de información diario en https://newsletter.mixx.io Escucha el podcast diario de información tecnológica en https://podcast.mixx.io Nuestro grupo de Telegram: https://t.me/mixxiocomunidad

0:00:00 – HKPUG 會訊 + 每週 IT 新聞 1:06:05 – Main Topic 本集全長：2:20:35 Tag: iPod 停產, 廣達, 蘋果在家工作政策, WFH, Ian Goodfellow, 京東方, BOE, iPhone OLED, …

Bienvenue dans cet épisode 90 d'iWeek (la semaine Apple), le podcast. Apple arrête l'iPod. Présentation : Benjamin Vincent. Intervenants : François Le Truédic, Gilles Dounès, Elie Abitbol, Fabrice Neuman. Production : OUATCH Audio. Cette semaine : Apple met fin à l'extraordinaire saga de l'iPod, commencée le 23 octobre 2001 avec Steve Jobs. Nous vous faisons revivre un moment de cette mini-keynote dédiée à un "appareil numérique révolutionnaire (ce n'était pas un Mac)". Le co-fondateur d'Apple était en train de présenter l'iPod qui allait révolutionner l'industrie musicale. C'est évidemment l'événement de la semaine puisqu'Apple ne fabriquera plus d'iPod Touch et écoule les stocks, déjà quasiment vides en 48 heures. L'info de la semaine, c'est la chute brutale et sans préavis du prix de reprise, par Apple, de tous les appareils (sauf l'iPhone) : jusqu'à -42% ! Dans Pomme-S, nous revenons notamment sur l'usine Qanta de Shanghai à seulement 30% de sa capacité et bientôt 50% au mieux. Conséquence : le délai pour certains Mac Studio M1 Ultra dépasse maintenant trois mois. Et près de deux mois pour les MacBook Pro 14 et 16 pouces configurés à la demande. Comme chaque semaine, retrouvez le diner d'Elie, les mises à jour de la semaine (pour les AirPods et le Studio Display mais aussi Photoshop pour iPad, Premiere Pro et WhatsApp sur iOS) et le tuto audio : Benjamin vous explique comment extraire l'audio d'une vidéo sur Mac (sur iPhone et iPad, ce sera la semaine prochaine !). Dans les rumeurs de la semaine, Ming-Chi Kuo parie sur l'USB-C à la place du Lightning sur tous les iPhone 15 en 2023, gamme sur l'ensemble de laquelle l'encoche devrait avoir été remplacée par un point d'exclamation à l'horizontale. Et on vous dit ce qu'on en pense ! Sans oublier ce nouveau brevet qui pourrait profiter aux Apple Glasses... Le châpitrage de cet épisode 90 est intégré par Apple, à Apple Podcasts. Vous pouvez donc en profiter aussi, désormais, en nous écoutant sur votre iPhone, iPad, Mac, Apple TV ou Apple Watch ! Rendez-vous jeudi prochain, 19 mai 2022 vers 20h, pour l'épisode 91 d'iWeek (la semaine Apple) ! Par ailleurs, retrouvez la version vidéo du podcast sur la chaîne YouTube d'iWeek ! Mise en ligne : chaque vendredi. Abonnez-vous à la chaîne YouTube d'iWeek et cliquez sur la cloche pour être alerté dès qu'un nouvel épisode est disponible en vidéo. Essayez iFive, votre dose Apple quotidienne, le 1er podcast quotidien sur l'actu Apple : 5 minutes par jour, 5 jours par semaine, du lundi au vendredi, avec l'essentiel de l'info Apple quotidienne. iFive (la dose Apple) by iWeek : 4,99€ par mois, sur Spotify et Apple Podcasts (sans engagement et avec 3 jours d'essai gratuit sur Apple Podcasts). iFive sur Spotify : https://open.spotify.com/show/35TL5Av7WVKCjih07Jtdb5?si=fb4913aa7bf2477a iFive sur Apple Podcasts : https://apple.co/2RORQzn Pour avoir les dernières nouvelles d'iFive et d'iWeek, suivez nos deux comptes sur Twitter : @iFiveFR et @iweeknews.

Apple's director of machine learning, Ian Goodfellow, is leaving the company due to its return to work policy, according to a tweet by Zoe Schiffer, a tech reporter with The Verge. Ride-hailing network provider Uber's CEO has promised cutbacks on spending, CNBC reports. And the global tech-led sell-off after US monetary tightening has hit cryptocurrencies as well, with Bitcoin losing more than half its value from its November peak. Notes: Apple's director of machine learning, Ian Goodfellow, is leaving the company due to its return-to-work policy, according to a tweet on May 8 by Zoe Schiffer, a tech reporter with The Verge. In a note to staff, Goodfellow said “I believe strongly that more flexibility would have been the best policy for my team,” according to Schiffer's tweet, which has been picked up widely. Uber, is the latest tech company to announce cutbacks, with money becoming less cheap, and investors looking elsewhere. The ride-hailing network provider will cut back on spending and focus on becoming a leaner business to address a “seismic shift” in investor sentiment, CEO Dara Khosrowshahi told employees in an email, CNBC reported on Monday. Uber will slash spending on marketing and incentives and treat hiring as a “privilege,” Khosrowshahi said, according to CNBC. The world's biggest tech companies have lost over a trillion dollars in value, as dumped stocks after central banks around the world raised the rates at which commercial banks could borrow from them. Bitcoin fell below the $30,000 mark today as both traditional financial markets and cryptocurrencies suffered from a sell-off caused by the US Federal Reserve's monetary tightening as well as fears of a recession, CoinDesk reports. The latest decline left bitcoin at a 10-month low and its lowest price this year, less than half the value that the cryptocurrency had in November last. HCL Technologies is acquiring Confinale AG, a Switzerland-based digital banking and wealth management consulting specialist, India's third-biggest IT services company said in a press release. The acquisition will help HCL expand its reach in the global wealth management market with an emphasis on consulting, implementation and management of banking software from Avaloq, another Swiss company, whose software is used by some 140 banks around the world. Clearview AI, an American facial recognition surveillance company, has agreed to permanently ban most private companies from using its service under a court settlement, The Verge reports. The agreement, filed in a court in the US state of Illinois yesterday, would settle a 2020 American Civil Liberties Union lawsuit that alleged the company had built its business on facial recognition data taken without user consent. Theme music courtesy Free Music & Sounds: https://soundcloud.com/freemusicandsounds

Tech stocks are crumbling, NFTs losing steam, SafeGraph, Google IO preview  Elon Musk raises $7 billion in new funding for Twitter buyout.  Evaluating Elon Musk's Plan To Fix Twitter.  Elon Musk Plans to Take Twitter Public a Few Years After Buyout.  Why Tech Stocks Are Crashing and Burning.  Bitcoin drops below $35,000 over the weekend, extending Friday's losses.  NFT Sales Are Flatlining.  NFTs Are Legally Problematic ft. Steve Mould & Coffeezilla.  Unconventional Success: A Fundamental Approach to Personal Investment by David F. Swensen.  Why the Past 10 Years of American Life Have Been Uniquely Stupid.  Vatican Offers, Mysteriously Rescinds Interview About Pope's Metaverse Plans.  Data Broker SafeGraph Stops Selling Location Data of People Who Visit Planned Parenthood.  CDC Tracked Millions of Phones to See If Americans Followed COVID Lockdown Orders.  Global Privacy Control (GPC).  Google previews I/O 2022 schedule, 'What's new' keynotes, and sessions.  The latest Pixel Watch spec rumors show Google's trying to make a flagship. Ian Goodfellow, Apple's director of machine learning, is leaving the company due to its return to work policy.  AMD sales jump 71%, shrugging off concerns about PC slowdown.  Salt_Hank on TikTok.  Nvidia pays $5.5 million for allegedly hiding how many gaming GPUs were sold to crypto miners.  Intuit to pay $141M settlement over 'free' TurboTax ads.  Frontier lied about Internet speeds and "ripped off customers," FTC says.  New York City sues Activision, targeting CEO Bobby Kotick.  Apple's Self Service Repair now available. Host: Leo Laporte Guests: Brianna Wu and Alex Kantrowitz Download or subscribe to this show at https://twit.tv/shows/this-week-in-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: UserWay.org/twit wwt.com/twit mintmobile.com/twit policygenius.com/twit

