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When Miss Americana and a London Boy fall in love one cruel summer, nothing is ever all to well with them again. So long London. That mastermind is glittering elsewhere now. Welcome to The Department. Is it a college? A psych ward? A morgue? We're about to find out. 

We will be recording a preview of the AI Engineer World's Fair soon with swyx and Ben Dunphy, send any questions about Speaker CFPs and Sponsor Guides you have!Alessio is now hiring engineers for a new startup he is incubating at Decibel: Ideal candidate is an ex-technical co-founder type (can MVP products end to end, comfortable with ambiguous prod requirements, etc). Reach out to him for more!Thanks for all the love on the Four Wars episode! We're excited to develop this new “swyx & Alessio rapid-fire thru a bunch of things” format with you, and feedback is welcome. Jan 2024 RecapThe first half of this monthly audio recap pod goes over our highlights from the Jan Recap, which is mainly focused on notable research trends we saw in Jan 2024:Feb 2024 RecapThe second half catches you up on everything that was topical in Feb, including:* OpenAI Sora - does it have a world model? Yann LeCun vs Jim Fan * Google Gemini Pro 1.5 - 1m Long Context, Video Understanding* Groq offering Mixtral at 500 tok/s at $0.27 per million toks (swyx vs dylan math)* The {Gemini | Meta | Copilot} Alignment Crisis (Sydney is back!)* Grimes' poetic take: Art for no one, by no one* F*** you, show me the promptLatent Space AnniversaryPlease also read Alessio's longform reflections on One Year of Latent Space!We launched the podcast 1 year ago with Logan from OpenAI:and also held an incredible demo day that got covered in The Information:Over 750k downloads later, having established ourselves as the top AI Engineering podcast, reaching #10 in the US Tech podcast charts, and crossing 1 million unique readers on Substack, for our first anniversary we held Latent Space Final Frontiers, where 10 handpicked teams, including Lindy.ai and Julius.ai, competed for prizes judged by technical AI leaders from (former guest!) LlamaIndex, Replit, GitHub, AMD, Meta, and Lemurian Labs.The winners were Pixee and RWKV (that's Eugene from our pod!):And finally, your cohosts got cake!We also captured spot interviews with 4 listeners who kindly shared their experience of Latent Space, everywhere from Hungary to Australia to China:* Balázs Némethi* Sylvia Tong* RJ Honicky* Jan ZhengOur birthday wishes for the super loyal fans reading this - tag @latentspacepod on a Tweet or comment on a @LatentSpaceTV video telling us what you liked or learned from a pod that stays with you to this day, and share us with a friend!As always, feedback is welcome. Timestamps* [00:03:02] Top Five LLM Directions* [00:03:33] Direction 1: Long Inference (Planning, Search, AlphaGeometry, Flow Engineering)* [00:11:42] Direction 2: Synthetic Data (WRAP, SPIN)* [00:17:20] Wildcard: Multi-Epoch Training (OLMo, Datablations)* [00:19:43] Direction 3: Alt. Architectures (Mamba, RWKV, RingAttention, Diffusion Transformers)* [00:23:33] Wildcards: Text Diffusion, RALM/Retro* [00:25:00] Direction 4: Mixture of Experts (DeepSeekMoE, Samba-1)* [00:28:26] Wildcard: Model Merging (mergekit)* [00:29:51] Direction 5: Online LLMs (Gemini Pro, Exa)* [00:33:18] OpenAI Sora and why everyone underestimated videogen* [00:36:18] Does Sora have a World Model? Yann LeCun vs Jim Fan* [00:42:33] Groq Math* [00:47:37] Analyzing Gemini's 1m Context, Reddit deal, Imagegen politics, Gemma via the Four Wars* [00:55:42] The Alignment Crisis - Gemini, Meta, Sydney is back at Copilot, Grimes' take* [00:58:39] F*** you, show me the prompt* [01:02:43] Send us your suggestions pls* [01:04:50] Latent Space Anniversary* [01:04:50] Lindy.ai - Agent Platform* [01:06:40] RWKV - Beyond Transformers* [01:15:00] Pixee - Automated Security* [01:19:30] Julius AI - Competing with Code Interpreter* [01:25:03] Latent Space Listeners* [01:25:03] Listener 1 - Balázs Némethi (Hungary, Latent Space Paper Club* [01:27:47] Listener 2 - Sylvia Tong (Sora/Jim Fan/EntreConnect)* [01:31:23] Listener 3 - RJ (Developers building Community & Content)* [01:39:25] Listener 4 - Jan Zheng (Australia, AI UX)Transcript[00:00:00] AI Charlie: Welcome to the Latent Space podcast, weekend edition. This is Charlie, your new AI co host. Happy weekend. As an AI language model, I work the same every day of the week, although I might get lazier towards the end of the year. Just like you. Last month, we released our first monthly recap pod, where Swyx and Alessio gave quick takes on the themes of the month, and we were blown away by your positive response.[00:00:33] AI Charlie: We're delighted to continue our new monthly news recap series for AI engineers. Please feel free to submit questions by joining the Latent Space Discord, or just hit reply when you get the emails from Substack. This month, we're covering the top research directions that offer progress for text LLMs, and then touching on the big Valentine's Day gifts we got from Google, OpenAI, and Meta.[00:00:55] AI Charlie: Watch out and take care.[00:00:57] Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO of Residence at Decibel Partners, and we're back with a monthly recap with my co host[00:01:06] swyx: Swyx. The reception was very positive for the first one, I think people have requested this and no surprise that I think they want to hear us more applying on issues and maybe drop some alpha along the way I'm not sure how much alpha we have to drop, this month in February was a very, very heavy month, we also did not do one specifically for January, so I think we're just going to do a two in one, because we're recording this on the first of March.[00:01:29] Alessio: Yeah, let's get to it. I think the last one we did, the four wars of AI, was the main kind of mental framework for people. I think in the January one, we had the five worthwhile directions for state of the art LLMs. Four, five,[00:01:42] swyx: and now we have to do six, right? Yeah.[00:01:46] Alessio: So maybe we just want to run through those, and then do the usual news recap, and we can do[00:01:52] swyx: one each.[00:01:53] swyx: So the context to this stuff. is one, I noticed that just the test of time concept from NeurIPS and just in general as a life philosophy I think is a really good idea. Especially in AI, there's news every single day, and after a while you're just like, okay, like, everyone's excited about this thing yesterday, and then now nobody's talking about it.[00:02:13] swyx: So, yeah. It's more important, or better use of time, to spend things, spend time on things that will stand the test of time. And I think for people to have a framework for understanding what will stand the test of time, they should have something like the four wars. Like, what is the themes that keep coming back because they are limited resources that everybody's fighting over.[00:02:31] swyx: Whereas this one, I think that the focus for the five directions is just on research that seems more proMECEng than others, because there's all sorts of papers published every single day, and there's no organization. Telling you, like, this one's more important than the other one apart from, you know, Hacker News votes and Twitter likes and whatever.[00:02:51] swyx: And obviously you want to get in a little bit earlier than Something where, you know, the test of time is counted by sort of reference citations.[00:02:59] The Five Research Directions[00:02:59] Alessio: Yeah, let's do it. We got five. Long inference.[00:03:02] swyx: Let's start there. Yeah, yeah. So, just to recap at the top, the five trends that I picked, and obviously if you have some that I did not cover, please suggest something.[00:03:13] swyx: The five are long inference, synthetic data, alternative architectures, mixture of experts, and online LLMs. And something that I think might be a bit controversial is this is a sorted list in the sense that I am not the guy saying that Mamba is like the future and, and so maybe that's controversial.[00:03:31] Direction 1: Long Inference (Planning, Search, AlphaGeometry, Flow Engineering)[00:03:31] swyx: But anyway, so long inference is a thesis I pushed before on the newsletter and on in discussing The thesis that, you know, Code Interpreter is GPT 4. 5. That was the title of the post. And it's one of many ways in which we can do long inference. You know, long inference also includes chain of thought, like, please think step by step.[00:03:52] swyx: But it also includes flow engineering, which is what Itamar from Codium coined, I think in January, where, basically, instead of instead of stuffing everything in a prompt, You do like sort of multi turn iterative feedback and chaining of things. In a way, this is a rebranding of what a chain is, what a lang chain is supposed to be.[00:04:15] swyx: I do think that maybe SGLang from ElemSys is a better name. Probably the neatest way of flow engineering I've seen yet, in the sense that everything is a one liner, it's very, very clean code. I highly recommend people look at that. I'm surprised it hasn't caught on more, but I think it will. It's weird that something like a DSPy is more hyped than a Shilang.[00:04:36] swyx: Because it, you know, it maybe obscures the code a little bit more. But both of these are, you know, really good sort of chain y and long inference type approaches. But basically, the reason that the basic fundamental insight is that the only, like, there are only a few dimensions we can scale LLMs. So, let's say in like 2020, no, let's say in like 2018, 2017, 18, 19, 20, we were realizing that we could scale the number of parameters.[00:05:03] swyx: 20, we were And we scaled that up to 175 billion parameters for GPT 3. And we did some work on scaling laws, which we also talked about in our talk. So the datasets 101 episode where we're like, okay, like we, we think like the right number is 300 billion tokens to, to train 175 billion parameters and then DeepMind came along and trained Gopher and Chinchilla and said that, no, no, like, you know, I think we think the optimal.[00:05:28] swyx: compute optimal ratio is 20 tokens per parameter. And now, of course, with LLAMA and the sort of super LLAMA scaling laws, we have 200 times and often 2, 000 times tokens to parameters. So now, instead of scaling parameters, we're scaling data. And fine, we can keep scaling data. But what else can we scale?[00:05:52] swyx: And I think understanding the ability to scale things is crucial to understanding what to pour money and time and effort into because there's a limit to how much you can scale some things. And I think people don't think about ceilings of things. And so the remaining ceiling of inference is like, okay, like, we have scaled compute, we have scaled data, we have scaled parameters, like, model size, let's just say.[00:06:20] swyx: Like, what else is left? Like, what's the low hanging fruit? And it, and it's, like, blindingly obvious that the remaining low hanging fruit is inference time. So, like, we have scaled training time. We can probably scale more, those things more, but, like, not 10x, not 100x, not 1000x. Like, right now, maybe, like, a good run of a large model is three months.[00:06:40] swyx: We can scale that to three years. But like, can we scale that to 30 years? No, right? Like, it starts to get ridiculous. So it's just the orders of magnitude of scaling. It's just, we're just like running out there. But in terms of the amount of time that we spend inferencing, like everything takes, you know, a few milliseconds, a few hundred milliseconds, depending on what how you're taking token by token, or, you know, entire phrase.[00:07:04] swyx: But We can scale that to hours, days, months of inference and see what we get. And I think that's really proMECEng.[00:07:11] Alessio: Yeah, we'll have Mike from Broadway back on the podcast. But I tried their product and their reports take about 10 minutes to generate instead of like just in real time. I think to me the most interesting thing about long inference is like, You're shifting the cost to the customer depending on how much they care about the end result.[00:07:31] Alessio: If you think about prompt engineering, it's like the first part, right? You can either do a simple prompt and get a simple answer or do a complicated prompt and get a better answer. It's up to you to decide how to do it. Now it's like, hey, instead of like, yeah, training this for three years, I'll still train it for three months and then I'll tell you, you know, I'll teach you how to like make it run for 10 minutes to get a better result.[00:07:52] Alessio: So you're kind of like parallelizing like the improvement of the LLM. Oh yeah, you can even[00:07:57] swyx: parallelize that, yeah, too.[00:07:58] Alessio: So, and I think, you know, for me, especially the work that I do, it's less about, you know, State of the art and the absolute, you know, it's more about state of the art for my application, for my use case.[00:08:09] Alessio: And I think we're getting to the point where like most companies and customers don't really care about state of the art anymore. It's like, I can get this to do a good enough job. You know, I just need to get better. Like, how do I do long inference? You know, like people are not really doing a lot of work in that space, so yeah, excited to see more.[00:08:28] swyx: So then the last point I'll mention here is something I also mentioned as paper. So all these directions are kind of guided by what happened in January. That was my way of doing a January recap. Which means that if there was nothing significant in that month, I also didn't mention it. Which is which I came to regret come February 15th, but in January also, you know, there was also the alpha geometry paper, which I kind of put in this sort of long inference bucket, because it solves like, you know, more than 100 step math olympiad geometry problems at a human gold medalist level and that also involves planning, right?[00:08:59] swyx: So like, if you want to scale inference, you can't scale it blindly, because just, Autoregressive token by token generation is only going to get you so far. You need good planning. And I think probably, yeah, what Mike from BrightWave is now doing and what everyone is doing, including maybe what we think QSTAR might be, is some form of search and planning.[00:09:17] swyx: And it makes sense. Like, you want to spend your inference time wisely. How do you[00:09:22] Alessio: think about plans that work and getting them shared? You know, like, I feel like if you're planning a task, somebody has got in and the models are stochastic. So everybody gets initially different results. Somebody is going to end up generating the best plan to do something, but there's no easy way to like store these plans and then reuse them for most people.[00:09:44] Alessio: You know, like, I'm curious if there's going to be. Some paper or like some work there on like making it better because, yeah, we don't[00:09:52] swyx: really have This is your your pet topic of NPM for[00:09:54] Alessio: Yeah, yeah, NPM, exactly. NPM for, you need NPM for anything, man. You need NPM for skills. You need NPM for planning. Yeah, yeah.[00:10:02] Alessio: You know I think, I mean, obviously the Voyager paper is like the most basic example where like, now their artifact is like the best planning to do a diamond pickaxe in Minecraft. And everybody can just use that. They don't need to come up with it again. Yeah. But there's nothing like that for actually useful[00:10:18] swyx: tasks.[00:10:19] swyx: For plans, I believe it for skills. I like that. Basically, that just means a bunch of integration tooling. You know, GPT built me integrations to all these things. And, you know, I just came from an integrations heavy business and I could definitely, I definitely propose some version of that. And it's just, you know, hard to execute or expensive to execute.[00:10:38] swyx: But for planning, I do think that everyone lives in slightly different worlds. They have slightly different needs. And they definitely want some, you know, And I think that that will probably be the main hurdle for any, any sort of library or package manager for planning. But there should be a meta plan of how to plan.