Podcasts about google research

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

At the beginning of December 2026: ICE announced an enforcement surge in the Twin Cities.January 6, 2026: DHS announced what it called the largest immigration enforcement operation ever carried out, sending 2,000 agents to the Minneapolis–Saint Paul metropolitan area. January 7, 2026: ICE agent Jonathan Ross fatally shoots Renée Nicole GoodJanuary 8–14, 2026: Protests, vigils, and marches continue in Minneapolis against ICE and Operation Metro SurgeJanuary 13, 2026: ‘Madness': two US citizens violently detained by ICE in Minnesota, officials say. Two Target employees forced to the ground, then into SUV, then dumped in different parking lotJanuary 14, 2026: A different ICE agent shoots and injures a man in north Minneapolis; the man survives after being shot in the leg. This second shooting further intensifies public anger and calls for an end to the federal surgeJanuary 17, 2026: National Anger Spills Into Target Stores, AgainJanuary 22, 2026: Target Store Staff Are Skipping Work Over ICE's Crackdown in MinnesotaJanuary 23, 2026: A statewide Day of Truth & Freedom / Minnesota general strike is held, described as the first U.S. general strike in about 80 years, explicitly targeting ICE operations and Operation Metro Surge. On that day, many workers, businesses, schools, and institutions in Minneapolis and across Minnesota participate in work stoppages, marches, and large rallies against federal immigration enforcement.January 24, 2026: Federal Border Patrol agents assigned to the metro surge shoot and kill Alex Jeffrey PrettiJanuary 25, 2026: The Minnesota Chamber of Commerce released this letter on behalf of more than 60 CEOs of Minnesota-based companies today.Eight people have died in dealings with ICE so far in 2026. Keith Porter, Parady La, Heber Sanchaz Domínguez, Victor Manuel Diaz, Luis Beltran Yanez-Cruz, Luis Gustavo Nunez Caceres, and Geraldo Lunas Campos. The high-profile fatal shootings follow the deaths of at least 32 people in ICE custody in 2025 – the highest number since 2004.Minnesota CEOs Seek De-Escalation After Border Police Shooting“The business community in Minnesota prides itself in providing leadership and solving problems to ensure a strong and vibrant state. The recent challenges facing our state have created widespread disruption and tragic loss of life. For the past several weeks, representatives of Minnesota's business community have been working every day behind the scenes with federal, state and local officials to advance real solutions. These efforts have included close communication with the Governor, the White House, the Vice President and local mayors. There are ways for us to come together to foster progress. With yesterday's tragic news, we are calling for an immediate deescalation of tensions and for state, local and federal officials to work together to find real solutions. We have been working for generations to build a strong and vibrant state here in Minnesota and will do so in the months and years ahead with equal and even greater commitment. In this difficult moment for our community, we call for peace and focused cooperation among local, state and federal leaders to achieve a swift and durable solution that enables families, businesses, our employees, and communities across Minnesota to resume our work to build a bright and prosperous future. “3M – William Brown, Chairman and CEOAmeriprise Financial – James Cracchiolo, Chairman and CEOAPi Group – Russell Becker, CEOBest Buy – Corie Barry, CEO C.H. Robinson – Dave Bozeman, President and CEODeluxe Corporation – Barry McCarthy, President and CEODonaldson Company, Inc. – Tod Carpenter, Chairman and CEOEcolab – Christophe Beck, Chairman and CEOGeneral Mills – Jeff Harmening, Chairman and CEOH.B. Fuller – On behalf of our entire organization [CEO Celeste Mastin]Hormel – Jeff Ettinger, Interim CEOMedtronic – Geoff Martha, CEO and ChairmannVent – Beth Wozniak, Chair and CEO Patterson Companies – Robert Rajalingam, CEOPentair – John L. Stauch, President and CEOPiper Sandler – Chad Abraham, Chairman and CEOSleep Number – Linda Findley, CEO (4/2025)Solventum – Bryan Hanson, CEOSPS Commerce – Chad Collins, CEO SunOpta – Brian Kocher, CEOTarget – Michael Fiddelke, Incoming CEO Tennant Company – Dave Huml, CEOThe Toro Company – Rick Olson, Chairman and CEOU.S. Bancorp – Gunjan Kedia, CEOWinnebago Industries – Michael Happe, CEOXcel Energy – Bob Frenzel, Chairman and CEO Keith Rabois, Managing director of Khosla Ventures: “no law enforcement has shot an innocent person. illegals are committing violent crimes everyday.”Khosla Ventures: “We prefer brutal honesty to hypocritical politeness.”“Technology and innovation have reshaped our world and disrupted the way we all live and work. The future may not be knowable, but it is inventable—and it belongs to those who dare to imagine what's possible.”Managing Directors: 5 dudes (3 stanford; 3 harvard)Founder Vinod Khosla: “I agree with @EthanChoi7. Macho ICE vigilantes running amuck empowered by a conscious-less administration. The video was sickening to watch and the storytelling without facts or with invented fictitious facts by authorities almost unimaginable in a civilized society. ICE personnel must have ice water running thru their veins to treat other human beings this way. There is politics but humanity should transcend that”Target's incoming CEO Michael Fiddelke in a video message sent to employees (January 26, 2026): “Right now, as someone who is raising a family here in the Twin Cities and as a leader of this hometown company I want to acknowledge where we are. The violence and loss of life in our community is incredibly painful. I know it's weighing heavily on many of you across the country, as it is with me. What's happening affects us not just as a company but as people, as neighbors, friends and family members.”A company spokesman declined to comment. Still nothing official on website.Lloyd Vogel, CEO Garage Grown Gear: said he felt compelled to condemn the shootings in a LinkedIn post because he lives and works in the Twin Cities. "My primary rationale was to show solidarity with my community," he told Business Insider. "It's also just bad for business when people are afraid to leave their homes.""There's so much fear in Minnesota right now," he said. "It would just be cowardice to not have a perspective on this."JPMorgan Chase CEO and Chair Jamie Dimon 1/22/26 Davos): ″I don't like what I'm seeing, five grown men beating up a little old lady. So I think we should calm down a little bit on the internal anger about immigration… We need these people. They work in our hospitals and hotels and restaurants and agriculture, and they're good people.