Tech stocks are crumbling, NFTs losing steam, SafeGraph, Google IO preview  Elon Musk raises $7 billion in new funding for Twitter buyout.  Evaluating Elon Musk's Plan To Fix Twitter.  Elon Musk Plans to Take Twitter Public a Few Years After Buyout.  Why Tech Stocks Are Crashing and Burning.  Bitcoin drops below $35,000 over the weekend, extending Friday's losses.  NFT Sales Are Flatlining.  NFTs Are Legally Problematic ft. Steve Mould & Coffeezilla.  Unconventional Success: A Fundamental Approach to Personal Investment by David F. Swensen.  Why the Past 10 Years of American Life Have Been Uniquely Stupid.  Vatican Offers, Mysteriously Rescinds Interview About Pope's Metaverse Plans.  Data Broker SafeGraph Stops Selling Location Data of People Who Visit Planned Parenthood.  CDC Tracked Millions of Phones to See If Americans Followed COVID Lockdown Orders.  Global Privacy Control (GPC).  Google previews I/O 2022 schedule, 'What's new' keynotes, and sessions.  The latest Pixel Watch spec rumors show Google's trying to make a flagship. Ian Goodfellow, Apple's director of machine learning, is leaving the company due to its return to work policy.  AMD sales jump 71%, shrugging off concerns about PC slowdown.  Salt_Hank on TikTok.  Nvidia pays $5.5 million for allegedly hiding how many gaming GPUs were sold to crypto miners.  Intuit to pay $141M settlement over 'free' TurboTax ads.  Frontier lied about Internet speeds and "ripped off customers," FTC says.  New York City sues Activision, targeting CEO Bobby Kotick.  Apple's Self Service Repair now available. Host: Leo Laporte Guests: Brianna Wu and Alex Kantrowitz Download or subscribe to this show at https://twit.tv/shows/this-week-in-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: UserWay.org/twit wwt.com/twit mintmobile.com/twit policygenius.com/twit

Tech stocks are crumbling, NFTs losing steam, SafeGraph, Google IO preview  Elon Musk raises $7 billion in new funding for Twitter buyout.  Evaluating Elon Musk's Plan To Fix Twitter.  Elon Musk Plans to Take Twitter Public a Few Years After Buyout.  Why Tech Stocks Are Crashing and Burning.  Bitcoin drops below $35,000 over the weekend, extending Friday's losses.  NFT Sales Are Flatlining.  NFTs Are Legally Problematic ft. Steve Mould & Coffeezilla.  Unconventional Success: A Fundamental Approach to Personal Investment by David F. Swensen.  Why the Past 10 Years of American Life Have Been Uniquely Stupid.  Vatican Offers, Mysteriously Rescinds Interview About Pope's Metaverse Plans.  Data Broker SafeGraph Stops Selling Location Data of People Who Visit Planned Parenthood.  CDC Tracked Millions of Phones to See If Americans Followed COVID Lockdown Orders.  Global Privacy Control (GPC).  Google previews I/O 2022 schedule, 'What's new' keynotes, and sessions.  The latest Pixel Watch spec rumors show Google's trying to make a flagship. Ian Goodfellow, Apple's director of machine learning, is leaving the company due to its return to work policy.  AMD sales jump 71%, shrugging off concerns about PC slowdown.  Salt_Hank on TikTok.  Nvidia pays $5.5 million for allegedly hiding how many gaming GPUs were sold to crypto miners.  Intuit to pay $141M settlement over 'free' TurboTax ads.  Frontier lied about Internet speeds and "ripped off customers," FTC says.  New York City sues Activision, targeting CEO Bobby Kotick.  Apple's Self Service Repair now available. Host: Leo Laporte Guests: Brianna Wu and Alex Kantrowitz Download or subscribe to this show at https://twit.tv/shows/this-week-in-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: UserWay.org/twit wwt.com/twit mintmobile.com/twit policygenius.com/twit

Tech stocks are crumbling, NFTs losing steam, SafeGraph, Google IO preview  Elon Musk raises $7 billion in new funding for Twitter buyout.  Evaluating Elon Musk's Plan To Fix Twitter.  Elon Musk Plans to Take Twitter Public a Few Years After Buyout.  Why Tech Stocks Are Crashing and Burning.  Bitcoin drops below $35,000 over the weekend, extending Friday's losses.  NFT Sales Are Flatlining.  NFTs Are Legally Problematic ft. Steve Mould & Coffeezilla.  Unconventional Success: A Fundamental Approach to Personal Investment by David F. Swensen.  Why the Past 10 Years of American Life Have Been Uniquely Stupid.  Vatican Offers, Mysteriously Rescinds Interview About Pope's Metaverse Plans.  Data Broker SafeGraph Stops Selling Location Data of People Who Visit Planned Parenthood.  CDC Tracked Millions of Phones to See If Americans Followed COVID Lockdown Orders.  Global Privacy Control (GPC).  Google previews I/O 2022 schedule, 'What's new' keynotes, and sessions.  The latest Pixel Watch spec rumors show Google's trying to make a flagship. Ian Goodfellow, Apple's director of machine learning, is leaving the company due to its return to work policy.  AMD sales jump 71%, shrugging off concerns about PC slowdown.  Salt_Hank on TikTok.  Nvidia pays $5.5 million for allegedly hiding how many gaming GPUs were sold to crypto miners.  Intuit to pay $141M settlement over 'free' TurboTax ads.  Frontier lied about Internet speeds and "ripped off customers," FTC says.  New York City sues Activision, targeting CEO Bobby Kotick.  Apple's Self Service Repair now available. Host: Leo Laporte Guests: Brianna Wu and Alex Kantrowitz Download or subscribe to this show at https://twit.tv/shows/this-week-in-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: UserWay.org/twit wwt.com/twit mintmobile.com/twit policygenius.com/twit

Tech stocks are crumbling, NFTs losing steam, SafeGraph, Google IO preview  Elon Musk raises $7 billion in new funding for Twitter buyout.  Evaluating Elon Musk's Plan To Fix Twitter.  Elon Musk Plans to Take Twitter Public a Few Years After Buyout.  Why Tech Stocks Are Crashing and Burning.  Bitcoin drops below $35,000 over the weekend, extending Friday's losses.  NFT Sales Are Flatlining.  NFTs Are Legally Problematic ft. Steve Mould & Coffeezilla.  Unconventional Success: A Fundamental Approach to Personal Investment by David F. Swensen.  Why the Past 10 Years of American Life Have Been Uniquely Stupid.  Vatican Offers, Mysteriously Rescinds Interview About Pope's Metaverse Plans.  Data Broker SafeGraph Stops Selling Location Data of People Who Visit Planned Parenthood.  CDC Tracked Millions of Phones to See If Americans Followed COVID Lockdown Orders.  Global Privacy Control (GPC).  Google previews I/O 2022 schedule, 'What's new' keynotes, and sessions.  The latest Pixel Watch spec rumors show Google's trying to make a flagship. Ian Goodfellow, Apple's director of machine learning, is leaving the company due to its return to work policy.  AMD sales jump 71%, shrugging off concerns about PC slowdown.  Salt_Hank on TikTok.  Nvidia pays $5.5 million for allegedly hiding how many gaming GPUs were sold to crypto miners.  Intuit to pay $141M settlement over 'free' TurboTax ads.  Frontier lied about Internet speeds and "ripped off customers," FTC says.  New York City sues Activision, targeting CEO Bobby Kotick.  Apple's Self Service Repair now available. Host: Leo Laporte Guests: Brianna Wu and Alex Kantrowitz Download or subscribe to this show at https://twit.tv/shows/this-week-in-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: UserWay.org/twit wwt.com/twit mintmobile.com/twit policygenius.com/twit