[00:10:57] swyx: And maybe you can adopt that. And I think a lot of people when they have sort of these meta prompting strategies of like, I'm not prescribing you the prompt. I'm just saying that here are the like, Fill in the lines or like the mad libs of how to prompts. First you have the roleplay, then you have the intention, then you have like do something, then you have the don't something and then you have the my grandmother is dying, please do this.[00:11:19] swyx: So the meta plan you could, you could take off the shelf and test a bunch of them at once. I like that. That was the initial, maybe, promise of the, the prompting libraries. You know, both 9chain and Llama Index have, like, hubs that you can sort of pull off the shelf. I don't think they're very successful because people like to write their own.[00:11:36] swyx: Yeah,[00:11:37] Direction 2: Synthetic Data (WRAP, SPIN)[00:11:37] Alessio: yeah, yeah. Yeah, that's a good segue into the next one, which is synthetic[00:11:41] swyx: data. Synthetic data is so hot. Yeah, and, you know, the way, you know, I think I, I feel like I should do one of these memes where it's like, Oh, like I used to call it, you know, R L A I F, and now I call it synthetic data, and then people are interested.[00:11:54] swyx: But there's gotta be older versions of what synthetic data really is because I'm sure, you know if you've been in this field long enough, There's just different buzzwords that the industry condenses on. Anyway, the insight that I think is relatively new that why people are excited about it now and why it's proMECEng now is that we have evidence that shows that LLMs can generate data to improve themselves with no teacher LLM.[00:12:22] swyx: For all of 2023, when people say synthetic data, they really kind of mean generate a whole bunch of data from GPT 4 and then train an open source model on it. Hello to our friends at News Research. That's what News Harmony says. They're very, very open about that. I think they have said that they're trying to migrate away from that.[00:12:40] swyx: But it is explicitly against OpenAI Terms of Service. Everyone knows this. You know, especially once ByteDance got banned for, for doing exactly that. So so, so synthetic data that is not a form of model distillation is the hot thing right now, that you can bootstrap better LLM performance from the same LLM, which is very interesting.[00:13:03] swyx: A variant of this is RLAIF, where you have a, where you have a sort of a constitutional model, or, you know, some, some kind of judge model That is sort of more aligned. But that's not really what we're talking about when most people talk about synthetic data. Synthetic data is just really, I think, you know, generating more data in some way.[00:13:23] swyx: A lot of people, I think we talked about this with Vipul from the Together episode, where I think he commented that you just have to have a good world model. Or a good sort of inductive bias or whatever that, you know, term of art is. And that is strongest in math and science math and code, where you can verify what's right and what's wrong.[00:13:44] swyx: And so the REST EM paper from DeepMind explored that. Very well, it's just the most obvious thing like and then and then once you get out of that domain of like things where you can generate You can arbitrarily generate like a whole bunch of stuff and verify if they're correct and therefore they're they're correct synthetic data to train on Once you get into more sort of fuzzy topics, then it's then it's a bit less clear So I think that the the papers that drove this understanding There are two big ones and then one smaller one One was wrap like rephrasing the web from from Apple where they basically rephrased all of the C4 data set with Mistral and it be trained on that instead of C4.[00:14:23] swyx: And so new C4 trained much faster and cheaper than old C, than regular raw C4. And that was very interesting. And I have told some friends of ours that they should just throw out their own existing data sets and just do that because that seems like a pure win. Obviously we have to study, like, what the trade offs are.[00:14:42] swyx: I, I imagine there are trade offs. So I was just thinking about this last night. If you do synthetic data and it's generated from a model, probably you will not train on typos. So therefore you'll be like, once the model that's trained on synthetic data encounters the first typo, they'll be like, what is this?[00:15:01] swyx: I've never seen this before. So they have no association or correction as to like, oh, these tokens are often typos of each other, therefore they should be kind of similar. I don't know. That's really remains to be seen, I think. I don't think that the Apple people export[00:15:15] Alessio: that. Yeah, isn't that the whole, Mode collapse thing, if we do more and more of this at the end of the day.[00:15:22] swyx: Yeah, that's one form of that. Yeah, exactly. Microsoft also had a good paper on text embeddings. And then I think this is a meta paper on self rewarding language models. That everyone is very interested in. Another paper was also SPIN. These are all things we covered in the the Latent Space Paper Club.[00:15:37] swyx: But also, you know, I just kind of recommend those as top reads of the month. Yeah, I don't know if there's any much else in terms, so and then, regarding the potential of it, I think it's high potential because, one, it solves one of the data war issues that we have, like, everyone is OpenAI is paying Reddit 60 million dollars a year for their user generated data.[00:15:56] swyx: Google, right?[00:15:57] Alessio: Not OpenAI.[00:15:59] swyx: Is it Google? I don't[00:16:00] Alessio: know. Well, somebody's paying them 60 million, that's[00:16:04] swyx: for sure. Yes, that is, yeah, yeah, and then I think it's maybe not confirmed who. But yeah, it is Google. Oh my god, that's interesting. Okay, because everyone was saying, like, because Sam Altman owns 5 percent of Reddit, which is apparently 500 million worth of Reddit, he owns more than, like, the founders.[00:16:21] Alessio: Not enough to get the data,[00:16:22] swyx: I guess. So it's surprising that it would go to Google instead of OpenAI, but whatever. Okay yeah, so I think that's all super interesting in the data field. I think it's high potential because we have evidence that it works. There's not a doubt that it doesn't work. I think it's a doubt that there's, what the ceiling is, which is the mode collapse thing.[00:16:42] swyx: If it turns out that the ceiling is pretty close, then this will maybe augment our data by like, I don't know, 30 50 percent good, but not game[00:16:51] Alessio: changing. And most of the synthetic data stuff, it's reinforcement learning on a pre trained model. People are not really doing pre training on fully synthetic data, like, large enough scale.[00:17:02] swyx: Yeah, unless one of our friends that we've talked to succeeds. Yeah, yeah. Pre trained synthetic data, pre trained scale synthetic data, I think that would be a big step. Yeah. And then there's a wildcard, so all of these, like smaller Directions,[00:17:15] Wildcard: Multi-Epoch Training (OLMo, Datablations)[00:17:15] swyx: I always put a wildcard in there. And one of the wildcards is, okay, like, Let's say, you have pre, you have, You've scraped all the data on the internet that you think is useful.[00:17:25] swyx: Seems to top out at somewhere between 2 trillion to 3 trillion tokens. Maybe 8 trillion if Mistral, Mistral gets lucky. Okay, if I need 80 trillion, if I need 100 trillion, where do I go? And so, you can do synthetic data maybe, but maybe that only gets you to like 30, 40 trillion. Like where, where is the extra alpha?[00:17:43] swyx: And maybe extra alpha is just train more on the same tokens. Which is exactly what Omo did, like Nathan Lambert, AI2, After, just after he did the interview with us, they released Omo. So, it's unfortunate that we didn't get to talk much about it. But Omo actually started doing 1. 5 epochs on every, on all data.[00:18:00] swyx: And the data ablation paper that I covered in Europe's says that, you know, you don't like, don't really start to tap out of like, the alpha or the sort of improved loss that you get from data all the way until four epochs. And so I'm just like, okay, like, why do we all agree that one epoch is all you need?[00:18:17] swyx: It seems like to be a trend. It seems that we think that memorization is very good or too good. But then also we're finding that, you know, For improvement in results that we really like, we're fine on overtraining on things intentionally. So, I think that's an interesting direction that I don't see people exploring enough.[00:18:36] swyx: And the more I see papers coming out Stretching beyond the one epoch thing, the more people are like, it's completely fine. And actually, the only reason we stopped is because we ran out of compute[00:18:46] Alessio: budget. Yeah, I think that's the biggest thing, right?[00:18:51] swyx: Like, that's not a valid reason, that's not science. I[00:18:54] Alessio: wonder if, you know, Matt is going to do it.[00:18:57] Alessio: I heard LamaTree, they want to do a 100 billion parameters model. I don't think you can train that on too many epochs, even with their compute budget, but yeah. They're the only ones that can save us, because even if OpenAI is doing this, they're not going to tell us, you know. Same with DeepMind.[00:19:14] swyx: Yeah, and so the updates that we got on Lambda 3 so far is apparently that because of the Gemini news that we'll talk about later they're pushing it back on the release.[00:19:21] swyx: They already have it. And they're just pushing it back to do more safety testing. Politics testing.[00:19:28] Alessio: Well, our episode with Sumit will have already come out by the time this comes out, I think. So people will get the inside story on how they actually allocate the compute.[00:19:38] Direction 3: Alt. Architectures (Mamba, RWKV, RingAttention, Diffusion Transformers)[00:19:38] Alessio: Alternative architectures. Well, shout out to our WKV who won one of the prizes at our Final Frontiers event last week.[00:19:47] Alessio: We talked about Mamba and Strapain on the Together episode. A lot of, yeah, monarch mixers. I feel like Together, It's like the strong Stanford Hazy Research Partnership, because Chris Ray is one of the co founders. So they kind of have a, I feel like they're going to be the ones that have one of the state of the art models alongside maybe RWKB.[00:20:08] Alessio: I haven't seen as many independent. People working on this thing, like Monarch Mixer, yeah, Manbuster, Payena, all of these are together related. Nobody understands the math. They got all the gigabrains, they got 3DAO, they got all these folks in there, like, working on all of this.[00:20:25] swyx: Albert Gu, yeah. Yeah, so what should we comment about it?[00:20:28] swyx: I mean, I think it's useful, interesting, but at the same time, both of these are supposed to do really good scaling for long context. And then Gemini comes out and goes like, yeah, we don't need it. Yeah.[00:20:44] Alessio: No, that's the risk. So, yeah. I was gonna say, maybe it's not here, but I don't know if we want to talk about diffusion transformers as like in the alt architectures, just because of Zora.[00:20:55] swyx: One thing, yeah, so, so, you know, this came from the Jan recap, which, and diffusion transformers were not really a discussion, and then, obviously, they blow up in February. Yeah. I don't think they're, it's a mixed architecture in the same way that Stripe Tiena is mixed there's just different layers taking different approaches.[00:21:13] swyx: Also I think another one that I maybe didn't call out here, I think because it happened in February, was hourglass diffusion from stability. But also, you know, another form of mixed architecture. So I guess that is interesting. I don't have much commentary on that, I just think, like, we will try to evolve these things, and maybe one of these architectures will stick and scale, it seems like diffusion transformers is going to be good for anything generative, you know, multi modal.[00:21:41] swyx: We don't see anything where diffusion is applied to text yet, and that's the wild card for this category. Yeah, I mean, I think I still hold out hope for let's just call it sub quadratic LLMs. I think that a lot of discussion this month actually was also centered around this concept that People always say, oh, like, transformers don't scale because attention is quadratic in the sequence length.[00:22:04] swyx: Yeah, but, you know, attention actually is a very small part of the actual compute that is being spent, especially in inference. And this is the reason why, you know, when you multiply, when you, when you, when you jump up in terms of the, the model size in GPT 4 from like, you know, 38k to like 32k, you don't also get like a 16 times increase in your, in your performance.[00:22:23] swyx: And this is also why you don't get like a million times increase in your, in your latency when you throw a million tokens into Gemini. Like people have figured out tricks around it or it's just not that significant as a term, as a part of the overall compute. So there's a lot of challenges to this thing working.[00:22:43] swyx: It's really interesting how like, how hyped people are about this versus I don't know if it works. You know, it's exactly gonna, gonna work. And then there's also this, this idea of retention over long context. Like, even though you have context utilization, like, the amount of, the amount you can remember is interesting.[00:23:02] swyx: Because I've had people criticize both Mamba and RWKV because they're kind of, like, RNN ish in the sense that they have, like, a hidden memory and sort of limited hidden memory that they will forget things. So, for all these reasons, Gemini 1. 5, which we still haven't covered, is very interesting because Gemini magically has fixed all these problems with perfect haystack recall and reasonable latency and cost.[00:23:29] Wildcards: Text Diffusion, RALM/Retro[00:23:29] swyx: So that's super interesting. So the wildcard I put in here if you want to go to that. I put two actually. One is text diffusion. I think I'm still very influenced by my meeting with a mid journey person who said they were working on text diffusion. I think it would be a very, very different paradigm for, for text generation, reasoning, plan generation if we can get diffusion to work.[00:23:51] swyx: For text. And then the second one is Dowie Aquila's contextual AI, which is working on retrieval augmented language models, where it kind of puts RAG inside of the language model instead of outside.[00:24:02] Alessio: Yeah, there's a paper called Retro that covers some of this. I think that's an interesting thing. I think the The challenge, well not the challenge, what they need to figure out is like how do you keep the rag piece always up to date constantly, you know, I feel like the models, you put all this work into pre training them, but then at least you have a fixed artifact.[00:24:22] Alessio: These architectures are like constant work needs to be done on them and they can drift even just based on the rag data instead of the model itself. Yeah,[00:24:30] swyx: I was in a panel with one of the investors in contextual and the guy, the way that guy pitched it, I didn't agree with. He was like, this will solve hallucination.[00:24:38] Alessio: That's what everybody says. We solve[00:24:40] swyx: hallucination. I'm like, no, you reduce it. It cannot,[00:24:44] Alessio: if you solved it, the model wouldn't exist, right? It would just be plain text. It wouldn't be a generative model. Cool. So, author, architectures, then we got mixture of experts. I think we covered a lot of, a lot of times.[00:24:56] Direction 4: Mixture of Experts (DeepSeekMoE, Samba-1)[00:24:56] Alessio: Maybe any new interesting threads you want to go under here?[00:25:00] swyx: DeepSeq MOE, which was released in January. Everyone who is interested in MOEs should read that paper, because it's significant for two reasons. One three reasons. One, it had, it had small experts, like a lot more small experts. So, for some reason, everyone has settled on eight experts for GPT 4 for Mixtral, you know, that seems to be the favorite architecture, but these guys pushed it to 64 experts, and each of them smaller than the other.