… They should be treated that way.”On Saturday evening (1/24/2026), top technology executives gathered in Washington to attend a screening of “Melania,” a documentary produced by Amazon about the first lady, Melania Trump. Black-tie event: guests were handed monogrammed buckets of popcorn, framed screening tickets for their trophy shelves, and a limited-edition copy of Trump's 2024 book of the same title as her documentary, “Melania.“Among them was Andy Jassy, the chief executive of Amazon; Tim Cook, the chief executive of Apple; and Lisa Su, the chief executive of chip maker AMD.Also: Eric Yuan – CEO, Zoom; Lynn Martin – President, New York Stock Exchange; General Electric CEO Larry CulpApple CEO Tim Cook says it's 'time for de-escalation' in MinneapolisCook came under fire for appearing at The White House just hours after federal immigration authorities killed Alex Pretti, a veterans' nurse, in Minnesota“This is a time for de-escalation,” Cook wrote to Apple staff. “I believe America is strongest when we live up to our highest ideals, when we treat everyone with dignity and respect no matter who they are or where they're from, and when we embrace our shared humanity.”Cook said he “had a good conversation with the president this week where I shared my views, and I appreciate his openness to engaging on issues that matter to us all." Apple's Cook says he's ‘heartbroken' by Minneapolis events and has spoken with TrumpOpen AI CEO Sam Altman (1/27/26): I love the US and its values of democracy and freedom and will be supportive of the country however I can; OpenAI will too. But part of loving the country is the American duty to push back against overreach. What's happening with ICE is going too far. There is a big difference between deporting violent criminals and what's happening now, and we need to get the distinction right. President Trump is a very strong leader, and I hope he will rise to this moment and unite the country. I am encouraged by the last few hours of response and hope to see trust rebuilt with transparent investigations. As a company, we aim to stick to our convictions and not get blown around by changing fashions too much. We didn't become super woke when that was popular, we didn't start talking about masculine corporate energy when that was popular, and we are not going to make a lot of performative statements now about safety or politics or anything else. But we are going to continue to try to figure out how to actually do the right thing as best as we can, engage with leaders and push for our values, and speak up clearly about it as needed.James Dyett, Global Business at OpenAI: “There is far more outrage from tech leaders over a wealth tax than masked ICE agents terrorizing communities and executing civilians in the streets. Tells you what you need to know about the values of our industry.”Angel Investor Jason Calacanis: Once again, I will remind everyone that our leaders are failing us. True leadership would be to calm this situation down by telling these non-peaceful protestors to stay home while recalling these inadequately-trained agents.”Jeff Dean, Chief Scientist, Google DeepMind & Google Research. Gemini Lead: “This is absolutely shameful. Agents of a federal agency unnecessarily escalating, and then executing a defenseless citizen whose offense appears to be using his cell phone camera. Every person regardless of political affiliation should be denouncing this.”Jeffrey Sonnenfeld, senior associate dean for leadership studies at the Yale School of Management: "CEOs are feeling the community pressure." He said that reactions that convey sorrow and don't mention Trump or ICE are likely to be perceived as an unwelcome challenge to the White House's immigration agenda. "That is not what the Trump administration wanted," he said.Business Roundtable CEO Joshua Bolten asked to comment on the chaos in Minneapolis: replied with a statement endorsing the Minnesota Chamber's call for "cooperation between state, local, and federal authorities to immediately de-escalate the situation in Minneapolis."Robert Pasin, CEO of toy company Radio Flyer: recently shared an email on LinkedIn that he sent to his employees that was critical of the shootings in Minneapolis: "I am deeply concerned about the current state of our democracy, and the continued actions we are seeing from President Trump and his administration that are intended to undermine democratic institutions, the rule of law, and the norms that hold our country together."Dario Amodei, CEO Anthropic: called the events in Minnesota a “horror” on Monday. An Anthropic spokeswoman said the company did not have contracts with ICE.ICEout.tech statement from January 24, 2026: "We condemn the Border Patrol's killing of Alex Pretti and the violent surge of federal agents across our cities. The wanton brutality we've seen from ICE and CBP has removed any credibility that these actions are about immigration enforcement. Their goal is terror, cruelty, and suppression of dissent. This must end. Tech professionals are speaking up against this brutality, and we call on all our colleagues who share our values to use their voice. We know our industry leaders have leverage: in October, they persuaded Trump to call off a planned ICE surge in San Francisco, and big tech CEOs are in the White House tonight. Now they need to go further, and join us in demanding ICE out of all of our cities." 811: 508 names; 19 one name with title, 284 role onlyReid Hoffman says business leaders are wrong to stay silent about the Trump administrationThe LinkedIn cofounder and tech investor said in an episode of the "Rapid Response" podcast published Tuesday that he rejects the idea that executives can simply wait out political turbulence: "The theory that if you just keep your mouth shut, the storm will blow over and it won't be a problem — you should be disabused of that theory now," Hoffman said.Palantir Defends Work With ICE to Staff Following Killing of Alex Pretti: Leadership defended its work as in part improving “ICE's operational effectiveness.”