Tech stocks are crumbling, NFTs losing steam, SafeGraph, Google IO preview  Elon Musk raises $7 billion in new funding for Twitter buyout.  Evaluating Elon Musk's Plan To Fix Twitter.  Elon Musk Plans to Take Twitter Public a Few Years After Buyout.  Why Tech Stocks Are Crashing and Burning.  Bitcoin drops below $35,000 over the weekend, extending Friday's losses.  NFT Sales Are Flatlining.  NFTs Are Legally Problematic ft. Steve Mould & Coffeezilla.  Unconventional Success: A Fundamental Approach to Personal Investment by David F. Swensen.  Why the Past 10 Years of American Life Have Been Uniquely Stupid.  Vatican Offers, Mysteriously Rescinds Interview About Pope's Metaverse Plans.  Data Broker SafeGraph Stops Selling Location Data of People Who Visit Planned Parenthood.  CDC Tracked Millions of Phones to See If Americans Followed COVID Lockdown Orders.  Global Privacy Control (GPC).  Google previews I/O 2022 schedule, 'What's new' keynotes, and sessions.  The latest Pixel Watch spec rumors show Google's trying to make a flagship. Ian Goodfellow, Apple's director of machine learning, is leaving the company due to its return to work policy.  AMD sales jump 71%, shrugging off concerns about PC slowdown.  Salt_Hank on TikTok.  Nvidia pays $5.5 million for allegedly hiding how many gaming GPUs were sold to crypto miners.  Intuit to pay $141M settlement over 'free' TurboTax ads.  Frontier lied about Internet speeds and "ripped off customers," FTC says.  New York City sues Activision, targeting CEO Bobby Kotick.  Apple's Self Service Repair now available. Host: Leo Laporte Guests: Brianna Wu and Alex Kantrowitz Download or subscribe to this show at https://twit.tv/shows/this-week-in-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit Sponsors: UserWay.org/twit wwt.com/twit mintmobile.com/twit policygenius.com/twit

Ian Goodfellow（イアン・グッドフェロー）、Appleを辞任。リモートワーク方針をめぐって？ MetaとAdobe、中小企業向けトレーニングプログラムで提携 5月17日開始、Adobe Expressを使ったオンライントレーニングプログラム「Express Your Brand」 高校講座の先行トライアルについて

Is antimicrobial resistance (AMR) the greatest threat to human health? In this episode, we discuss how the misuse and overuse of antimicrobials in humans and agriculture have accelerated bacteria, viruses, and other pathogens' ability to mutate and develop resistance against the treatments designed to curb and control them. We talked with molecular biologist Stephen Baker, virologist Ian Goodfellow and infectious disease epidemiologist Caroline Trotter about the magnitude of the problem and how it is not a problem of the future, but of the now. Along the way, we discuss whether post COVID19, are we in a better position now to deal with the next pandemic? Can we predict when it might happen? And if it does happen, will we deal with it any differently?This episode was produced by Nick Saffell, James Dolan, Naomi Clements-Brod and Annie Thwaite. Please take our survey!How did you find us? What do you like about Mind Over Chatter? We want to know. So we put together this survey https://forms.gle/r9CfHpJVUEWrxoyx9. If you could please take a few minutes to fill it out, it would be a big help. Timestamps: [00:00] - Introductions[01:10] - A bit about the guests' research[02:03] - What are antimicrobials and what is antimicrobial resistance (AMR)?[03:00] - How do antimicrobials kill bacteria? How do the chemicals interact and stop a process? How were they discovered? [04:20] - Antibiotic means anti-life. How long have they been around? [05:10] - How does the process of antimicrobial resistance (AMR) work?[06:40] - What are the consequences of antimicrobial resistance? The example of drug-resistant typhoid[08:50] - How do you use vaccines to prevent diseases like drug-resistant typhoid? Vaccines, sanitation, and how vaccination is implemented and reformulated. [10:15] - Is antimicrobial resistance (AMR) the greatest threat to human health? Do we underestimate the impact that antibiotics have had?[11:15] - Do we understand the scale of the resistance out there? What about mortality and morbidity because of antimicrobial resistance?[13:00] - Antimicrobial resistance-specific diseases. What about meningitis? The power of early action?[13:45] - The magnitude of the problem. The terrifying realisation that antimicrobials are irrelevant in some countries because of the sheer amount of biomass of drug resistance out there. [15:00] - The overuse of antimicrobrials, human microbiome and the community of bacteria that live in your body. [15:50] - Does the human microbiome recover from an antibiotic. How antibiotics work - basically an atomic bomb going off. [17:00] - Do we have a full picture of how important a microbiome is. Links to obesity and the long-term effects of early exposure to antibiotics. [17:45] - What is the impact of microbiome variation on vaccines? [19:10] - Have we misused antibiotics? Is this on us? Or is inevitable? [19:45] - Resistance is inevitable. Resistance is reported within two years of a drug being licensed and used. We created is this arms race. This will be known as the antimicrobial era. [21:05] - Do we need a better diagnosis before we administer antimicrobials? [21:45] - The volume use of antimicrobials - healthcare vs agriculture. [22:35] - The overuse of antimicrobials. gentamicin being spread on...

'Genius Makers' - Marketing Artificial Intelligence

In this video I will talk about the Deep Learning book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This is a book about the fundamentals of deep learning and neural networks. I try to explain what a neural network is and how it differs from other Artificial Intelligence approaches. Deep learning is one of the most prosperous fields of AI research currently, many companies use it to help our everyday life. To understand how it works we must start from scratch and that's exactly what I did. I am totally new to this field, and I will try to explain things as I learn, so come with me. I made some animation to this video and I talk about images that I show, so for a better experience you should watch the YouTube video: https://youtu.be/4RtlI1Im6w0 The video I learned from and used its animation: https://youtu.be/aircAruvnKk My playlist about AI: https://www.youtube.com/playlist?list=PL8k7NlvXa9ZmDp_a4XAJVG1jspQkIesgZ Twitter: https://twitter.com/AttilaonthWorld YouTube channel: https://www.youtube.com/channel/UCADpTO2CJBS7HNudJu9-nvg

L'INTELLIGENCE ARTIFICIELLE pour le BUSINESS - Saison 3 -- Présentation de l'épisode -- Julien Mardas est le co-fondateur et CEO de Buster.ai, une start-up française spécialisée le fact checking et la lutte contre les fake news. Son profil linkedin : https://www.linkedin.com/in/julienmardas/ -- Les moments-clés de l'épisode -- Julien Mardas nous explique comment buster.ai agit comme un “anti virus” de l'information. Il témoigne sur différents cas d'usages appliqués à la détection des fake news : - Data reading : on apprend à lire à la machine (sémantique) en utilisant des bases de faits; - Déceler l'intention, le sens caché (NLP); - Qualification de l'information grâce à l'IA. Julien nous parle également du fact checking à TF1. Devant la déferlante d'information provoquée par le web, l'IA de buster.ai (30 algorithmes) est une alliée des journalistes. Buster.ai a été primé lors des assises de la sécurité. Les conseils de lecture de notre invité et de demain.ai : - « Deep learning book » de Ian Goodfellow, Yoshua Bengio et Aaron Courville - « de 0 à 1 » de Peter Thiel - « Deep learning for NLP » MOOC de Stanford -- Sponsor de l'épisode -- dataecriture.fr - Data Ecriture utilise l'intelligence artificielle pour transformer vos données en textes clairs et lisibles. DataEcriture et ses Robot-Rédacteurs sont au service de votre entreprise pour vous permettre d'exploiter pleinement le potentiel de vos données. -- A propos -- En savoir plus sur demain.ai sur www.demain.ai -- La musique du générique a été créée par une IA -- Soundtrack composed by AIVA (Artificial Intelligence Virtual Artist): www.aiva.ai