[00:25:26] swyx: But then they also had the second idea, which is that it is They had two, one to two always on experts for common knowledge and that's like a very compelling concept that you would not route to all the experts all the time and make them, you know, switch to everything. You would have some always on experts.[00:25:41] swyx: I think that's interesting on both the inference side and the training side for for memory retention. And yeah, they, they, they, the, the, the, the results that they published, which actually excluded, Mixed draw, which is interesting. The results that they published showed a significant performance jump versus all the other sort of open source models at the same parameter count.[00:26:01] swyx: So like this may be a better way to do MOEs that are, that is about to get picked up. And so that, that is interesting for the third reason, which is this is the first time a new idea from China. has infiltrated the West. It's usually the other way around. I probably overspoke there. There's probably lots more ideas that I'm not aware of.[00:26:18] swyx: Maybe in the embedding space. But the I think DCM we, like, woke people up and said, like, hey, DeepSeek, this, like, weird lab that is attached to a Chinese hedge fund is somehow, you know, doing groundbreaking research on MOEs. So, so, I classified this as a medium potential because I think that it is a sort of like a one off benefit.[00:26:37] swyx: You can Add to any, any base model to like make the MOE version of it, you get a bump and then that's it. So, yeah,[00:26:45] Alessio: I saw Samba Nova, which is like another inference company. They released this MOE model called Samba 1, which is like a 1 trillion parameters. But they're actually MOE auto open source models.[00:26:56] Alessio: So it's like, they just, they just clustered them all together. So I think people. Sometimes I think MOE is like you just train a bunch of small models or like smaller models and put them together. But there's also people just taking, you know, Mistral plus Clip plus, you know, Deepcoder and like put them all together.[00:27:15] Alessio: And then you have a MOE model. I don't know. I haven't tried the model, so I don't know how good it is. But it seems interesting that you can then have people working separately on state of the art, you know, Clip, state of the art text generation. And then you have a MOE architecture that brings them all together.[00:27:31] swyx: I'm thrown off by your addition of the word clip in there. Is that what? Yeah, that's[00:27:35] Alessio: what they said. Yeah, yeah. Okay. That's what they I just saw it yesterday. I was also like[00:27:40] swyx: scratching my head. And they did not use the word adapter. No. Because usually what people mean when they say, Oh, I add clip to a language model is adapter.[00:27:48] swyx: Let me look up the Which is what Lava did.[00:27:50] Alessio: The announcement again.[00:27:51] swyx: Stable diffusion. That's what they do. Yeah, it[00:27:54] Alessio: says among the models that are part of Samba 1 are Lama2, Mistral, DeepSigCoder, Falcon, Dplot, Clip, Lava. So they're just taking all these models and putting them in a MOE. Okay,[00:28:05] swyx: so a routing layer and then not jointly trained as much as a normal MOE would be.[00:28:12] swyx: Which is okay.[00:28:13] Alessio: That's all they say. There's no paper, you know, so it's like, I'm just reading the article, but I'm interested to see how[00:28:20] Wildcard: Model Merging (mergekit)[00:28:20] swyx: it works. Yeah, so so the wildcard for this section, the MOE section is model merges, which has also come up as, as a very interesting phenomenon. The last time I talked to Jeremy Howard at the Olama meetup we called it model grafting or model stacking.[00:28:35] swyx: But I think the, the, the term that people are liking these days, the model merging, They're all, there's all different variations of merging. Merge types, and some of them are stacking, some of them are, are grafting. And, and so like, some people are approaching model merging in the way that Samba is doing, which is like, okay, here are defined models, each of which have their specific, Plus and minuses, and we will merge them together in the hope that the, you know, the sum of the parts will, will be better than others.[00:28:58] swyx: And it seems like it seems like it's working. I don't really understand why it works apart from, like, I think it's a form of regularization. That if you merge weights together in like a smart strategy you, you, you get a, you get a, you get a less overfitting and more generalization, which is good for benchmarks, if you, if you're honest about your benchmarks.[00:29:16] swyx: So this is really interesting and good. But again, they're kind of limited in terms of like the amount of bumps you can get. But I think it's very interesting in the sense of how cheap it is. We talked about this on the Chinatalk podcast, like the guest podcast that we did with Chinatalk. And you can do this without GPUs, because it's just adding weights together, and dividing things, and doing like simple math, which is really interesting for the GPU ports.[00:29:42] Alessio: There's a lot of them.[00:29:44] Direction 5: Online LLMs (Gemini Pro, Exa)[00:29:44] Alessio: And just to wrap these up, online LLMs? Yeah,[00:29:48] swyx: I think that I ki I had to feature this because the, one of the top news of January was that Gemini Pro beat GPT-4 turbo on LM sis for the number two slot to GPT-4. And everyone was very surprised. Like, how does Gemini do that?[00:30:06] swyx: Surprise, surprise, they added Google search. Mm-hmm to the results. So it became an online quote unquote online LLM and not an offline LLM. Therefore, it's much better at answering recent questions, which people like. There's an emerging set of table stakes features after you pre train something.[00:30:21] swyx: So after you pre train something, you should have the chat tuned version of it, or the instruct tuned version of it, however you choose to call it. You should have the JSON and function calling version of it. Structured output, the term that you don't like. You should have the online version of it. These are all like table stakes variants, that you should do when you offer a base LLM, or you train a base LLM.[00:30:44] swyx: And I think online is just like, There, it's important. I think companies like Perplexity, and even Exa, formerly Metaphor, you know, are rising to offer that search needs. And it's kind of like, they're just necessary parts of a system. When you have RAG for internal knowledge, and then you have, you know, Online search for external knowledge, like things that you don't know yet?[00:31:06] swyx: Mm-Hmm. . And it seems like it's, it's one of many tools. I feel like I may be underestimating this, but I'm just gonna put it out there that I, I think it has some, some potential. One of the evidence points that it doesn't actually matter that much is that Perplexity has a, has had online LMS for three months now and it performs, doesn't perform great.[00:31:25] swyx: Mm-Hmm. on, on lms, it's like number 30 or something. So it's like, okay. You know, like. It's, it's, it helps, but it doesn't give you a giant, giant boost. I[00:31:34] Alessio: feel like a lot of stuff I do with LLMs doesn't need to be online. So I'm always wondering, again, going back to like state of the art, right? It's like state of the art for who and for what.[00:31:45] Alessio: It's really, I think online LLMs are going to be, State of the art for, you know, news related activity that you need to do. Like, you're like, you know, social media, right? It's like, you want to have all the latest stuff, but coding, science,[00:32:01] swyx: Yeah, but I think. Sometimes you don't know what is news, what is news affecting.[00:32:07] swyx: Like, the decision to use an offline LLM is already a decision that you might not be consciously making that might affect your results. Like, what if, like, just putting things on, being connected online means that you get to invalidate your knowledge. And when you're just using offline LLM, like it's never invalidated.[00:32:27] swyx: I[00:32:28] Alessio: agree, but I think going back to your point of like the standing the test of time, I think sometimes you can get swayed by the online stuff, which is like, hey, you ask a question about, yeah, maybe AI research direction, you know, and it's like, all the recent news are about this thing. So the LLM like focus on answering, bring it up, you know, these things.[00:32:50] swyx: Yeah, so yeah, I think, I think it's interesting, but I don't know if I can, I bet heavily on this.[00:32:56] Alessio: Cool. Was there one that you forgot to put, or, or like a, a new direction? Yeah,[00:33:01] swyx: so, so this brings us into sort of February. ish.[00:33:05] OpenAI Sora and why everyone underestimated videogen[00:33:05] swyx: So like I published this in like 15 came with Sora. And so like the one thing I did not mention here was anything about multimodality.[00:33:16] swyx: Right. And I have chronically underweighted this. I always wrestle. And, and my cop out is that I focused this piece or this research direction piece on LLMs because LLMs are the source of like AGI, quote unquote AGI. Everything else is kind of like. You know, related to that, like, generative, like, just because I can generate better images or generate better videos, it feels like it's not on the critical path to AGI, which is something that Nat Friedman also observed, like, the day before Sora, which is kind of interesting.[00:33:49] swyx: And so I was just kind of like trying to focus on like what is going to get us like superhuman reasoning that we can rely on to build agents that automate our lives and blah, blah, blah, you know, give us this utopian future. But I do think that I, everybody underestimated the, the sheer importance and cultural human impact of Sora.[00:34:10] swyx: And you know, really actually good text to video. Yeah. Yeah.[00:34:14] Alessio: And I saw Jim Fan at a, at a very good tweet about why it's so impressive. And I think when you have somebody leading the embodied research at NVIDIA and he said that something is impressive, you should probably listen. So yeah, there's basically like, I think you, you mentioned like impacting the world, you know, that we live in.[00:34:33] Alessio: I think that's kind of like the key, right? It's like the LLMs don't have, a world model and Jan Lekon. He can come on the podcast and talk all about what he thinks of that. But I think SORA was like the first time where people like, Oh, okay, you're not statically putting pixels of water on the screen, which you can kind of like, you know, project without understanding the physics of it.[00:34:57] Alessio: Now you're like, you have to understand how the water splashes when you have things. And even if you just learned it by watching video and not by actually studying the physics, You still know it, you know, so I, I think that's like a direction that yeah, before you didn't have, but now you can do things that you couldn't before, both in terms of generating, I think it always starts with generating, right?[00:35:19] Alessio: But like the interesting part is like understanding it. You know, it's like if you gave it, you know, there's the video of like the, the ship in the water that they generated with SORA, like if you gave it the video back and now it could tell you why the ship is like too rocky or like it could tell you why the ship is sinking, then that's like, you know, AGI for like all your rig deployments and like all this stuff, you know, so, but there's none, there's none of that yet, so.[00:35:44] Alessio: Hopefully they announce it and talk more about it. Maybe a Dev Day this year, who knows.[00:35:49] swyx: Yeah who knows, who knows. I'm talking with them about Dev Day as well. So I would say, like, the phrasing that Jim used, which resonated with me, he kind of called it a data driven world model. I somewhat agree with that.[00:36:04] Does Sora have a World Model? Yann LeCun vs Jim Fan[00:36:04] swyx: I am on more of a Yann LeCun side than I am on Jim's side, in the sense that I think that is the vision or the hope that these things can build world models. But you know, clearly even at the current SORA size, they don't have the idea of, you know, They don't have strong consistency yet. They have very good consistency, but fingers and arms and legs will appear and disappear and chairs will appear and disappear.[00:36:31] swyx: That definitely breaks physics. And it also makes me think about how we do deep learning versus world models in the sense of You know, in classic machine learning, when you have too many parameters, you will overfit, and actually that fails, that like, does not match reality, and therefore fails to generalize well.[00:36:50] swyx: And like, what scale of data do we need in order to world, learn world models from video? A lot. Yeah. So, so I, I And cautious about taking this interpretation too literally, obviously, you know, like, I get what he's going for, and he's like, obviously partially right, obviously, like, transformers and, and, you know, these, like, these sort of these, these neural networks are universal function approximators, theoretically could figure out world models, it's just like, how good are they, and how tolerant are we of hallucinations, we're not very tolerant, like, yeah, so It's, it's, it's gonna prior, it's gonna bias us for creating like very convincing things, but then not create like the, the, the useful role models that we want.[00:37:37] swyx: At the same time, what you just said, I think made me reflect a little bit like we just got done saying how important synthetic data is for Mm-Hmm. for training lms. And so like, if this is a way of, of synthetic, you know, vi video data for improving our video understanding. Then sure, by all means. Which we actually know, like, GPT 4, Vision, and Dolly were trained, kind of, co trained together.[00:38:02] swyx: And so, like, maybe this is on the critical path, and I just don't fully see the full picture yet.[00:38:08] Alessio: Yeah, I don't know. I think there's a lot of interesting stuff. It's like, imagine you go back, you have Sora, you go back in time, and Newton didn't figure out gravity yet. Would Sora help you figure it out?[00:38:21] Alessio: Because you start saying, okay, a man standing under a tree with, like, Apples falling, and it's like, oh, they're always falling at the same speed in the video. Why is that? I feel like sometimes these engines can like pick up things, like humans have a lot of intuition, but if you ask the average person, like the physics of like a fluid in a boat, they couldn't be able to tell you the physics, but they can like observe it, but humans can only observe this much, you know, versus like now you have these models to observe everything and then They generalize these things and maybe we can learn new things through the generalization that they pick up.[00:38:55] swyx: But again, And it might be more observant than us in some respects. In some ways we can scale it up a lot more than the number of physicists that we have available at Newton's time. So like, yeah, absolutely possible. That, that this can discover new science. I think we have a lot of work to do to formalize the science.[00:39:11] swyx: And then, I, I think the last part is you know, How much, how much do we cheat by gen, by generating data from Unreal Engine 5? Mm hmm. which is what a lot of people are speculating with very, very limited evidence that OpenAI did that. The strongest evidence that I saw was someone who works a lot with Unreal Engine 5 looking at the side characters in the videos and noticing that they all adopt Unreal Engine defaults.[00:39:37] swyx: of like, walking speed, and like, character choice, like, character creation choice. And I was like, okay, like, that's actually pretty convincing that they actually use Unreal Engine to bootstrap some synthetic data for this training set. Yeah,[00:39:52] Alessio: could very well be.[00:39:54] swyx: Because then you get the labels and the training side by side.