Google Research found that repeating input prompts significantly improves accuracy in large language models for non-reasoning tasks. The technique works by allowing the model to reference the entire prompt during a second pass, which enhances information retrieval without increasing processing time. Tests across multiple benchmarks and models showed consistent performance gains, especially for direct-answer tasks. The method is most effective for non-reasoning scenarios and offers enterprises a low-cost optimization option. Security teams are advised to consider the impact of prompt repetition on both vulnerabilities and safety measures.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Support the show to get full episodes, full archive, and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. Read more about our partnership. Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. Can you look at all the synaptic connections of a brain, and tell me one nontrivial memory from the organism that has that brain? If so, you shall win the $100,000 prize from the Aspirational Neuroscience group. I was recently invited for the second time to chair a panel of experts to discuss that question and all the issues around that question - how to decode a non-trivial memory from a static map of synaptic connectivity. Before I play that recording, let me set the stage a bit more. Aspirational Neuroscience is a community of neuroscientists run by Kenneth Hayworth, with the goal, from their website, to "balance aspirational thinking with respect to the long-term implications of a successful neuroscience with practical realism about our current state of ignorance and knowledge." One of those aspirations is to decoding things - memories, learned behaviors, and so on - from static connectomes. They hold satellite events at the SfN conference, and invite experts in connectomics from academia and from industry to share their thoughts and progress that might advance that goal. In this panel discussion, we touch on multiple relevant topics. One question is what is the right experimental design or designs that would answer whether we are decoding memory - what is a benchmark in various model organisms, and for various theoretical frameworks? We discuss some of the obstacles in the way, both technologically and conceptually. Like the fact that proofreading connectome connections - manually verifying and editing them - is a giant bottleneck, or like the very definition of memory, what counts as a memory, let alone a "nontrivial" memory, and so on. And they take lots of questions from the audience as well. I apologize the audio is not crystal clear in this recording. I did my best to clean it up, and I take full blame for not setting up my audio recorder to capture the best sound. So, if you are a listener, I'd encourage you to check out the video version, which also has subtitles throughout for when the language isn't clear. Anyway, this is a fun and smart group of people, and I look forward to another one next year I hope. The last time I did this was episode 180, BI 180, which I link to in the show notes. Before that I had on Ken Hayworth, whom I mentioned runs Aspirational Neuroscience, and Randal Koene, who is on the panel this time. They were on to talk about the future possibility of uploading minds to computers based on connectomes. That was episode 103. Aspirational Neuroscience Panel Michał Januszewski@michalwj.bsky.social Research scientist (connectomics) with Google Research, automated neural tracing expert Sven Dorkenwald @sdorkenw.bsky.social Research fellow at the Allen Institute, first-author on first full Drosophila connectome paper Helene Schmidt@helenelab.bsky.social Group leader at Ernst Strungmann Institute, hippocampus connectome & EM expert Andrew Payne @andrewcpayne.bsky.social Founder of E11 Bio, expansion microscopy & viral tracing expert Randal Koene Founder of the Carboncopies Foundation, computational neuroscientist dedicated to the problem of brain emulation. Related episodes: BI 103 Randal Koene and Ken Hayworth: The Road to Mind Uploading BI 180 Panel Discussion: Long-term Memory Encoding and Connectome Decoding