Ouriel Bettach, Data Scientist depuis plus de 6ans, nous propose un panorama de ses expériences au sein de grands groupes industriels sur des projets de machine learning (ML). On en profite pour faire le bilan sur la façon dont les grands groupes approchent des projets ML et d'évoquer les points bloquants récurrents dans ces projets, avant d'ouvrir sur les challenges qui se dressent à l'horizon.  Points clés ; Avoir une équipe multi-compétences (Software Engineer et Data Scientist) dans une même squad permet de livrer des produits (pas simplement mener des projets) ML plus rapidement. Le data et le model management sont le nerf de la guerre pour répondre aux questions de montée en charge. Le ML Ops est là pour rester. Voir ML Flow. Au-delà du technique, la conduite du changement pour le déploiement d'un produit ML doit être préparée avec les utilisateurs business.  Références Ouriel nous recommande le blog Towards Data Science pour se tenir au courant des dernières tendances du ML. Pour les livres, deux recommandations cette semaine, une lecture sur le data management et un classique du ML :     - Data Management at Scale: Best Practices for Enterprise Architecture de Piethein Strengholt, ISBN 9781492054788     - Deep Learning de Ian Goodfellow, Yoshua Bengio et Aaron Courville, ISBN 9780262035613  En bonus, Ouriel nous recommande chaudement les interventions de Yann Lecun sur l'apprentissage profond.  La transcription de notre discussion est disponible sur le blog du podcast Post Mortem. 

El desarrollo de Ian Goodfellow nos impide diferenciar las creaciones basadas en aprendizaje profundo e inteligencia artificial, de las genuinas. No podremos saber si interactuamos con personas humanas o robots. Imposible salir de la matrix.

Originally published January 31, 2019Artificial intelligence is reshaping every aspect of our lives, from transportation to agriculture to dating. Someday, we may even create a superintelligence–a computer system that is demonstrably smarter than humans. But there is widespread disagreement on how soon we could build a superintelligence. There is not even a broad consensus on how we can define the term “intelligence”.Information technology is improving so rapidly we are losing the ability to forecast the near future. Even the most well-informed politicians and business people are constantly surprised by technological changes, and the downstream impact on society. Today, the most accurate guidance on the pace of technology comes from the scientists and the engineers who are building the tools of our future.Martin Ford is a computer engineer and the author of Architects of Intelligence, a new book of interviews with the top researchers in artificial intelligence. His interviewees include Jeff Dean, Andrew Ng, Demis Hassabis, Ian Goodfellow, and Ray Kurzweil.Architects of Intelligence is a privileged look at how AI is developing. Martin Ford surveys these different AI experts with similar questions. How will China's adoption of AI differ from that of the US? What is the difference between the human brain and that of a computer? What are the low-hanging fruit applications of AI that we have yet to build?Martin joins the show to talk about his new book. In our conversation, Martin synthesizes ideas from these different researchers, and describes the key areas of disagreement from across the field.

In this episode, we talked with Saad Abouzahir, Researcher on Smart Farming & Machine Vision. we talked a lot about how artificial intelligence and machine learning are revolutionizing the field of agriculture and farming. Mentioned resources : - Machine Learning Courseby Andrew Ng - Deep Learning by Aaron Courville, Ian Goodfellow, and Yoshua Bengio. - Python Parallel Programming Cookbook

Встречайте 113-й выпуск подкаста, в котором у меня в гостях Александр Сербул, руководитель направления контроля качества интеграций и внедрений в компании 1С-Битрикс, а так же технологический евангелист. В этом выпуске мы говорим про архитектуру, языки программирования, machine learning, нейросети, облака и многое другое. И нет, не думайте, что этот выпуск только про PHP и 1C-Битрикс! Вначале Саша рассказал про свой довольно насыщенный и тернистый путь в IT, с чем сталкивался, какие задачи приходилось решать и какие роли играть. Саша поделился теми книгами, которые произвели на него сильное впечатление сыграли не последнюю роль в его профессиональных навыках. Саша рассказал про общую архитектуру системы, её компоненты, сервисы, используемые языки и технологии. Отдельно мы обсудили тему облаков, облачных решений, AWS в частности, его плюсы и минусы и возможные альтернативы. Так же Саша рассказал про Rust, чем он так хорош, где нашлось ему место и какую выгоду это принесло. Обсудили мы и тему строгой типизации в различных интерпретируемых языках, хайп вокруг неё и немного подискутировали о том, когда она не очень нужна, а когда без неё уже не обойтись. Большой темой беседы стало машинное обучение. Саша рассказал про то, где у себя в системе они применяют машинное обучение, какие решают задачи с её помощью. Рассказал про используемые алгоритмы, фреймворки, языки и технологии. Не обошли мы стороной и вопрос первого языка программирования. Саша поделился своим мнением на этот счёт. Ссылки на ресурсы по темам выпуска: * Фильмы: * Одержимость (Whiplash) (https://www.kinopoisk.ru/film/725190/) * Общество мертвых поэтов (Dead Poets Society) (https://www.kinopoisk.ru/film/4996/) * Книги: * Архитектура компьютера (https://www.ozon.ru/context/detail/id/20032936/), Таненбаум Э., Остин Т. * Философия Java (https://www.ozon.ru/context/detail/id/4073388/), Эккель Б. * Java. Эффективное программирование (https://www.litres.ru/dzhoshua-bloh/javatm-effektivnoe-programmirovanie-48411247/), Блох Джошуа * Advanced Programming in the UNIX Environment (https://www.amazon.com/Advanced-Programming-UNIX-Environment-3rd/dp/0321637739), Richard Stevens * Deep Learning (http://www.deeplearningbook.org/), Ian Goodfellow and Yoshua Bengio and Aaron Courville * PyTorch (https://pytorch.org/). An open source machine learning framework that accelerates the path from research prototyping to production deployment. * LightFM (http://lyst.github.io/lightfm/docs/home.html) is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback. * Статья «Towards optimal personalization: synthesisizing machine learning and operations research» (https://www.ethanrosenthal.com/2016/08/30/towards-optimal-personalization/) * Paper «Factorization Machines» (pdf) Понравился выпуск? — Поддержи подкаст на patreon.com/KSDaemon (https://www.patreon.com/KSDaemon), звёздочками в iTunes (https://podcasts.apple.com/ru/podcast/software-development-podcast/id890468606?l=en), а так же ретвитом, постом и просто рассказом друзьям!