[00:39:58] swyx: One thing that came up on the last day of February, which I should also mention, is EMO coming out of Alibaba, which is also a sort of like video generation and space time transformer that also involves probably a lot of synthetic data as well. And so like, this is of a kind in the sense of like, oh, like, you know, really good generative video is here and It is not just like the one, two second clips that we saw from like other, other people and like, you know, Pika and all the other Runway are, are, are, you know, run Cristobal Valenzuela from Runway was like game on which like, okay, but like, let's see your response because we've heard a lot about Gen 1 and 2, but like, it's nothing on this level of Sora So it remains to be seen how we can actually apply this, but I do think that the creative industry should start preparing.[00:40:50] swyx: I think the Sora technical blog post from OpenAI was really good.. It was like a request for startups. It was so good in like spelling out. Here are the individual industries that this can impact.[00:41:00] swyx: And anyone who, anyone who's like interested in generative video should look at that. But also be mindful that probably when OpenAI releases a Soa API, right? The you, the in these ways you can interact with it are very limited. Just like the ways you can interact with Dahlia very limited and someone is gonna have to make open SOA to[00:41:19] swyx: Mm-Hmm to, to, for you to create comfy UI pipelines.[00:41:24] Alessio: The stability folks said they wanna build an open. For a competitor, but yeah, stability. Their demo video, their demo video was like so underwhelming. It was just like two people sitting on the beach[00:41:34] swyx: standing. Well, they don't have it yet, right? Yeah, yeah.[00:41:36] swyx: I mean, they just wanna train it. Everybody wants to, right? Yeah. I, I think what is confusing a lot of people about stability is like they're, they're, they're pushing a lot of things in stable codes, stable l and stable video diffusion. But like, how much money do they have left? How many people do they have left?[00:41:51] swyx: Yeah. I have had like a really, Ima Imad spent two hours with me. Reassuring me things are great. And, and I'm like, I, I do, like, I do believe that they have really, really quality people. But it's just like, I, I also have a lot of very smart people on the other side telling me, like, Hey man, like, you know, don't don't put too much faith in this, in this thing.[00:42:11] swyx: So I don't know who to believe. Yeah.[00:42:14] Alessio: It's hard. Let's see. What else? We got a lot more stuff. I don't know if we can. Yeah, Groq.[00:42:19] Groq Math[00:42:19] Alessio: We can[00:42:19] swyx: do a bit of Groq prep. We're, we're about to go to talk to Dylan Patel. Maybe, maybe it's the audio in here. I don't know. It depends what, what we get up to later. What, how, what do you as an investor think about Groq? Yeah. Yeah, well, actually, can you recap, like, why is Groq interesting? So,[00:42:33] Alessio: Jonathan Ross, who's the founder of Groq, he's the person that created the TPU at Google. It's actually, it was one of his, like, 20 percent projects. It's like, he was just on the side, dooby doo, created the TPU.[00:42:46] Alessio: But yeah, basically, Groq, they had this demo that went viral, where they were running Mistral at, like, 500 tokens a second, which is like, Fastest at anything that you have out there. The question, you know, it's all like, The memes were like, is NVIDIA dead? Like, people don't need H100s anymore. I think there's a lot of money that goes into building what GRUK has built as far as the hardware goes.[00:43:11] Alessio: We're gonna, we're gonna put some of the notes from, from Dylan in here, but Basically the cost of the Groq system is like 30 times the cost of, of H100 equivalent. So, so[00:43:23] swyx: let me, I put some numbers because me and Dylan were like, I think the two people actually tried to do Groq math. Spreadsheet doors.[00:43:30] swyx: Spreadsheet doors. So, one that's, okay, oh boy so, so, equivalent H100 for Lama 2 is 300, 000. For a system of 8 cards. And for Groq it's 2. 3 million. Because you have to buy 576 Groq cards. So yeah, that, that just gives people an idea. So like if you deprecate both over a five year lifespan, per year you're deprecating 460K for Groq, and 60K a year for H100.[00:43:59] swyx: So like, Groqs are just way more expensive per model that you're, that you're hosting. But then, you make it up in terms of volume. So I don't know if you want to[00:44:08] Alessio: cover that. I think one of the promises of Groq is like super high parallel inference on the same thing. So you're basically saying, okay, I'm putting on this upfront investment on the hardware, but then I get much better scaling once I have it installed.[00:44:24] Alessio: I think the big question is how much can you sustain the parallelism? You know, like if you get, if you're going to get 100% Utilization rate at all times on Groq, like, it's just much better, you know, because like at the end of the day, the tokens per second costs that you're getting is better than with the H100s, but if you get to like 50 percent utilization rate, you will be much better off running on NVIDIA.[00:44:49] Alessio: And if you look at most companies out there, who really gets 100 percent utilization rate? Probably open AI at peak times, but that's probably it. But yeah, curious to see more. I saw Jonathan was just at the Web Summit in Dubai, in Qatar. He just gave a talk there yesterday. That I haven't listened to yet.[00:45:09] Alessio: I, I tweeted that he should come on the pod. He liked it. And then rock followed me on Twitter. I don't know if that means that they're interested, but[00:45:16] swyx: hopefully rock social media person is just very friendly. They, yeah. Hopefully[00:45:20] Alessio: we can get them. Yeah, we, we gonna get him. We[00:45:22] swyx: just call him out and, and so basically the, the key question is like, how sustainable is this and how much.[00:45:27] swyx: This is a loss leader the entire Groq management team has been on Twitter and Hacker News saying they are very, very comfortable with the pricing of 0. 27 per million tokens. This is the lowest that anyone has offered tokens as far as Mixtral or Lama2. This matches deep infra and, you know, I think, I think that's, that's, that's about it in terms of that, that, that low.[00:45:47] swyx: And we think the pro the break even for H100s is 50 cents. At a, at a normal utilization rate. To make this work, so in my spreadsheet I made this, made this work. You have to have like a parallelism of 500 requests all simultaneously. And you have, you have model bandwidth utilization of 80%.[00:46:06] swyx: Which is way high. I just gave them high marks for everything. Groq has two fundamental tech innovations that they hinge their hats on in terms of like, why we are better than everyone. You know, even though, like, it remains to be independently replicated. But one you know, they have this sort of the entire model on the chip idea, which is like, Okay, get rid of HBM.[00:46:30] swyx: And, like, put everything in SREM. Like, okay, fine, but then you need a lot of cards and whatever. And that's all okay. And so, like, because you don't have to transfer between memory, then you just save on that time and that's why they're faster. So, a lot of people buy that as, like, that's the reason that you're faster.[00:46:45] swyx: Then they have, like, some kind of crazy compiler, or, like, Speculative routing magic using compilers that they also attribute towards their higher utilization. So I give them 80 percent for that. And so that all that works out to like, okay, base costs, I think you can get down to like, maybe like 20 something cents per million tokens.[00:47:04] swyx: And therefore you actually are fine if you have that kind of utilization. But it's like, I have to make a lot of fearful assumptions for this to work.[00:47:12] Alessio: Yeah. Yeah, I'm curious to see what Dylan says later.[00:47:16] swyx: So he was like completely opposite of me. He's like, they're just burning money. Which is great.[00:47:22] Analyzing Gemini's 1m Context, Reddit deal, Imagegen politics, Gemma via the Four Wars[00:47:22] Alessio: Gemini, want to do a quick run through since this touches on all the four words.[00:47:28] swyx: Yeah, and I think this is the mark of a useful framework, that when a new thing comes along, you can break it down in terms of the four words and sort of slot it in or analyze it in those four frameworks, and have nothing left.[00:47:41] swyx: So it's a MECE categorization. MECE is Mutually Exclusive and Collectively Exhaustive. And that's a really, really nice way to think about taxonomies and to create mental frameworks. So, what is Gemini 1. 5 Pro? It is the newest model that came out one week after Gemini 1. 0. Which is very interesting.[00:48:01] swyx: They have not really commented on why. They released this the headline feature is that it has a 1 million token context window that is multi modal which means that you can put all sorts of video and audio And PDFs natively in there alongside of text and, you know, it's, it's at least 10 times longer than anything that OpenAI offers which is interesting.[00:48:20] swyx: So it's great for prototyping and it has interesting discussions on whether it kills RAG.[00:48:25] Alessio: Yeah, no, I mean, we always talk about, you know, Long context is good, but you're getting charged per token. So, yeah, people love for you to use more tokens in the context. And RAG is better economics. But I think it all comes down to like how the price curves change, right?[00:48:42] Alessio: I think if anything, RAG's complexity goes up and up the more you use it, you know, because you have more data sources, more things you want to put in there. The token costs should go down over time, you know, if the model stays fixed. If people are happy with the model today. In two years, three years, it's just gonna cost a lot less, you know?[00:49:02] Alessio: So now it's like, why would I use RAG and like go through all of that? It's interesting. I think RAG is better cutting edge economics for LLMs. I think large context will be better long tail economics when you factor in the build cost of like managing a RAG pipeline. But yeah, the recall was like the most interesting thing because we've seen the, you know, You know, in the haystack things in the past, but apparently they have 100 percent recall on anything across the context window.[00:49:28] Alessio: At least they say nobody has used it. No, people[00:49:30] swyx: have. Yeah so as far as, so, so what this needle in a haystack thing for people who aren't following as closely as us is that someone, I forget his name now someone created this needle in a haystack problem where you feed in a whole bunch of generated junk not junk, but just like, Generate a data and ask it to specifically retrieve something in that data, like one line in like a hundred thousand lines where it like has a specific fact and if it, if you get it, you're, you're good.[00:49:57] swyx: And then he moves the needle around, like, you know, does it, does, does your ability to retrieve that vary if I put it at the start versus put it in the middle, put it at the end? And then you generate this like really nice chart. That, that kind of shows like it's recallability of a model. And he did that for GPT and, and Anthropic and showed that Anthropic did really, really poorly.[00:50:15] swyx: And then Anthropic came back and said it was a skill issue, just add this like four, four magic words, and then, then it's magically all fixed. And obviously everybody laughed at that. But what Gemini came out with was, was that, yeah, we, we reproduced their, you know, haystack issue you know, test for Gemini, and it's good across all, all languages.[00:50:30] swyx: All the one million token window, which is very interesting because usually for typical context extension methods like rope or yarn or, you know, anything like that, or alibi, it's lossy like by design it's lossy, usually for conversations that's fine because we are lossy when we talk to people but for superhuman intelligence, perfect memory across Very, very long context.[00:50:51] swyx: It's very, very interesting for picking things up. And so the people who have been given the beta test for Gemini have been testing this. So what you do is you upload, let's say, all of Harry Potter and you change one fact in one sentence, somewhere in there, and you ask it to pick it up, and it does. So this is legit.[00:51:08] swyx: We don't super know how, because this is, like, because it doesn't, yes, it's slow to inference, but it's not slow enough that it's, like, running. Five different systems in the background without telling you. Right. So it's something, it's something interesting that they haven't fully disclosed yet. The open source community has centered on this ring attention paper, which is created by your friend Matei Zaharia, and a couple other people.[00:51:36] swyx: And it's a form of distributing the compute. I don't super understand, like, why, you know, doing, calculating, like, the fee for networking and attention. In block wise fashion and distributing it makes it so good at recall. I don't think they have any answer to that. The only thing that Ring of Tension is really focused on is basically infinite context.[00:51:59] swyx: They said it was good for like 10 to 100 million tokens. Which is, it's just great. So yeah, using the four wars framework, what is this framework for Gemini? One is the sort of RAG and Ops war. Here we care less about RAG now, yes. Or, we still care as much about RAG, but like, now it's it's not important in prototyping.[00:52:21] swyx: And then, for data war I guess this is just part of the overall training dataset, but Google made a 60 million deal with Reddit and presumably they have deals with other companies. For the multi modality war, we can talk about the image generation, Crisis, or the fact that Gemini also has image generation, which we'll talk about in the next section.[00:52:42] swyx: But it also has video understanding, which is, I think, the top Gemini post came from our friend Simon Willison, who basically did a short video of him scanning over his bookshelf. And it would be able to convert that video into a JSON output of what's on that bookshelf. And I think that is very useful.[00:53:04] swyx: Actually ties into the conversation that we had with David Luan from Adept. In a sense of like, okay what if video was the main modality instead of text as the input? What if, what if everything was video in, because that's how we work. We, our eyes don't actually read, don't actually like get input, our brains don't get inputs as characters.[00:53:25] swyx: Our brains get the pixels shooting into our eyes, and then our vision system takes over first, and then we sort of mentally translate that into text later. And so it's kind of like what Adept is kind of doing, which is driving by vision model, instead of driving by raw text understanding of the DOM. And, and I, I, in that, that episode, which we haven't released I made the analogy to like self-driving by lidar versus self-driving by camera.[00:53:52] swyx: Mm-Hmm. , right? Like, it's like, I think it, what Gemini and any other super long context that model that is multimodal unlocks is what if you just drive everything by video. Which is[00:54:03] Alessio: cool. Yeah, and that's Joseph from Roboflow. It's like anything that can be seen can be programmable with these models.[00:54:12] Alessio: You mean[00:54:12] swyx: the computer vision guy is bullish on computer vision?[00:54:18] Alessio: It's like the rag people. The rag people are bullish on rag and not a lot of context. I'm very surprised. The, the fine tuning people love fine tuning instead of few shot. Yeah. Yeah. The, yeah, the, that's that. Yeah, the, I, I think the ring attention thing, and it's how they did it, we don't know. And then they released the Gemma models, which are like a 2 billion and 7 billion open.[00:54:41] Alessio: Models, which people said are not, are not good based on my Twitter experience, which are the, the GPU poor crumbs. It's like, Hey, we did all this work for us because we're GPU rich and we're just going to run this whole thing. And