La Danimarca prepara una legge senza precedenti: vuole dare ai cittadini diritto d'autore sulla propria identità digitale - immagine, voce e somiglianza - per contrastare la diffusione dei deepfake.Un esperimento normativo che potrebbe riscrivere il concetto stesso di “io digitale”.Nel frattempo, Google integra Gemini Deep Research dentro Gmail, Drive e Chat: l'assistente AI diventa parte del flusso di lavoro, capace di leggere documenti, email e conversazioni per generare insight personalizzati.Un passo avanti verso un'AI immersiva, ma con nuove domande su privacy e controllo.E da Google Research arriva Nested Learning, un nuovo paradigma di machine learning pensato per permettere ai modelli di imparare in modo continuo senza dimenticare.Un'idea che riapre il dibattito sull'evoluzione cognitiva delle AI.Tre notizie, una direzione chiara: stiamo passando da un'AI che “produce” a un'AI che comprende, ricorda e si integra nel mondo reale.Tutti i link agli articoli qui

Google Research is detailing a new research initiative they're calling Project Suncatcher, the blueprints for a constellation of solar-powered satellites carrying Tensor Processing Units to operate as space-based AI data centers. US President Donald Trump has renominated Jared Isaacman to become Administrator of NASA after withdrawing his initial nomination just this past May. China's crewed spacecraft Shenzhou‑20, docked at the Tiangong space station since April 24th, has unfortunately had its return to Earth postponed. And, more. Remember to leave us a 5-star rating and review in your favorite podcast app. Be sure to follow T-Minus on LinkedIn and Instagram. Selected Reading Exploring a space-based, scalable AI infrastructure system design Trump renominates Musk ally Jared Isaacman to run NASA months after withdrawal SEALSQ, WISeKey and Swiss Armed Forces advance quantum-secure satellite security  Our October 14 show: Are satellites vulnerable to eavesdropping?  China's Shenzhou-20 return mission delayed due to space debris impact- Reuters  ESA - Sentinel-1D and Ariane 6 ready for liftoff Rocket Lab - 'The Nation God Navigates' Launch Astronomer captures 2 meteors slamming into the moon (video) | Space Share your feedback. What do you think about T-Minus Space Daily? Please take a few minutes to share your thoughts with us by completing our brief listener survey. Thank you for helping us continue to improve our show.   Want to hear your company in the show? You too can reach the most influential leaders and operators in the industry. Here's our media kit. Contact us at space@n2k.com to request more info. Want to join us for an interview? Please send your pitch to space-editor@n2k.com and include your name, affiliation, and topic proposal. T-Minus is a production of N2K Networks, your source for strategic workforce intelligence. © N2K Networks, Inc. Learn more about your ad choices. Visit megaphone.fm/adchoices

Yossi Matias is the head of Google Research. He joins Big Technology Podcast to discuss the company's research efforts in areas like cancer treatment and Quantum and to discuss the relationship between research and product. Tune in to hear how Google used LLMs to generate a cancer hypothesis validated in living cells, what a “13,000×” quantum result really means, and how the research product loop turns papers into products. We also cover whether AI can automate a researcher's job. This conversation was recorded in front of a live audience at Google's Mountain View headquarters. --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b Questions? Feedback? Write to: bigtechnologypodcast@gmail.com