Show Notes:(2:08) Mael recalled his experience getting a Bachelor of Science Degree in Economics from HEC Lausanne in Switzerland.(4:47) Mael discussed his experience co-founding Wanago, which is the world’s first van acquisition and conversion crowdfunding platform.(9:48) Mael talked about his decision to pursue a Master’s degree in Actuarial Science, also at HEC Lausanne.(11:51) Mael talked about his teaching assistantships experience for courses in Corporate and Public Finance.(13:30) Mael talked about his 6-month internship at Vaudoise Assurances, in which he focused on an individual non-life product pricing.(16:26) Mael gave his insights on the state of adopting new tools in the actuarial science space.(18:12) Mael briefly went over his decision to do a Post Master’s program in Big Data at Telecom Paris, which focuses on statistics, machine learning, deep learning, reinforcement learning, and programming.(20:51) Mael explained the end-to-end process of a deep learning research project for the French employment center on multi-modal emotion recognition, where his team delivered state-of-the-art models in text, sound, and video processing for sentiment analysis (check out the GitHub repo).(26:12) Mael talked about his 6-month part-time internship doing Natural Language Processing for Veamly, a productivity app for engineers.(28:58) Mael talked about his involvement with VIVADATA, a specialized AI programming school in Paris, as a machine learning instructor.(34:18) Mael discussed his current responsibilities at Anasen, a Paris-based startup backed by Y Combinator back in 2017.(38:12) Mael talked about his interest in machine learning for healthcare, and his goal to pursue a Ph.D. degree.(40:00) Mael provided a neat summary on current state of data engineering technologies, referring to his list of in-depth Data Engineering Articles.(42:36) Mael discussed his NoSQL Big Data Project, in which he built a Cassandra architecture for the GDELT database.(47:38) Mael talked about his generic process of writing technical content (check out his Machine Learning Tutorials GitHub Repo).(52:50) Mael discussed 2 machine learning projects that I personally found to be very interesting: (1) a Language Recognition App built using Markov Chains and likelihood decoding algorithms, and (2) the Data Visualization of French traffic accidents database built with D3, Python, Flask, and Altair.(56:13) Mael discussed his resources to learn deep learning (check out his Deep Learning articles on the theory of deep learning, different architectures of deep neural networks, and the applications in Natural Language Processing / Computer Vision).(57:33) Mael mentioned 2 impressive computer vision projects that he did: (1) a series of face classification algorithms using deep learning architectures, and (2) face detection algorithms using OpenCV.(59:47) Mael moved on to talk about his NLP project fsText, a few-shot learning text classification library on GitHub, using pre-trained embeddings and Siamese networks.(01:03:09) Mael went over applications of Reinforcement Learning that he is excited about (check out his recent Reinforcement Learning Articles).(01:05:14) Mael shared his advice for people who want to get into freelance technical writing.(01:06:47) Mael shared his thoughts on the tech and data community in Paris.(01:07:49) Closing segment.His Contact Info:TwitterWebsiteLinkedInGitHubMediumHis Recommended Resources:Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron CourvillePyImageSearch by Adrian RosebrockStation F Incubator in ParisBenevolentAIEconometrics Data Science: A Predictive Modeling Approach by Francis Diebold

Show Notes:(2:08) Mael recalled his experience getting a Bachelor of Science Degree in Economics from HEC Lausanne in Switzerland.(4:47) Mael discussed his experience co-founding Wanago, which is the world’s first van acquisition and conversion crowdfunding platform.(9:48) Mael talked about his decision to pursue a Master’s degree in Actuarial Science, also at HEC Lausanne.(11:51) Mael talked about his teaching assistantships experience for courses in Corporate and Public Finance.(13:30) Mael talked about his 6-month internship at Vaudoise Assurances, in which he focused on an individual non-life product pricing.(16:26) Mael gave his insights on the state of adopting new tools in the actuarial science space.(18:12) Mael briefly went over his decision to do a Post Master’s program in Big Data at Telecom Paris, which focuses on statistics, machine learning, deep learning, reinforcement learning, and programming.(20:51) Mael explained the end-to-end process of a deep learning research project for the French employment center on multi-modal emotion recognition, where his team delivered state-of-the-art models in text, sound, and video processing for sentiment analysis (check out the GitHub repo).(26:12) Mael talked about his 6-month part-time internship doing Natural Language Processing for Veamly, a productivity app for engineers.(28:58) Mael talked about his involvement with VIVADATA, a specialized AI programming school in Paris, as a machine learning instructor.(34:18) Mael discussed his current responsibilities at Anasen, a Paris-based startup backed by Y Combinator back in 2017.(38:12) Mael talked about his interest in machine learning for healthcare, and his goal to pursue a Ph.D. degree.(40:00) Mael provided a neat summary on current state of data engineering technologies, referring to his list of in-depth Data Engineering Articles.(42:36) Mael discussed his NoSQL Big Data Project, in which he built a Cassandra architecture for the GDELT database.(47:38) Mael talked about his generic process of writing technical content (check out his Machine Learning Tutorials GitHub Repo).(52:50) Mael discussed 2 machine learning projects that I personally found to be very interesting: (1) a Language Recognition App built using Markov Chains and likelihood decoding algorithms, and (2) the Data Visualization of French traffic accidents database built with D3, Python, Flask, and Altair.(56:13) Mael discussed his resources to learn deep learning (check out his Deep Learning articles on the theory of deep learning, different architectures of deep neural networks, and the applications in Natural Language Processing / Computer Vision).(57:33) Mael mentioned 2 impressive computer vision projects that he did: (1) a series of face classification algorithms using deep learning architectures, and (2) face detection algorithms using OpenCV.(59:47) Mael moved on to talk about his NLP project fsText, a few-shot learning text classification library on GitHub, using pre-trained embeddings and Siamese networks.(01:03:09) Mael went over applications of Reinforcement Learning that he is excited about (check out his recent Reinforcement Learning Articles).(01:05:14) Mael shared his advice for people who want to get into freelance technical writing.(01:06:47) Mael shared his thoughts on the tech and data community in Paris.(01:07:49) Closing segment.His Contact Info:TwitterWebsiteLinkedInGitHubMediumHis Recommended Resources:Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron CourvillePyImageSearch by Adrian RosebrockStation F Incubator in ParisBenevolentAIEconometrics Data Science: A Predictive Modeling Approach by Francis Diebold

*We discuss, *How AI generated Art is created (it's harder than you think)How to train an AI model to produce results you seekHow does technology affect the future of art, especially if the art can be generated rather easily?How startups can compete in an Ai worldPractical applications of enterprise & consumer dataHow that data is leveraged for the benefits of businesses and consumers.Threats that arise due to a data explosion*Educational References: *Blogs & Books:Distill PubOpen Ai’s blogFair Ai blogGoogle’s Ai blogDeep Learning book by Ian Goodfellow and Yoshua Bengio and Aaron CourvilleBig thank you to Sunny Parikh for making the connection to Paul and making the podcast possible. 

Ian Goodfellow mente at algoritmene måtte kunne bli mye mer kreative enn de var. I en bardiskusjon var det ingen som trodde på at algoritmene kunne bli kreative i konkurranse med seg selv. Han skulle motbevise dem alle.

Episode 31: The Technology Behind Deepfakes ______________________________________________________________ In Episode 29 of Pardon The Disruption, the team discussed the world of Deepfakes. But what is the underlying technology behind Deepfakes? Generative Adversarial Networks (GAN), is extremely interesting and could have profound implications for distorting reality when it comes to generating fake videos or images. Created by the researcher Ian Goodfellow at the age of 28, Generative Adversarial Networks are two artificial neural networks which compete against each other. In the case of deep fake images, one network (the generator) tries to generate an image which the other network discriminates (the discriminator). In essence, the generator is trying to fool the discriminator into believing the data, or image, is fake - and it continues to generate images until this objective is met. - What are Generative Adversarial Networks (GANs)? (2:14) - The role of the Generator and Discriminator in GANs (5:00) - Why Is This Important? (8:50) - Where will GANs be used in the future? (9:25) - Deep fake images are just the start (10:20) - Generating Complete Data (14:00) - Variational Auto-Encoders (What JPG did for image compression) (14:44) - Faking Social Media profiles (20:10) - Machine-Brain Interfaces (21:41) Links used in the show: A Beginner's Guide to GANs: https://skymind.ai/wiki/generative-adversarial-network-gan Play with GANs (The GAN Lab): https://poloclub.github.io/ganlab/ ________________________________________________________________ Leave some feedback: • What should we talk about next? Please let us know on Twitter - twitter.com/rumjog or in the comments below. • Enjoyed this episode? Let us know your thoughts in the comments, and please be sure to subscribe. ⚡️ Subscribe to Podcast: Google Play: bit.ly/2Cl97VS iTunes: apple.co/2SEndI8 Spotify: spoti.fi/2W7OB2N Stitcher: bit.ly/2XXwLkA SoundCloud: bit.ly/2Y0t25Z

Ian Goodfellow is the author of the popular textbook on deep learning (simply titled “Deep Learning”). He coined the term Generative Adversarial Networks (GANs) and with his 2014 paper is responsible for launching the incredible growth of research on GANs. Video version is available on YouTube. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations.