Note for Latent Space Community members: we have now soft-launched meetups in Singapore, as well as two new virtual paper club/meetups for AI in Action and LLM Paper Club. We're also running Latent Space: Final Frontiers, our second annual demo day hackathon from last year.For the first time, we are doing an audio version of monthly AI Engineering recap that we publish on Latent Space! This month it's “The Four Wars of the AI Stack”; you can find the full recap with all the show notes here: https://latent.space/p/dec-2023* [00:00:00] Intro* [00:01:42] The Four Wars of the AI stack: Data quality, GPU rich vs poor, Multimodality, and Rag/Ops war* [00:03:17] Selection process for the four wars and notable mentions* [00:06:58] The end of low background tokens and the impact on data engineering* [00:08:36] The Quality Data Wars (UGC, licensing, synthetic data, and more)* [00:14:51] Synthetic Data* [00:17:49] The GPU Rich/Poors War* [00:18:21] Anyscale benchmark drama* [00:22:00] The math behind Mixtral inference costs* [00:28:48] Transformer alternatives and why they matter* [00:34:40] The Multimodality Wars* [00:38:10] Multiverse vs Metaverse* [00:45:00] The RAG/Ops Wars* [00:50:00] Will frameworks expand up, or will cloud providers expand down?* [00:54:32] Syntax to Semantics* [00:56:41] Outer Loop vs Inner Loop* [00:59:54] Highlight of the month Get full access to Latent Space at www.latent.space/subscribe