In this episode of The 360 Experience Podcast, Tim Braheem takes mortgage loan officers into one of the most important conversations of our time: artificial intelligence and its role in transforming the mortgage business.Joining Tim are Mary Hurd and Sebastian “Seb” Assaf, founders of Haavn AI in London. With decades of experience at Google, Microsoft, YouTube, and Meta, Mary and Seb bring rare insight into what's possible with AI and how mortgage professionals can apply it today. They dive deep into the tools, prompts, and frameworks that originators can implement immediately to gain an edge and stay on the forefront of the AI revolution.Top Takeaways for Loan Officers:1️⃣ How to brief AI like your eager intern so it becomes a true teammate in role-play, objection handling, and sales prep.2️⃣ The transcript-to-follow-up system that turns every client or Realtor meeting into same-day action steps and personalized outreach.3️⃣ The 90-day nurture campaign framework you can build, launch, and improve with AI to stay top-of-mind with past clients and partners.We want to hear from you! What challenges are you facing in your business right now? Email us at 360@theloanatlas.com with your questions and struggles - you may hear them answered in a future episode.DOWNLOAD THE FREE RESOURCE FROM MARY AND SEB https://haavn.ai/360-experience Get in touch with Mary and Seb: https://haavn.ai/ Check out Notebook LM: https://notebooklm.google/ _________________________________________________________________Unlock the Systems Top Producers are Using to Differentiate Their Value and Thrive in Any Market

Ali is VP of AI Product at SandboxAQ and was Product lead at Google DeepMind. Previously at Google Research. Before that: Meta (including Facebook AI - FAIR, Integrity and News Feed), LinkedIn, Yahoo, Microsoft and a startup. PHD in computer science.In this episode we talk about the current climate in silicon valley. To reach Ali go to :https://www.instagram.com/alikh1980 Hosted on Acast. See acast.com/privacy for more information.

เปิดพอดแคสต์เอพิโสดนี้ใน YouTube เพื่อประสบการณ์การรับชมที่ดีที่สุด Google คิดอะไรกับวงการ Healthcare? AI จะมาแทนที่แพทย์หรือไม่? การแข่งขันในสมรภูมิ AI ของโลกนี้จะจบที่ตรงไหน?  The Secret Sauce เอพิโสดนี้ เคน นครินทร์ พูดคุยกับ Katherine Chou Head of Product & UX for Google Research ถึงประเด็นสำคัญว่าทำไมบริษัทเทคฯ ยักษ์ใหญ่อย่าง Google ถึงสนใจในวงการสาธารณสุขหรือ Healthcare  รวมถึงเทคโนโลยี AI จะสามารถต่อยอดไป Disrupt วงการอะไรได้อีกบ้าง เพื่อช่วยยกระดับคุณภาพชีวิตของผู้คนระดับพันล้านคน นอกจากนี้ ประเทศไทยจะสามารถเป็นองค์ประกอบสำคัญอย่างไร เพื่อที่จะช่วยให้ Google Research ใช้ AI ปฏิวัติวงการแพทย์ได้อย่างไรบ้าง?

This week's episode of The Refresh dives into Walmart's evolving partnership with The Trade Desk, signaling potential changes in retail media alliances. We explore Google's use of large language models to combat ad fraud, achieving significant reductions in invalid traffic. Finally, we break down Variety's latest upfronts report, showing a continued decline in primetime TV ad commitments and notable growth in streaming investment. This week we cover: Walmart and The Trade Desk's relationship is moving from exclusive to open, raising questions about Walmart's retail data strategy and potential in-house platform development. The Trade Desk faces growing competition from vertically integrated giants like Amazon, Google, and Meta, which benefit from owned inventory and rich first-party data. Google's traffic quality team, in collaboration with Google Research and DeepMind, deployed large language models to detect and reduce mobile invalid traffic by 40%. Variety reports primetime TV ad commitments fell for the third consecutive year, with broadcast down 2.5% and cable down 4.3%. Streaming ad commitments surged nearly 18% year over year, driven by advanced targeting, programmatic buying opportunities, and high-value live sports content moving to digital platforms. Learn more about your ad choices. Visit megaphone.fm/adchoices

Google คิดอะไรกับวงการ Healthcare? AI จะมาแทนที่แพทย์หรือไม่? การแข่งขันในสมรภูมิ AI ของโลกนี้จะจบที่ตรงไหน? The Secret Sauce เอพิโสดนี้ เคน นครินทร์ พูดคุยกับ Katherine Chou Head of Product & UX for Google Research ถึงประเด็นสำคัญว่าทำไมบริษัทเทคฯ ยักษ์ใหญ่อย่าง Google ถึงสนใจในวงการสาธารณสุขหรือ Healthcare รวมถึงเทคโนโลยี AI จะสามารถต่อยอดไป Disrupt วงการอะไรได้อีกบ้าง เพื่อช่วยยกระดับคุณภาพชีวิตของผู้คนระดับพันล้านคน นอกจากนี้ ประเทศไทยจะสามารถเป็นองค์ประกอบสำคัญอย่างไร เพื่อที่จะช่วยให้ Google Research ใช้ AI ปฏิวัติวงการแพทย์ได้อย่างไรบ้าง?