Recorded 7th April 2019 This week was a bit slim on Apple news, or at least until the last minute... Then all of a sudden there were quite a few stories including MagSafe, an ML/AI hire, iTunes rumours and Apple ads on YouTube. Anyway to discuss all of these and whatever else we turn up along the way I am joined by Nick “Spligosh” Riley and original EAP host Mark Chappell. Also I have to give a big shout out to all the slackers who put in stories - I don't always thank them but every week members post possiible stories into the slack, and without them I would never find some of the great things they turn up. Thanks to all of them. GIVEAWAYS & OFFERS Listeners of this show can claim $10 off purchases of Luminar and/or Aurora HD 2019 use the coupon code EssentialApple at checkout for your extra discount! Get Photolemur 2 free by helping this YouTube video to 100,000 views. Why not come and join the Slack community? You can now just click on this Slackroom Link to sign up and join in the chatter! We can now also be found RadioPublic, PlayerFM and TuneIn as well as all the other places previously available. On this week's show: MARK CHAPPELL @oceanspeed on Twitter and sometimes puts Essential Apple related stuff on YouTube NICK RILEY @spligosh on Twitter very occasionally. Sometimes appears on Bart Busschots' Let's Talk Apple APPLE Sonnet eGFX Radeon RX 560 Breakaway Puck hits Apple Store, but you don't have to wait – AppleInsider Apple hires Google AI expert Ian Goodfellow to direct machine learning – VentureBeat Apple is exploring an updated version of MagSafe, one of its best charging inventions ever – Business Insider Apple's Ad About a Scrappy Group of Coworkers Is Honestly Better Than Most Sitcoms - AdWeek Actual video – YouTube This one is good too - “Homework” – YouTube Nexflix Removing AirPlay Support is a Strange and Somewhat Consumer Hostile Move – iPad Insight Rumor: macOS 10.15 may see iTunes broken up into multiple apps – Apple World Today We tend not to get into “rumours” too much but this is Steve Troughton-Smith we're hearing from here... TECHNOLOGY Bad Apple Demo on lots of hardware on YouTube as mentioned by Mark SECURITY & PRIVACY Cloudflare announces Warp: a new free VPN service for iOS – 9to5Mac Understanding Outline – Google's new DIY VPN service – VPN Pro Browser choice screen for Android must offer real alternatives – Cliqz This is an interesting look at the dependencies of browsers etc (based on Android, so hence no Safari etc) but interesting none the less) Russia blocks encrypted mail service provider ProtonMail – DataBreaches.net These Chinese sanitation workers have to wear location-tracking bracelets now – The Verge WORTH A CHIRP / ESSENTIAL TIPS LuLu - the open source Little Snitch “lite” – Objective-See The paid for Little Snitch is far more comprehensive though – Objective Development Privacy Pro SmartVPN by Disconnect JUST A SNIPPET For things that are not worth more than a flypast Apple patents system to help self-driving cars correct slipping tires – Motor Authority Nemo's Hardware Store (1:02:46) iRig Micro Amp – $150 US Direct. Available on Amazon US for $152 US - Not in the UK store at time of writing. Essential Apple Recommended Services: Ghostery - protect yourself from trackers, scripts and ads while browsing. 33mail.com – Never give out your real email address online again. Sudo – Get up to 9 “avatars” with email addresses, phone numbers and more to mask your online identity. Free for the first year and priced from $0.99 US / £2.50 UK per month thereafter... ProtonMail – End to end encrypted, open source, based in Switzerland. Prices start from FREE... what more can you ask? ProtonVPN – a VPN to go with it perhaps? Prices also starting from nothing! Fake Name Generator – So much more than names! Create whole identities (for free) with all the information you could ever need. Wire – Free for personal use, open source and end to end encryted messenger and VoIP. Pinecast – a fabulous podcast hosting service with costs that start from nothing. Essential Apple is not affiliated with or paid to promote any of these services... We recommend services that we use ourselves and feel are either unique or outstanding in their field, or in some cases are just the best value for money in our opinion. Social Media and Slack You can follow us on: Twitter / Slack / EssentialApple.com / Spotify / Soundcloud / YouTube / Facebook / Pinecast Also a big SHOUT OUT to the members of the Slack room without whom we wouldn't have half the stories we actually do – we thank you all for your contributions and engagement. You can always help us out with a few pennies by using our Amazon Affiliate Link so we get a tiny kickback on anything you buy after using it. If you really like the show that much and would like to make a regular donation then please consider joining our Patreon or using the Pinecast Tips Jar (which accepts one off or regular donations) And a HUGE thank you to the patrons who already do. This podcast is powered by Pinecast.

Today, we're excited to continue our Black in AI series with Alvin Grissom II, Assistant Professor of Computer Science at Ursinus College. Alvin’s research is focused on computational linguistics, and we begin with a brief chat about some of his prior work on verb prediction using reinforcement learning. We then dive into the paper he presented at the workshop, “Pathologies of Neural Models Make Interpretations Difficult.” We talk through some of the “pathological behaviors” he identified in the paper, how we can better understand the overconfidence of trained deep learning models in certain settings, and how we can improve model training with entropy regularization. We also touch on the parallel between his work and the work being done on adversarial examples by Ian Goodfellow and others. For the complete show notes, visit https://twimlai.com/talk/229. To follow along with our Black in AI series, visit https://twimlai.com/blackinai19.  

Show Notes: (3:20) Saurabh recalled his college experience. (4:25) Saurabh talked about his first role out of school as a software engineer specializing in database at CA Technologies. (8:06) Saurabh landed database consulting roles with different companies. (9:14) Saurabh gave insights on the differences between database systems now and a decade ago. (11:05) Saurabh shared his experience landing a senior data scientist job with Barnes & Noble. (13:15) Saurabh explained major challenges in hiring good data scientists. (16:10) Saurabh discussed his decision to go work for Rent The Runway. (17:57) Saurabh gave insights on the data problems he had worked with at Rent The Runway. (19:43) In reference to his blog post on scaling machine learning at RTR, Saurabh shared knowledge on structuring a data science team. (21:36) Saurabh gave advice for data scientists to incorporate feedback loops into their workflow. (26:00) Saurabh talked about how to give better pitches to business stakeholders. (29:03) Saurabh showed great appreciation for Rent The Runway’s CEO and Founder, Jennifer Hyman. (30:39) In his post Human in AI loop, Saurabh shared the details of building Deep Dress AI, which can show the Instagram photos on Rent The Runway’s product pages and help the customers find fashion inspiration through these high-quality shots. (34:10) Saurabh talked about unique challenges of doing data science in the retail space. (36:18) Saurabh gave a brief glimpse about his stealth-mode startup Virevol. (36:58) Saurabh gave advice for data scientists who want to pursue the entrepreneurial route and start their own ventures. (38:36) In a fun blog post, Saurabh used Generative Adversarial Network to generate dresses photos and use them as training data for the model. He discussed the potential usage of GAN models for fashion. (41:02) Closing segments. His Contact Info: Website Twitter LinkedIn His recommended resources: Salesforce AI Elucd Nate Silver's "The Signal and The Noise" Deep Learning Textbook by Ian Goodfellow, Yoshua Bengio and Aaron Courville