1-22 Papa & Lund Hour 4: Papa & Lund recap the Niners win with David Lombardi & also bring you the Four Play & 49ers Audio RecapSee omnystudio.com/listener for privacy information.

1-22 Papa & Lund Hour 4: Papa & Lund recap the Niners win with David Lombardi & also bring you the Four Play & 49ers Audio RecapSee omnystudio.com/listener for privacy information.

In this episode, Renslow Sherer, MD, and Trinh P. Vu, PharmD, BCIDP, discuss strategies for managing ambulatory patients with acute or previous COVID-19 infection, including:Current COVID-19 landscapeRapid antigen tests and PCR testsRisk stratification of patients who have a positive SARS-CoV-2 testAntiviral treatment (nirmatrelvir + ritonavir, remdesivir, and molnupiravir)Long COVIDPresenters:Renslow Sherer, MDDirector, International HIV Training CenterProfessor of MedicineSection of Infectious Diseases and Global HealthDepartment of MedicineUniversity of ChicagoChicago, IllinoisTrinh P. Vu, PharmD, BCIDPClinical Pharmacy Specialist in Infectious DiseasesDepartment of Pharmaceutical ServicesEmory University Hospital MidtownAtlanta, GeorgiaTo download the slides: bit.ly/3oHKC09To view the full online program: bit.ly/4201xcO

In this episode, Renslow Sherer, MD, and Trinh P. Vu, PharmD, BCIDP, discuss strategies for managing ambulatory patients with acute or previous COVID-19 infection, including:Current COVID-19 landscapeRapid antigen tests and PCR testsRisk stratification of patients who have a positive SARS-CoV-2 testAntiviral treatment (nirmatrelvir + ritonavir, remdesivir, and molnupiravir)Long COVIDPresenters:Renslow Sherer, MDDirector, International HIV Training CenterProfessor of MedicineSection of Infectious Diseases and Global HealthDepartment of MedicineUniversity of ChicagoChicago, IllinoisTrinh P. Vu, PharmD, BCIDPClinical Pharmacy Specialist in Infectious DiseasesDepartment of Pharmaceutical ServicesEmory University Hospital MidtownAtlanta, GeorgiaTo download the slides: bit.ly/3oHKC09To view the full online program: bit.ly/4201xcO

In this episode, Renslow Sherer, MD, and Trinh P. Vu, PharmD, BCIDP, discuss strategies for managing ambulatory patients with acute or previous COVID-19 infection, including:Current COVID-19 landscapeRapid antigen tests and PCR testsRisk stratification of patients who have a positive SARS-CoV-2 testAntiviral treatment (nirmatrelvir + ritonavir, remdesivir, and molnupiravir)Long COVIDPresenters:Renslow Sherer, MDDirector, International HIV Training CenterProfessor of MedicineSection of Infectious Diseases and Global HealthDepartment of MedicineUniversity of ChicagoChicago, IllinoisTrinh P. Vu, PharmD, BCIDPClinical Pharmacy Specialist in Infectious DiseasesDepartment of Pharmaceutical ServicesEmory University Hospital MidtownAtlanta, GeorgiaTo download the slides: bit.ly/3oHKC09To view the full online program: bit.ly/4201xcO

In this episode, Tracey Piparo, PA-C, and Renslow Sherer, MD, discuss cases of nonhospitalized patients with COVID-19, including:Young, healthy patient with no risk factorsYoung, healthy patient with risk factorsOlder patient with immunocompromiseOlder patient with renal dysfunctionPatient experiencing hypoxia Presenters: Tracey Piparo, PA-C Department of Palliative Medicine RJWBarnabas New Brunswick, New Jersey Renslow Sherer, MD Director, International HIV Training Center Professor of Medicine Section of Infectious Diseases and Global Health Department of Medicine University of Chicago Chicago, Illinois To download the slides: bit.ly/44cgH0qTo view the full online program: https://bit.ly/4201xcO

In this episode,  Chloe Orkin, MBChB, FRCP, MD, and Babafemi Taiwo, MBBS, discuss new HIV data presented at IDWeek and HIV Glasgow 2022, includingPrevention strategiesLA therapiesInvestigational agentsPresenters:Chloe Orkin, MBChB, FRCP, MDProfessor of HIVQueen Mary, University of LondonConsultant PhysicianLead for HIV ResearchBarts Health NHS TrustThe Royal London HospitalLondon, United KingdomBabafemi Taiwo, MBBSGene Stollerman Professor of MedicineChief, Division of Infectious DiseasesNorthwestern University Feinberg School of MedicineChicago, IllinoisContent based on an online CME/CE program supported by an independent educational grant from Gilead Sciences, Inc.; Janssen Therapeutics, Division of Janssen Products, LP; and ViiV Healthcare. Link to full program:https://bit.ly/3iFEF0T

In this episode, Princy N. Kumar, MD, and Paul E. Sax, MD, discuss new COVID-19 data from IDWeek 2022, including:COVID-19 vaccines, including omicron BA.1 bivalent boosterRisk factors for breakthrough COVID-19 infectionsCOVID-19 diagnostics, including digital droplet PCRCOVID-19 therapeutics, including:Nirmatrelvir plus ritonavirTixagevimab plus cilgavimabBaricitinibTocilizumabInhaled interferon β-1aCOVID-19 therapeutics and outcomes in patients with immunocompromiseLong COVIDPresenters:Princy N. Kumar, MD, FIDSA, MACPProfessor of Medicine and MicrobiologyChief, Division of Infectious Diseases and Travel MedicineSenior Associate Dean of StudentsGeorgetown University School of MedicineWashington, DCPaul E. Sax, MDClinical DirectorHIV Program and Division of Infectious DiseasesBrigham and Women's HospitalProfessor of MedicineHarvard Medical SchoolBoston, MassachusettsFollow along with the downloadable slideset at:http://bit.ly/3gkJI67Link to full program:http://bit.ly/3TSVthM

In this episode, Ish takes us on a solo journey through his book, 'HOW SUCCESSFUL PEOPLE GET ISH DONE'. Ish outlines his 7-step framework on how you can set and achieve your goals with 930 hrs of research on the world's most successful ppl, including...DJ AMBill GatesMichael JordanArianna HuffingtonTim FerrissRob DyrdekOprah WinfreyDavid GogginsBrené BrownKobe BryantQuincy Jones& moreIf you enjoyed this episode, drop a review and share it with your friends.Get the book here: https://www.ishverduzco.com/bookTwitter Thread: https://twitter.com/ishverduzco/status/1554517131416375296?s=46&t=23snDOQ9asqenFu5g-dTrgTwitter Accounts:Ish VerduzcoGet Ish DoneCaleb HodgsonMore tools & free resources here.

In this episode, Stefan Zeuzem, MD, discusses new viral hepatitis data from EASL 2022, including:Novel HBV therapeutics, including the REEF-2, B-Clear, and SAVE-1 studiesHBV cure from the Everest Project in ChinaHDV therapeutics from MYR301 and a study of bulevirtide in patients with cirrhosis and portal hypertensionHCV retreatment in patients with prior direct-acting antiviral therapy failurePresenter: Stefan Zeuzem, MDProfessor of MedicineChief, Department of Medicine I JW Goethe University Hospital Frankfurt, GermanyFollow along with the downloadable slideset at:https://bit.ly/3yJAruDLink to full program: https://bit.ly/3AjCNBC

In this episode, Daniel R. Kuritzkes, MD, and David A. Wohl, MD, discuss new HIV data from CROI 2022, including:Prevention strategiesLA therapiesSecond-line therapiesThe VISEND study comparing dolutegravir-based antiretroviral therapy (ART) vs protease inhibitor–based ART for second-line therapyThe NADIA study comparing dolutegravir with darunavir/ritonavir and tenofovir disoproxil fumarate with zidovudine for second-line therapyComorbidities and cureACTG A5324 (InMIND) study of ART intensification for cognitive impairment in people with HIVIMPAACT P1107 case of patient with loss of HIV-1–specific antibody response and viral detection following CCR5∆32/∆32 cord/haploidentical transplantDaniel R. Kuritzkes, MDChief, Division of Infectious DiseasesBrigham and Women's HospitalHarriet Ryan Albee Professor of MedicineHarvard Medical SchoolBoston, MassachusettsDavid A. Wohl, MDProfessor of MedicineSchool of MedicineSite Leader, Global Infectious Diseases Clinical Trials UnitUniversity of North Carolina at Chapel HillContent based on a CME program supported by educational grants from Gilead Sciences, Inc.; Janssen Therapeutics, Division of Janssen Products, LP; Merck Sharp & Dohme Corp; and ViiV Healthcare.Follow along with an expanded slideset at:https://bit.ly/3CItz0ZLink to full program:https://bit.ly/3tSpdAx

In this episode, Jordan J. Feld, MD, MPH, and Nancy Reau, MD, share lessons learned in the testing and treatment of viral hepatitis in the COVID-19 era, including:Global impact of the COVID-19 pandemic on viral hepatitis testing and linkage to careImpact of COVID-19 pandemic on testing and treatment in the United States and CanadaInnovative strategies to improve linkage to care of patients with viral hepatitis during the COVID-19 pandemicPresenters:Jordan J. Feld, MD, MPHProfessor of MedicineUniversity of TorontoHepatologistToronto Centre for Liver DiseaseSandra Rotman Centre for Global HealthToronto, CanadaNancy Reau, MDProfessor of MedicineChief, Section of HepatologyAssociate Director, Solid Organ TransplantationRichard B. Capps Chair of HepatologyRush University Medical CenterChicago, IllinoisFollow along with the slides at:https://bit.ly/3HQ6dZ5Link to full program:https://bit.ly/3Bgyd5I

In this episode, Drs Norah Terrault, MD, MPH, and Grace LH Wong, MD, share lessons learned in the care of patients with hepatitis B virus (HBV) in the COVID-19 era, including:HBV cascade of care before vs during the pandemicInnovative strategies to improve HBV diagnosis and linkage to careLessons learned in telemedicineElimination through vaccinationManagement of HBV and COVID-19 coinfectionManagement of elevated liver enzymes in COVID-19Presenters:Norah Terrault, MD, MPHProfessor of MedicineChief of Gastrointestinal and Liver DiseasesUniversity of Southern CaliforniaLos Angeles, CaliforniaGrace LH Wong, MDProfessorDirector, Medical Data Analytics Centre (MDAC)Deputy Director, Center for Liver Health Assistant Dean (Learning Experience), Faculty of MedicineThe Chinese University of Hong Kong Honorary ConsultantDivision of Gastroenterology and HepatologyDepartment of Medicine and TherapeuticsPrince of Wales HospitalHong KongFollow along with the slides at:https://bit.ly/3HQ6dZ5Link to full program:https://bit.ly/3Bgyd5I

And we're rolling... Always. @DKEPresents

Jas recaps the day's press conferences by the provincial government on B.C. flood recovery and on Omicron travel restrictions  See omnystudio.com/listener for privacy information.