Illia Polosukhin is a veteran AI researcher and one of the original authors of the landmark Transformer paper, Attention is All You Need, which he co-authored during his time at Google Research. He has a deep background in machine learning and natural language processing, and has spent over a decade working at the intersection of The post Building Open Infrastructure for AI with Illia Polosukhin appeared first on Software Engineering Daily.

Illia Polosukhin is a veteran AI researcher and one of the original authors of the landmark Transformer paper, Attention is All You Need, which he co-authored during his time at Google Research. He has a deep background in machine learning and natural language processing, and has spent over a decade working at the intersection of The post Building Open Infrastructure for AI with Illia Polosukhin appeared first on Software Engineering Daily.

Emily Forlini is here to give us an update on the state of brain computer interfaces. Google DeepMind and Google Research launched a new AI-based tropical cyclone forecasting model. NYU and UC Berkeley researchers have come up with a way to train robots by watching people perform tasks from their own perspectives. And Amazon Prime Video pads out their streaming non-ad free subscription with more ads. Starring Sarah Lane, Robb Dunewood, Emily Forlini, Roger Chang, Joe. To read the show notes in a separate page click here! Support the show on Patreon by becoming a supporter!

n this powerful episode, we sit down with Dr. Akila Kadambi, a leading researcher at the UCLA Brain Research Institute, USC Brain and Creativity Institute, and fellow at the UCLA-CDU Dana Center. She is an expert in Cognitive and Systems Neuroscience, exploring how positive and humanistic neuroscience reshapes our understanding of the mind. Dr. Kadambi's groundbreaking work focuses on the neuroscience of empathy, pro-social behavior, and how societal factors shape our brain's expression of compassion and connection. From the wiring of our neural circuits to the impact of culture and community, she offers a compelling look at how kindness isn't just a moral ideal—it's a measurable, trainable, and deeply biological part of being human. Whether you're curious about the science behind empathy or how we can build a more connected and compassionate world, this conversation is a must-listen. Her work is funded by the Dana Center, Google Research, Sanford Institute, and the NeuroArts Blueprint Initiative #USC #UCLA #Neuroscience

L'interoperabilità è diventata un elemento cruciale nel panorama tecnologico moderno. Se fino a qualche decennio fa erano poche le aziende che potevano creare innovazione a 360 gradi imponendosi sul mercato con ecosistemi completi, oggi assistiamo a un proliferare di aziende specializzate in tecnologie specifiche che stanno diventando leader nei rispettivi settori. Questa evoluzione ha reso fondamentale la capacità di sistemi diversi di collaborare efficacemente, scambiandosi dati e informazioni in modo trasparente: trovare standard comuni è ormai imprescindibile. In questa puntata approfondiamo il tema dell'interoperabilità in vari ambiti: dall'hardware industriale e medico alla domotica, fino ai servizi software e alla Pubblica Amministrazione.Nella sezione delle notizie parliamo di Microsoft che ha reso WSL (Windows Subsystem for Linux) open source, del "Take It Down Act" firmato negli USA anche contro i deepfake e dei satelliti FireSat per il monitoraggio degli incendi boschivi.--Indice--00:00 - Introduzione01:00 - Microsoft rende WSL open source (HDBlog.it, Luca Martinelli)02:04 - In USA è stata firmata la legge federale contro i deepfake (TheGuardian.com, Davide Fasoli)03:19 - I satelliti FireSat per il monitoraggio degli incendi (Google Research, Matteo Gallo)04:55 - Un futuro interoperabile, dall'Industria 4.0 ai servizi per il cittadino (Luca Martinelli)17:01 - Conclusione--Testo--Leggi la trascrizione: https://www.dentrolatecnologia.it/S7E21#testo--Contatti--• www.dentrolatecnologia.it• Instagram (@dentrolatecnologia)• Telegram (@dentrolatecnologia)• YouTube (@dentrolatecnologia)• redazione@dentrolatecnologia.it--Immagini--• Foto copertina: Macrovector su Freepik--Brani--• Ecstasy by Rabbit Theft• Capsized by Tollef