Show Notes: (3:20) Saurabh recalled his college experience. (4:25) Saurabh talked about his first role out of school as a software engineer specializing in database at CA Technologies. (8:06) Saurabh landed database consulting roles with different companies. (9:14) Saurabh gave insights on the differences between database systems now and a decade ago. (11:05) Saurabh shared his experience landing a senior data scientist job with Barnes & Noble. (13:15) Saurabh explained major challenges in hiring good data scientists. (16:10) Saurabh discussed his decision to go work for Rent The Runway. (17:57) Saurabh gave insights on the data problems he had worked with at Rent The Runway. (19:43) In reference to his blog post on scaling machine learning at RTR, Saurabh shared knowledge on structuring a data science team. (21:36) Saurabh gave advice for data scientists to incorporate feedback loops into their workflow. (26:00) Saurabh talked about how to give better pitches to business stakeholders. (29:03) Saurabh showed great appreciation for Rent The Runway’s CEO and Founder, Jennifer Hyman. (30:39) In his post Human in AI loop, Saurabh shared the details of building Deep Dress AI, which can show the Instagram photos on Rent The Runway’s product pages and help the customers find fashion inspiration through these high-quality shots. (34:10) Saurabh talked about unique challenges of doing data science in the retail space. (36:18) Saurabh gave a brief glimpse about his stealth-mode startup Virevol. (36:58) Saurabh gave advice for data scientists who want to pursue the entrepreneurial route and start their own ventures. (38:36) In a fun blog post, Saurabh used Generative Adversarial Network to generate dresses photos and use them as training data for the model. He discussed the potential usage of GAN models for fashion. (41:02) Closing segments. His Contact Info: Website Twitter LinkedIn His recommended resources: Salesforce AI Elucd Nate Silver's "The Signal and The Noise" Deep Learning Textbook by Ian Goodfellow, Yoshua Bengio and Aaron Courville

Jeff Dean, the lead of Google AI, is on the podcast this week to talk with Melanie and Mark about AI and machine learning research, his upcoming talk at Deep Learning Indaba and his educational pursuit of parallel processing and computer systems was how his career path got him into AI. We covered topics from his team’s work with TPUs and TensorFlow, the impact computer vision and speech recognition is having on AI advancements and how simulations are being used to help advance science in areas like quantum chemistry. We also discussed his passion for the development of AI talent in the content of Africa and the opening of Google AI Ghana. It’s a full episode where we cover a lot of ground. One piece of advice he left us with, “the way to do interesting things is to partner with people who know things you don’t.” Listen for the end of the podcast where our colleague, Gabe Weiss, helps us answer the question of the week about how to get data from IoT core to display in real time on a web front end. Jeff Dean Jeff Dean joined Google in 1999 and is currently a Google Senior Fellow, leading Google AI and related research efforts. His teams are working on systems for speech recognition, computer vision, language understanding, and various other machine learning tasks. He has co-designed/implemented many generations of Google’s crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google’s initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google’s distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, the open-source TensorFlow system for machine learning, and a variety of internal and external libraries and developer tools. Jeff received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on whole-program optimization techniques for object-oriented languages. He received a B.S. in computer science & economics from the University of Minnesota in 1990. He is a member of the National Academy of Engineering, and of the American Academy of Arts and Sciences, a Fellow of the Association for Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Sciences (AAAS), and a winner of the ACM Prize in Computing. Cool things of the week Google Dataset Search is in beta site Expanding our Public Datasets for geospatial and ML-based analytics blog Zip Code Tabulation Area (ZCTA) site Google AI and Kaggle Inclusive Images Challenge site We are rated in the top 100 technology podcasts on iTunes site What makes TPUs fine-tuned for deep learning? blog Interview Jeff Dean on Google AI profile Deep Learning Indaba site Google AI site Google AI in Ghana blog Google Brain site Google Cloud site DeepMind site Cloud TPU site Google I/O Effective ML with Cloud TPUs video Liquid cooling system article DAWNBench Results site Waymo (Alphabet’s Autonomous Car) site DeepMind AlphaGo site Open AI Dota 2 blog Moustapha Cisse profile Sanjay Ghemawat profile Neural Information Processing Systems Conference site Previous Podcasts GCP Podcast Episode 117: Cloud AI with Dr. Fei-Fei Li podcast GCP Podcast Episode 136: Robotics, Navigation, and Reinforcement Learning with Raia Hadsell podcast TWiML & AI Systems and Software for ML at Scale with Jeff Dean podcast Additional Resources arXiv.org site Chris Olah blog Distill Journal site Google’s Machine Learning Crash Course site Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville book and site NAE Grand Challenges for Engineering site Senior Thesis Parallel Implementations of Neural Network Training: Two Back-Propagation Approaches by Jeff Dean paper and tweet Machine Learning for Systems and Systems for Machine Learning slides Question of the week How do I get data from IoT core to display in real time on a web front end? Building IoT Applications on Google Cloud video MQTT site Cloud Pub/Sub site Cloud Functions site Cloud Firestore site Where can you find us next? Melanie is at Deep Learning Indaba and Mark is at Tokyo NEXT. We’ll both be at Strangeloop end of the month. Gabe will be at Cloud Next London and the IoT World Congress.

In this podcast Mike Tamir (@MikeTamir, Head of #DataScience) talked about building a data science AI team. He shared his AI project (FakerFact.org). He shared the lifecycle of an AI project and some things that leaders could keep in mind to help create a successful data science AI team. This podcast is great for leaders learning to build a strong AI workforce. TIMELINE: 0:28 Micheal's journey. 2:36 Micheal's current role. 3:18 AI and businesses. 5:28 Parameters to consider for AI adoption. 9:30 When do businesses invest in ML resources. 13:20 Tips for candidates in vetting data companies. 16:05 What's the faker fact? 20:45 Getting started on an AI product design. 24:58 Achieving accuracy in data. 27:40 AI the newsmaker and AI the fact-checker. 33:56 Tips for hiring the right data leader for a business. 35:32 Creating a great data science team. 37:19 Challenges in forming a data science team. 39:00 In job training to achieve technological competence. 44:00 Ingredients of a good hire. 47:35 Micheal's secret to success. 50:55 Micheal's favorite reads. 54:20 Key takeaways. Mike's Recommended Read: What Technology Wants by Kevin Kelly https://amzn.to/2MaNiuN Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville http://www.deeplearningbook.org/ Podcast Link: https://futureofdata.org/building-data-science-ai-teams-by-miketamir-uberatg-futureofdata-podcast/ Mike's BIO: Mike serves as Head of Data Science at Uber ATG, UC Berkeley Data Science faculty, and head of Phronesis ML Labs. He has led teams of Data Scientists in the bay area as Chief Data Scientist for InterTrust and Takt, Director of Data Sciences for MetaScale/Sears, and CSO for Galvanize, where he founded the galvanizeU-UNH accredited Masters of Science in Data Science degree and oversaw the company's transformation from co-working space to Data Science organization. Mike's most recent passion in research has involved applying Machine Learning techniques to help combat fake news through the FakerFact.org project About #Podcast: #FutureOfData podcast is a conversation starter to bring leaders, influencers and lead practitioners to discuss their journey to create the data-driven future. Wanna Join? If you or any you know wants to join in, Register your interest @ https://analyticsweek.com/ Want to sponsor? Email us @ info@analyticsweek.com Keywords: #FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

This week Connor, Jack and Andrew are joined by Professor Ian Goodfellow from the University of Cambridge to hear about his career so far in virology and his recent work in helping stop viruses in Africa including during the recent West African Ebola outbreak. If you like this podcast check out some of our previous content about viruses like ebola virus over at cvrblogs.myportfolio.com. Music: The Zeppelin by Blue Dot Sessions (freemusicarchive.org/music/Blue_Dot…_Zeppelin_1908)