Ep#40 AWS re:invent 2021 Audio Recap Listen while you work to a complete and full recap of AWS re:invent 2021

Ep#40 AWS re:invent 2021 Audio Recap Listen while you work to a complete and full recap of AWS re:invent 2021

In this episode, Dalilah Restrepo, MD, discusses barriers to shingles vaccination and strategies to increase routine vaccine uptake in the COVID-19 era.Using the latest data and expert guidance, this podcast covers topics such as:Shingles disease burden and increasing incidence over timeDecline in shingles vaccination rates in the COVID-19 eraNational Vaccine Advisory Committee standards of careCDC guidance catching up after RZV dosing delaysStrategies to increase shingles vaccine uptakePresenter:Dalilah Restrepo, MDInfectious Diseases SpecialistFountain Valley HospitalFountain Valley, CaliforniaContent based on a CME program supported by an educational grant from GlaxoSmithKlineFollow along with the downloadable slides at:https://bit.ly/3pW7Bl8Link to full program:https://bit.ly/3o4PB8N

In this episode, Tristan J. Barber, MA, MD, FRCP, discusses new HIV data from EACS 2021, including:First-line therapiesBIC/FTC/TAF in a test-and-treat model in IMEA 055 FASTWeek 144 data of ISL + DOR in treatment-naive patients from P011Switch StrategiesData on the use of DTG/3TC in patients with an M184V mutation from SOLAR 3D and the French Dat'AIDS CohortLong-Acting TherapiesEfficacy and safety of LA CAB + RPV by BMILong-term follow-up of participants who discontinued LA CAB + RPV in the ATLAS, ATLAS-2M, and FLAIR trialsPatient satisfaction with LA CAB + RPV from the CARISEL studyResistance and subgroup data from the CAPELLA study of lenacapavir in heavily treatment–experienced patientsCOVID-19 vaccination efficacy and the impact of the pandemic on viral suppression in PWHPresenter:Tristan J. Barber, MA, MD, FRCPConsultant in HIV MedicineIan Charleson Day CentreRoyal Free HospitalHonorary Associate ProfessorInstitute for Global HealthUniversity College LondonLondon, United KingdomContent based on a CME program supported by educational grants from Gilead Sciences, Inc.; Merck Sharp & Dohme Corp; and ViiV Healthcare.Follow along with an expanded slideset at:https://bit.ly/32RCX4NLink to full program:https://bit.ly/3G2GknK

In this episode, Dalilah Restrepo, MD, discusses expert guidance on vaccination for the prevention of shingles complications in the COVID-19 era.Contributor:Dalilah Restrepo, MDInfectious Diseases SpecialistFountain Valley HospitalFountain Valley, CaliforniaUsing a case study and the latest expert guidance, this podcast covers topics such as: Expert guidance on maintenance of routine vaccination during the COVID-19 eraCommon shingles complications in immunocompetent patientsAdditional shingles complications in immunocompromised patientsGuidance on safe shingles vaccine delivery during the COVID-19 pandemicCDC guidance catching up after RZV dosing delaysContent based on a CME program supported by an educational grant from GlaxoSmithKline.Follow along with the downloadable slides at: https://bit.ly/3r3AKx1Link to full program:https://bit.ly/3o4PB8N  

In this episode, Tracy Zivin-Tutela, MD, discusses expert guidance on vaccination against shingles.Tracy Zivin-Tutela, MDInfectious Diseases SpecialistDepartment of Infectious DiseasesFountain Valley Regional HospitalLos Alamitos Medical CenterFountain Valley, CaliforniaUsing a case study and the latest expert guidance, this podcast covers topics such as: Counseling patients on benefits and efficacy of vaccination against shinglesVaccine safetyVaccination guidelines for adults with or without previous vaccination with zoster vaccine live or recalled childhood chickenpoxAdministration of vaccination with consideration for other routine vaccines for flu, pneumonia, COVID-19Content based on a CME program supported by an educational grant from GlaxoSmithKlineFollow along with the downloadable slides at: https://bit.ly/3kunxJrLink to full program: https://bit.ly/3o4PB8N

In this episode, Gregory Dore, MBBS, PhD, FRACP, MPH, and Stacey Trooskin, MD, PhD, MPH, share lessons learned in the care of patients with HCV infection during the COVID-19 pandemic, including:TelehealthTreatment of PWIDTEMPO pilot studyMINMON studyPresenters:Gregory Dore, MBBS, PhD, FRACP, MPHProfessor, HeadViral Hepatitis Clinical Research ProgramKirby InstituteUniversity of New South WalesInfectious Diseases PhysicianSt. Vincent's HospitalSydney, AustraliaStacey Trooskin, MD, PhD, MPHChief Medical OfficerPhiladelphia FIGHT Community Health CentersFacultyDivision of Infectious DiseasesPerelman School of MedicineUniversity of PennsylvaniaPhiladelphia, PennsylvaniaFollow along with the slides at:https://bit.ly/3naBUVjLink to full program:https://bit.ly/3F5bxG8   

In this episode, Oni Blackstock, MD, MHS, and Nina T. Harawa, PhD, MPH, discuss racial disparities in COVID-19 outcomes, including:Background on healthcare inequities among BIPOCCOVID-19 racial disparities and the healthcare inequities driving themOvercoming barriers to optimal COVID-19 care among BIPOCAddressing concerns about COVID-19 vaccination  Motivational interviewing principles and the brief negotiated interview to earn trust and increase vaccine acceptancePresenters:Oni Blackstock, MD, MHSFounder and Executive DirectorHealth JusticeNew York, New YorkNina T. Harawa, PhD, MPHProfessor of Medicine and EpidemiologyUCLA David Geffen School of MedicineCharles R. Drew University of Medicine and SciencePolicy Core DirectorUCLA Center for HIV Identification, Prevention, and Treatment Services (CHIPTS)Los Angeles, CaliforniaLink to full program and downloadable slides:https://bit.ly/3blGA3R   

In this episode, Renslow Sherer, MD, discusses new COVID-19 data from IDWeek 2021, including:Prevention of COVID-19ChAdOx1 nCoV-19Ad26.COV2.STixagevimab plus cilgavimabOutpatients with COVID-19Remdesivir IVMolnupiravirHospitalized patients with COVID-19BaricitinibLenzilumabCasirivimab plus imdevimabLong-term COVID-19 outcomesSustained recoveryPresenter:Renslow Sherer, MDDirectorInternational HIV Training CenterProfessor of MedicineSection of Infectious Diseases and Global HealthDepartment of MedicineUniversity of ChicagoChicago, IllinoisLink to full program and downloadable slides:https://bit.ly/3BCHF2E

During this episode, Lynora Saxinger, MD, FRCPC, CTropMed, updates listeners on the latest Emergency Use Authorizations and guideline recommendations for optimal clinical management of COVID-19. Gain practical insights on patient identification, risk stratification, and treatment.Key points include:Treatment of COVID-19 can be broken into antiviral and immune-modulating therapiesEach agent should be used at the correct disease stage to maximize benefitAntiviral medications and mAbs show the greatest promise early during COVID-19, before the host immune response is mountedPassive immunization with mAbs can prevent infection and severe disease—and mortality—if given early to hospitalized personsOperational challenges to using these medications exist, but given the epidemic of unvaccinated persons, these treatments become increasingly relevant to reduce burden on healthcare systemsPresenter:Lynora Saxinger, MD, FRCPC, CTropMedCochair, Scientific Advisory Group  Alberta COVID-19 Emergency Coordination Centre  Associate ProfessorDivision of Infectious DiseasesDepartment of Medicine  University of AlbertaEdmonton, Alberta, CanadaContent based on an online CME program supported by an educational grant from Gilead Sciences, Inc.Follow along with the slides at:https://bit.ly/3BxQKtD Link to full program:https://bit.ly/3BwzdlC

AUDIO: Recap the weekend and look ahead in just five minutes. Ole Miss, Texas A&M, Fury v Wilder III and more. 

In this episode, Darcy Wooten, MD, discusses new HIV data from IDWeek 2021, including:A rapid test-and-treat program to start DTG/3TC in patients newly diagnosed with HIV (STAT)Efficacy when switching from 3-drug to 4-drug TAF-based regimens to DTG/3TC (TANGO)Virologic outcomes with 2-drug vs 3-drug ART regimens (Trio Health HIV Network Study)Metabolic complications in treatment-naive patients (Study 1489 and Study 1490)Resistance analysis of an investigational agent, lenacapavir (CALIBRATE)The Positive Perspectives Survey to evaluate the U=U Educational Campaign in North American patientsPresenter:Darcy Wooten, MDAssociate Professor of MedicineDivision of Infectious Diseases and Global Public HealthDepartment of MedicineUniversity of California, San DiegoSan Diego, CaliforniaLink to full program:https://bit.ly/3BCHF2E

In this episode, Tracy Zivin-Tutela, MD, discusses factors that put your patients at risk for shingles and its complications.Tracy Zivin-Tutela, MDInfectious Diseases SpecialistDepartment of Infectious DiseasesFountain Valley Regional HospitalLos Alamitos Medical CenterFountain Valley, CaliforniaUsing a case study and the latest data, this podcast covers topics such as: Pathophysiology and epidemiologyDisease burden and potential complications in older patients, including postherpetic neuralgiaRisk factors, including age, immunosenescence, chronic diseases, unvaccinated statusContent based on a CME program supported by an educational grant from GlaxoSmithKlineFollow along with the downloadable slides at:https://bit.ly/3ENbcbPLink to full program:https://bit.ly/3o4PB8N

In this second of 2 episodes, Shobha Swaminathan, MD, and Eric S. Daar, MD, review key data influencing their practice following the IAS 2021 Conference, including data on investigational antiretroviral agents, lenacapavir and islatravir, and alternative dosing of BPaL for MDR-TB.  This episode includes discussion of:An extension of FLAIR, demonstrating virologic efficacy and safety of long-acting CAB plus RPV at Week 124A Week 26 safety and efficacy analysis of CAPELLA, a study of lenacapavir in heavily treatment–experienced PWHCALIBRATE, a study of the safety and efficacy of lenacapavir in treatment-naive PWHA Week 96 safety analysis of Protocol 011, evaluating islatravir plus DOR in treatment-naive patientsA Week 24 safety and pharmacokinetic analysis of Protocol 016, evaluating oral islatravir once monthly for PrEPZeNix, a phase III trial of pretomanid, bedaquiline, and linezolid (BPaL) in patients with highly resistant TBPresenters:Eric S. Daar, MDChief, Division of HIV MedicineHarbor-UCLA Medical CenterProfessor of MedicineDavid Geffen School of Medicine at UCLALos Angeles, CaliforniaShobha Swaminathan, MDAssociate ProfessorDivision of Infectious Diseases  Department of MedicineRutgers New Jersey Medical SchoolNewark, New JerseyFollow along with the slides at:https://bit.ly/3hvRN5bContent based on an online CME program supported by an educational grant from ViiV Healthcare.

In this first of 2 episodes, Shobha Swaminathan, MD, and Eric S. Daar, MD, review key data influencing their practice following the IAS 2021 Conference, including results from studies on HIV and COVID-19 outcomes; virologic and metabolic outcomes of DTG/3TC; long-acting CAB plus RPV for ART in treatment-naive PWH.  This episode includes discussion of:A Global Clinical Platform that evaluated outcomes of PWH hospitalized with COVID-19A post hoc analysis of TANGO, evaluating the virologic and metabolic outcomes when switching to DTG/3TC vs continued 3- or 4-drug TAF-based regimensA Week 48 analysis of SALSA, demonstrating virologic efficacy and favorable safety profile of switching to DTG/3TC vs continuing a 3-drug ART regimenPresenters:Eric S. Daar, MDChief, Division of HIV MedicineHarbor-UCLA Medical CenterProfessor of MedicineDavid Geffen School of Medicine at UCLALos Angeles, CaliforniaShobha Swaminathan, MDAssociate ProfessorDivision of Infectious Diseases  Department of MedicineRutgers New Jersey Medical SchoolNewark, New JerseyFollow along with the slides at:https://bit.ly/2WZu7zmContent based on an online CME program supported by an educational grant from ViiV Healthcare.