In this podcast episode, Dr. Jonathan H. Westover talks with Dr. Ishan Shivanand about dealing with workplace stress and burnout. Dr. Ishan Shivanand is bringing forth what is unknown about yoga. An acclaimed mental health researcher and professor, Dr. Shivanand is the founder of “Yoga of Immortals,” an evidence-based mental resilience meditation program for holistic well- being. The program has been clinically proven to reverse anxiety (75%), depression (72%), and insomnia (82%), and improve overall quality of life (77%) among test participants within four to eight weeks of regular practice. His YOI program has received commendations from U.S. Congress, the White House Office of National Drug Control Policy (ONDCP) and more. Dr. Ishan has expertise in working with people in high-stress environments and has been requested to teach YOI modalities at prestigious institutions, including M.D. Anderson Cancer Center, the Mayo Clinic, LinkedIn, Google Research etc.  Check out all of the podcasts in the HCI Podcast Network!

This week, Paul and Mike return with a rapid-fire breakdown. From major AI companies' bold policy recommendations to the AI Action Plan to Altman's teaser of a new creative writing model that blurs the line between human and machine—there's a lot to unpack. Plus: Google's AI infrastructure bets, Claude's web search rollout, and a new study showing how AI is transforming team dynamics and boosting productivity inside companies.  Access the show notes and show links here This episode is presented by Goldcast.  Goldcast is a B2B video content platform that helps marketing teams easily produce, repurpose, and distribute video content. We use Goldcast for our virtual Summits, and one of the standout features for us is their AI-powered Content Lab. If you're running virtual events and want to maximize your content effortlessly, check out Goldcast.  Learn more at goldcast.io. This episode is also presented by our Scaling AI webinar series.  Register now to learn the framework Paul Roetzer has taught to thousands of corporate, education, and government leaders. Learn more at ScalingAI.com and click on “Register for our upcoming webinar” Timestamps: 00:05:01 — NY Times Writer “Feeling the AGI” 00:15:00 — AI Action Plan Proposals 00:24:13 — Sam Altman Teases New Creative Writing Model 00:30:21 — Claude Gets Web Search 00:31:59 — AI's Impact on Google Search 00:36:35 — Anthropic's Strong Start to the Year 00:40:19 — It Turns Out That Gemini Can Remove Image Watermarks 00:44:32 — Google Research on New Way to Scale AI 00:48:42 — New Research Shows How GenAI Changes Performance in Corporate Work 00:57:18 — The Time Horizon of Tasks AI Can Handle Is Doubling Fast 01:05:14 — Apple Comes Clean on Siri AI Delays 01:08:51 — OpenAI Agents May Threaten Consumer Apps 01:14:03 — Powering the AI Revolution 01:17:44 — Google Deep Research Tips 01:21:14 — Other Product and Funding Updates Visit our website Receive our weekly newsletter Join our community: Slack LinkedIn Twitter Instagram Facebook Looking for content and resources? Register for a free webinar Come to our next Marketing AI Conference Enroll in our AI Academy

Die Themen in den Wissensnachrichten:+++ Mäuse leisten erste Hilfe +++ Süßstoff Aspartam verengt Blutgefäße +++ Google will Forschung mit KI voranbringen +++ **********Weiterführende Quellen zu dieser Folge:Hörtipp: Update Erde - deine News zu Klima, Mensch und NaturReviving-like prosocial behavior in response to unconscious or dead conspecifics in rodents, Science, 20.2.25A neural basis for prosocial behavior toward unresponsive individuals, Science 20.2.2025Sweetener aspartame aggravates atherosclerosis through insulin-triggered inflammation, Cell Metabolism, 19.2.25Accelerating scientific breakthroughs with an AI co-scientist, Google Research, 19.2.25Alle Quellen findet ihr hier.**********Ihr könnt uns auch auf diesen Kanälen folgen: TikTok auf&ab , TikTok wie_geht und Instagram .