You can never see through someone else's eyes, but can we, by stealth, tap into people's visual imaginations? The mind's eye is something most of us take for granted - the 'secret cinema' inside our mind, turning sounds into shapes, characters into faces - it sometimes seems like a sixth sense. For those who have it. Constantly viewing our own personal visuals, we are powerless to control it, and no one else can see it but us. "A man hitting his head with a bible" or "A tree being chopped down"? "A row of frogs" or "The bulging eyes of Malcolm McDowell in A Clockwork Orange" Using a series of soundscapes, we hear the visual musings of a range of people: an architect, a school boy, a DJ, an artist amongst them - playing with the way people's own personal experiences influence their mental pictures. But what about those who have no pictures in their brain? "In my late 20's I was on a management course doing a relaxation exercise, and they asked us to imagine dawn. And I thought dawn? Well I know it's pink. But I couldn't see it, I couldn't imagine it." Gill Morgan, doctor First recognised, but not named in 1880 by Francis Galton, aphantasia, as Professor of Cognitive and Behavioural Neurology Adam Zeman has recently called it, is being explored by neuroscientists around the world. It may affect 2% of the population, and studies have shown that there is a sliding scale of non-imagers. Some barely notice any difference in their relationship with their own personal history, but for others this may include an inability to recall life events. "From talking to close friends it became obvious to me that 'the mind's eye' was not a figure of speech, phrases like, 'it takes you back' exist because that's what they do". Nick Watkins, theoretical physicist Encouraging Radio 3 listeners to become aware of their own 'secret cinema', 'Between The Ears' trepans into the little grey cells that bring imagination to light - giving a glimpse inside the film-reel unspooling in our brains. Contributors: Professor Adam Zeman, Doctor Nick Watkins, Dame Gill Morgan, Michael Bywater The voices of Susan Aldworth, Francesca Vinti, Luca Goodfellow, Emma Kilbey, Ford Hickson, Ian Goodfellow, Danny Webb and readings by John Dougall and Dilly Barlow. Soundscapes featuring Alexander Frater in Goa in the monsoon Artwork by kind permission of artist Susan Aldworth. Music sourced by Danny Webb. Producer: Sara Jane Hall.

In this episode, I’m joined by Ian Goodfellow, Staff Research Scientist at Google Brain and Sandy Huang, Phd Student in the EECS department at UC Berkeley, to discuss their work on the paper Adversarial Attacks on Neural Network Policies. If you’re a regular listener here you’ve probably heard of adversarial attacks, and have seen examples of deep learning based object detectors that can be fooled into thinking that, for example, a giraffe is actually a school bus, by injecting some imperceptible noise into the image. Well, Sandy and Ian’s paper sits at the intersection of adversarial attacks and reinforcement learning, another area we’ve discussed quite a bit on the podcast. In their paper, they describe how adversarial attacks can also be effective at targeting neural network policies in reinforcement learning. Sandy gives us an overview of the paper, including how changing a single pixel value can throw off performance of a model trained to play Atari games. We also cover a lot of interesting topics relating to adversarial attacks and RL individually, and some related areas such as hierarchical reward functions and transfer learning. This was a great conversation that I’m really excited to bring to you! For complete show notes, head over to twimlai.com/talk/119

In this episode, I speak with Arthur Gretton, Wittawat Jitkrittum, Zoltan Szabo and Kenji Fukumizu, who, alongside Wenkai Xu authored the 2017 NIPS Best Paper Award winner “A Linear-Time Kernel Goodness-of-Fit Test.” In our discussion, we cover what exactly a “goodness of fit” test is, and how it can be used to determine how well a statistical model applies to a given real-world scenario. The group and I the discuss this particular test, the applications of this work, as well as how this work fits in with other research the group has recently published. Enjoy! In our discussion, we cover what exactly a “goodness of fit” test is, and how it can be used to determine how well a statistical model applies to a given real-world scenario. The group and I the discuss this particular test, the applications of this work, as well as how this work fits in with other research the group has recently published. Enjoy! This is your last chance to register for the RE•WORK Deep Learning and AI Assistant Summits in San Francisco, which are this Thursday and Friday, January 25th and 26th. These events feature leading researchers and technologists like the ones you heard in our Deep Learning Summit series last week. The San Francisco will event is headlined by Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration. The notes for this show can be found at twimlai.com/talk/100.

In this episode I speak with Tuomas Sandholm, Carnegie Mellon University Professor and Founder and CEO of startups Optimized Markets and Strategic Machine. Tuomas, along with his PhD student Noam Brown, won a 2017 NIPS Best Paper award for their paper “Safe and Nested Subgame Solving for Imperfect-Information Games.” Tuomas and I dig into the significance of the paper, including a breakdown of perfect vs imperfect information games, the role of abstractions in game solving, and how the concept of safety applies to gameplay. We discuss how all these elements and techniques are applied to poker, and how the algorithm described in this paper was used by Noam and Tuomas to create Libratus, the first AI to beat top human pros in No Limit Texas Hold’em, a particularly difficult game to beat due to its large state space. This was a fascinating interview that I'm really excited to share with you all. Enjoy! This is your last chance to register for the RE•WORK Deep Learning and AI Assistant Summits in San Francisco, which are this Thursday and Friday, January 25th and 26th. These events feature leading researchers and technologists like the ones you heard in our Deep Learning Summit series last week. The San Francisco will event is headlined by Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration. The notes for this show can be found at twimlai.com/talk/99

In today’s show, I sit down with Eric Humphrey, Research Scientist in the music understanding group at Spotify. Eric was at the Deep Learning Summit to give a talk on Advances in Deep Architectures and Methods for Separating Vocals in Recorded Music. We discuss his talk, including how Spotify's large music catalog enables such an experiment to even take place, the methods they use to train algorithms to isolate and remove vocals from music, and how architectures like U-Net and Pix2Pix come into play when building his algorithms. We also hit on the idea of “creative AI,” Spotify’s attempt at understanding music content at scale, optical music recognition, and more. This show is part of a series of shows recorded at the RE•WORK Deep Learning Summit in Montreal back in October. This was a great event and, in fact, their next event, the Deep Learning Summit San Francisco is right around the corner on January 25th and 26th, and will feature more leading researchers and technologists like the ones you’ll hear here on the show this week, including Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration. The notes for this show can be found at twimlai.com/talk/98

In this show I speak with Greg Diamos, senior computer systems researcher at Baidu. Greg joined me before his talk at the Deep Learning Summit, where he spoke on “The Next Generation of AI Chips.” Greg’s talk focused on some work his team was involved in that accelerates deep learning training by using mixed 16-bit and 32-bit floating point arithmetic. We cover a ton of interesting ground in this conversation, and if you’re interested in systems level thinking around scaling and accelerating deep learning, you’re really going to like this one. And of course, if you like this one, you’re also going to like TWiML Talk #14 with Greg’s former colleague, Shubho Sengupta, which covers a bunch of related topics. This show is part of a series of shows recorded at the RE•WORK Deep Learning Summit in Montreal back in October. This was a great event and, in fact, their next event, the Deep Learning Summit San Francisco is right around the corner on January 25th and 26th, and will feature more leading researchers and technologists like the ones you’ll hear here on the show this week, including Ian Goodfellow of Google Brain, Daphne Koller of Calico Labs, and more! Definitely check it out and use the code TWIMLAI for 20% off of registration.

How an argument in a bar led Google's Ian Goodfellow to create Generative Adversarial Networks - deep learning systems that argue with each other - an AI breakthrough that promises to help researchers build systems that can learn with less human intervention.

2016 saw some significant AI developments. To talk about the AI progress of the last year, we turned to Richard Mallah and Ian Goodfellow. Richard is the director of AI projects at FLI, he’s the Senior Advisor to multiple AI companies, and he created the highest-rated enterprise text analytics platform. Ian is a research scientist at OpenAI, he’s the lead author of a deep learning textbook, and he’s the inventor of Generative Adversarial Networks. Listen to the podcast here or review the transcript here.

Alberto Todeschini, Faculty Director of AI at UC Berkeley, I-School, discusses What You can Do to Reduce the Dangers of AI.-Guest speaker: Alberto Todeschini, Faculty Director in Artificial Intelligence at UC Berkeley School of Information.-Symantec, develops security products and solutions to SMEs including sophisticated threats such as cyber and malware.-Deepfakes emerge in the discussion with the example of Obama’s edited video by actor & directorJordan Peele.-JibJab, is an online service that offers personalized eCards and short videos for all occasions.-Ian Goodfellow(http://www.iangoodfellow.com), is the director of machine learning, special projects group at Apple and previously worked as a research scientist at Google Brain. He invented Generative Adversarial Networks.-Todeschini explores the dangers of AI by citing the example of Tesla, cars slamming into walls.