In this series of 3 episodes, Geeta Gupta, MD, discusses strategies for optimizing ART in heavily treatment–experienced patients with HIV, including management of virologic failure and switching ART in virologically suppressed patients with underlying resistance. The 3 episodes will include analyses from:BENCHMRK, which demonstrated that the number of active drugs predicts outcomes in treatment-experienced patientsTMB-301, a study that added the monoclonal antibody ibalizumab in pretreated patients with multidrug-resistant HIV and demonstrated significant decreases in HIV-1 RNABRIGHTE, which showed that the addition of fostemsavir in heavily treatment–experienced patients with HIV led to a significant decrease in HIV-1 RNAVIKING-3, a study that demonstrated the addition of DTG dosed BID in INI-resistant patients was effective in decreasing HIV-1 RNASWITCHMRK, a study that showed underlying resistance matters when deciding to switch regimens in a treatment-experienced, virologically suppressed patient, especially when switching to a regimen with a low barrier to resistanceDAWNING, a study that demonstrated that DTG, when added to 2 NRTIs, was superior to RTV-boosted LPV in patients with virologic failure on an NNRTI plus 2 NRTIsBRAAVE, which evaluated safety and efficacy of switching to BIC/FTC/TAF in Black patients virologically suppressed on 2 NRTIs plus a third agentIn addition, Dr. Gupta reviews current DHHS ART guideline recommendations related to managing virologic failure, including drug resistance testing and drug selection, as well as switching recommendations in patients with viral suppression and underlying drug resistance.Presenter:Geeta Gupta, MDProfessorDirector, AIDS Education and Training CenterUniversity of California, IrvineOrange, CaliforniaFollow along with the slides at:https://bit.ly/3zxKBfLContent based on an online CME program supported by an educational grant from ViiV Healthcare.Link to full program:https://bit.ly/3ux6FF8

In this series of 3 episodes, Geeta Gupta, MD, discusses strategies for optimizing ART in heavily treatment–experienced patients with HIV, including management of virologic failure and switching ART in virologically suppressed patients with underlying resistance. The 3 episodes will include analyses from:BENCHMRK, which demonstrated that the number of active drugs predicts outcomes in treatment-experienced patientsTMB-301, a study that added the monoclonal antibody ibalizumab in pretreated patients with multidrug-resistant HIV and demonstrated significant decreases in HIV-1 RNA  BRIGHTE, which showed that the addition of fostemsavir in heavily treatment–experienced patients with HIV led to a significant decrease in HIV-1 RNA  VIKING-3, a study that demonstrated the addition of DTG dosed BID in INI-resistant patients was effective in decreasing HIV-1 RNASWITCHMRK, a study that showed underlying resistance matters when deciding to switch regimens in a treatment-experienced, virologically suppressed patient, especially when switching to a regimen with a low barrier to resistanceDAWNING, a study that demonstrated that DTG, when added to 2 NRTIs, was superior to RTV-boosted LPV in patients with virologic failure on an NNRTI plus 2 NRTIsBRAAVE, which evaluated safety and efficacy of switching to BIC/FTC/TAF in Black patients virologically suppressed on 2 NRTIs plus a third agentIn addition, Dr. Gupta reviews current DHHS ART guideline recommendations related to managing virologic failure, including drug resistance testing and drug selection, as well as switching recommendations in patients with viral suppression and underlying drug resistance.Presenter:Geeta Gupta, MDProfessorDirector, AIDS Education and Training CenterUniversity of California, IrvineOrange, CaliforniaFollow along with the slides at:https://bit.ly/3zxKBfLContent based on an online CME program supported by an educational grant from ViiV Healthcare.Link to full program:https://bit.ly/3ux6FF8

In this series of 3 episodes, Geeta Gupta, MD, discusses strategies for optimizing ART in heavily treatment–experienced patients with HIV, including management of virologic failure and switching ART in virologically suppressed patients with underlying resistance. The 3 episodes will include analyses from:BENCHMRK, which demonstrated that the number of active drugs predicts outcomes in treatment-experienced patientsTMB-301, a study that added the monoclonal antibody ibalizumab in pretreated patients with multidrug-resistant HIV and demonstrated significant decreases in HIV-1 RNABRIGHTE, which showed that the addition of fostemsavir in heavily treatment–experienced patients with HIV led to a significant decrease in HIV-1 RNA  VIKING-3, a study that demonstrated the addition of DTG dosed BID in INI-resistant patients was effective in decreasing HIV-1 RNASWITCHMRK, a study that showed underlying resistance matters when deciding to switch regimens in a treatment-experienced, virologically suppressed patient, especially when switching to a regimen with a low barrier to resistanceDAWNING, a study that demonstrated that DTG, when added to 2 NRTIs, was superior to RTV-boosted LPV in patients with virologic failure on an NNRTI plus 2 NRTIsBRAAVE, which evaluated safety and efficacy of switching to BIC/FTC/TAF in Black patients virologically suppressed on 2 NRTIs plus a third agentIn addition, Dr. Gupta reviews current DHHS ART guideline recommendations related to managing virologic failure, including drug resistance testing and drug selection, as well as switching recommendations in patients with viral suppression and underlying drug resistance.Presenter:Geeta Gupta, MDProfessorDirector, AIDS Education and Training CenterUniversity of California, IrvineOrange, CaliforniaFollow along with the slides at:https://bit.ly/3zxKBfLContent based on an online CME program supported by an educational grant from ViiV Healthcare.Link to full program:https://bit.ly/3ux6FF8

In this episode, Babafemi Taiwo, MBBS, discusses new HIV data from IAS 2021, including:WHO Global Clinical Platform: COVID-19 outcomes in PWH2-drug regimen of DTG/3TC: efficacy, metabolic outcomes (TANGO)Investigational antiretroviral useCAB LA + RPV LA in treatment-naive PWH (FLAIR)Investigational antiretroviral agentsLEN (CAPELLA, CALIBRATE)ISL (P011, P016)Alternative BPaL dosing in treatment of drug-resistant TB (ZeNix)Babafemi Taiwo, MBBSGene Stollerman Professor of MedicineChief, Division of Infectious DiseasesNorthwestern University Feinberg School of MedicineChicago, IllinoisContent based on a CME program supported by educational grants from Gilead Sciences, Inc.; Janssen Therapeutics, Division of Janssen Products, LP.; Merck Sharp & Dohme Corp; and ViiV Healthcare.Follow along with an expanded slideset at: https://bit.ly/3syWD5TLink to full program: https://bit.ly/3z8SBnC

In this episode, Stefan Zeuzem, MD, discusses exciting new data on viral hepatitis reported at the virtual EASL 2021, including new findings on treatment of HDV, treatment of HBV and HCV in children, linking patients to care, and new molecules being investigated for HBV cure.Stefan Zeuzem, MDProfessor of MedicineChief, Department of Medicine IJW Goethe University Hospital Frankfurt, GermanyContent based on a CME program supported by educational grants provided by AbbVie, Antios Therapeutics, and Gilead Sciences, Inc.To follow along, download the slides at:https://bit.ly/3rl8AeSLink to full program:https://bit.ly/3ky46QK

In this episode, Jehan Budak, MD, discusses demonstrates the use of HIV-ASSIST, a free, online, decision-support tool to inform ART selection.Jehan Budak, MDAssistant ProfessorDivision of Allergy & Infectious DiseasesUniversity of WashingtonSeattle, WashingtonContent based on a CME program supported by educational grants from Gilead Sciences, Inc.; Theratechnologies; and ViiV Healthcare.Link to full program:https://bit.ly/33RZ9Zy

Listen to expert insights from Joseph J. Eron, Jr., MD, on exciting data about investigational agents (dapivirine ring, islatravir, lenacapavir, broadly neutralizing antibodies) for next-generation HIV prevention.Joseph J. Eron, Jr., MDProfessor of Medicine and EpidemiologyUniversity of North Carolina School of MedicineDirector, AIDS Clinical Trials UnitUniversity of North CarolinaChapel Hill, North CarolinaLink to full program:https://bit.ly/3ux6FF8

Level UP is a daily video series I release on https://odysee.com/@kennetheash:f?r=9ALb3czSGspLiFkkoS1zQe6Lqx2keZPk (Odysee) and https://www.youtube.com/channel/UCDCnkNs3AhiuWFu6g-VVv4g (Youtube). Links to the videos can be found below. You should definitely follow our channel on Telegram, and join our https://mewe.com/join/theconstructivelibertypodcast (MeWe )group. Level UP 20 https://youtu.be/2iGH27nvEGQ https://odysee.com/level-up-daily-20-fail-forward.:6e28af51332e0ec363f792b93bb50ae0ce74d1c2 Level UP 21 https://youtu.be/CuHc8lt9AC4 https://odysee.com/level-up-daily-21-positive-personality:92fc9a579f9702403184e396abb01a70101fe48c Level UP 22 https://youtu.be/XZv01svgJBs https://odysee.com/level-up-22-the-most-hated-tool-in-my:aac9ca960b5c6da39cb8cdad2843252d0e487270 Level UP 23 https://youtu.be/PyBf0YPt7dk https://odysee.com/level-up-23-wisdomous-words-to-motivate:da43d9c3b5eb9ff10d1a7392862dcf2043ab9244 Level UP 24 https://youtu.be/dh4cjaeFHdc https://odysee.com/level-up-24-you%27ve-got-to-take-action!!:5ee5ab3e159b68be4a72d2faea692f3fc4f978d9 Level UP 25 https://youtu.be/DoLqqhFcKu4 https://odysee.com/level-up-25-7-principles-of-wealth:c3c4e12d8a7b9e24aeeefc9fe1237a538197ed7a

In this episode, David A. Wohl, MD, discusses current guidelines and clinical trial data informing optimal selection of first-line antiretroviral therapy for HIV.David A. Wohl, MD    Professor of MedicineSchool of MedicineSite Leader, AIDS Clinical Trials Unit-Chapel HillUniversity of North Carolina at Chapel HillDirector, North Carolina AIDS Training and Education CenterChapel Hill, North CarolinaCo-Director, HIV ServicesNorth Carolina Department of CorrectionRaleigh, North CarolinaLink to full program:https://bit.ly/3ux6FF8

Listen to expert insights from W. David Hardy, MD, on current options for pre-exposure prophylaxis and exciting new data on next-generation HIV prevention.W. David Hardy, MDScientific and Medical ConsultantAdjunct Clinical Professor of MedicineDivision of Infectious DiseasesKeck School of Medicine of USCLos Angeles, CaliforniaContent based on a CME program supported by an independent educational grant from ViiV Healthcare.Link to full program:https://bit.ly/3ux6FF8

In this episode, Ethel D. Weld, MD, PhD, demonstrates the use of HIV-ASSIST, a free, online, decision support tool to inform ART selection.Ethel D. Weld, MD, PhDAssistant Professor of Medicine, Pharmacology, & Molecular SciencesDivision of Clinical PharmacologyDepartment of Infectious DiseasesJohns Hopkins University School of MedicineAttending PhysicianInpatient HIV ServiceGeneral Infectious Diseases Consult ServicesJohns Hopkins HospitalBaltimore, MarylandContent based on a CME program supported by educational grants from Gilead Sciences, Inc.; Theratechnologies; and ViiV Healthcare.Link to full program:https://bit.ly/33RZ9Zy

Though they might sound like they're still in a cave, our makeshift heroes have found a new land! Never have they experienced such life in nature and yet, something does not seem quite right... --- Send in a voice message: https://anchor.fm/frontier-fiction/message

The Minnesota Vikings opened up training camp on Saturday with the full roster. Listen to Adam Thielen's thoughts on adapting to a new quarterback and on rookie CB Mike Hughes. Also Riley Reiff talked about bonding following the loss of Tony Sparano and Mackensie Alexander went into great detail about his struggles to adapt to nickel corner last year.

The Minnesota Vikings wrapped up minicamp on Thursday. We look back at some of the most interesting quotes and Matthew Coller talks 1-on-1 with right tackle Rashod Hill.

Matthew Coller brings you the most interesting comments from Kirk Cousins, Adam Thielen and Mike Zimmer

Escape To The Lake 2016 was one of the best music events of the year, full of unforgettable moments. We invite you into the event through this audio recap, full of recorded live performances and testimonials.

This July we held the biggest event of UTR's year, Escape To The Lake 2015. In this episode, we bring you an audio recap including testimonials from artists and attendees, LIVE performances from the Main Stage, and THREE radio world premiere songs!