In this episode of Create Like The Greats, we dive into why publishing research can be a game-changer, especially for those in AI, marketing, and SEO. The host uncovers how leveraging research-driven content can generate buzz, backlinks, and unmatched visibility for brands. Using examples from industry leaders like Google DeepMind and NVIDIA, this episode provides actionable steps to create, repurpose, and distribute impactful research. If you're ready to learn how research can transform your marketing strategies and build authority in your field, this is a must-listen! Key Takeaways and Insights: 1. The Rise of AI and Its Impact Across Industries (02:00) AI is influencing professions beyond marketing, including healthcare, education, and law. Google Trends data highlights an 8X increase in searches for AI research over the last four years. Online communities like Reddit's r/Futurology and Artificial Intelligence have seen significant growth since 2022. 2. Why Research-Driven Content Matters (10:42) Major companies like Google and NVIDIA are publishing groundbreaking AI research to generate links, press mentions, and authority. Google's AI research hubs (Google Research and DeepMind) showcase the power of free, accessible industry data. Example: Google DeepMind has journal articles with over 500-2,000 referring domains, driving backlinks, traffic, and credibility. 3. Different Types of Research-Driven Content Proprietary Research: Conduct in-depth studies with your team or academics to develop unique insights. Repurposing Data: Don't let research sit idle in PDFs—turn it into blog posts, LinkedIn infographics, social media videos, and more. Curated Research: Take existing industry studies or journals and reframe them for broader audiences in your niche. 4. Case Study: Unlocking Engagement Through Simplified Research Example: A blog post combining coffee productivity studies with up-tempo music research led to a viral response. Translating complex journals into relatable concepts amplifies visibility and audience resonance. 5. The SEO and Brand Visibility Advantages Research publications draw backlinks from reputable sources (e.g., Microsoft, universities) and are frequently cited in blogs and press. Example: DeepMind's journals generate massive referral traffic due to their accessibility and simplicity. Insights from Stanford's AI Index: The number of AI research articles tripled from 2010 to 2022. 6. Pro Tips for Distributing Research Findings Distribute research summaries via subreddit communities, LinkedIn posts, and news outlets. Convert large datasets into visual aids like charts, infographics, and videos to maximize distribution potential. Leverage PR strategies by pitching your findings to publications and blogs for additional reach. Actionable Advice for Creating Research-Driven Content Create Research: Invest in proprietary studies, user surveys, or experiments in your niche. Repurpose Content: Tailor findings into blog posts, social media visuals, and bite-sized infographics for different platforms. Collaborate: Partner with academics or industry experts to co-create compelling research studies. Translate Research: Simplify complex academic papers into relatable and engaging content for non-technical audiences. Pitch Your Study: Reach out to journalists, bloggers, and influencers who may amplify and share your findings. Resources and Links Mentioned:  Google Research – Explore Google's latest AI publications and studies. DeepMind Research – Access over 183 AI-focused journal articles. Stanford AI Index 2024 – Annual report on AI trends and insights. Tool Mentioned: Distribution.ai – Automate your content distribution processes. Related Subreddits: r/Futurology r/ArtificialIntelligence —

Send us a textYossi Matias, Vice President of Google and Head of Google Research, leads groundbreaking efforts in foundational machine learning, quantum computing, and AI for societal impact in education, health, and climate. A world-renowned AI expert, Yossi has pioneered conversational AI, driven Google Search innovations, and launched transformative initiatives like AI for Social Good and Google for Startups Accelerator. His work focuses on leveraging AI to address global challenges and improve lives on a global scale.Obum Ekeke, Head of Education Partnerships at Google DeepMind, champions equitable access to AI education and fosters diversity in the tech industry. An OBE recipient for his contributions to computing and inclusion, Obum has led initiatives that have reached millions of learners worldwide, including founding Google Educator Groups in over 60 countries. His mission is to prepare students and educators for the future by making AI knowledge accessible and impactful for all.

Martin GonzalezMartin is the creator of Google's Effective Founders Project, a global research program that uses people analytics to uncover what makes the best startup founders succeed and shares their success formula with the world.Martin is the creator of Google's Effective Founders Project, a global research program that uses people analytics to uncover what makes the best startup founders succeed and shares their success formula with the world.He has run leadership courses for thousands of funded tech startup founders across more than seventy countries in the Americas, Asia, Africa, and Europe. He is a frequent lecturer on entrepreneurship, organization design, and people analytics at Stanford, Wharton, and INSEAD.Martin is a principal of organization and talent development at Google. He works with Google's senior leaders to shape team culture, develop their people, and expand their leadership, so they can build cool things that matter. In his ten years there, he's worked with leaders across Google Research, DeepMind, Technology & Society, Responsible AI, Pixel, Fitbit, YouTube, Search, Maps, Android, and Chrome, to name a few.In 2023, The Aspen Institute recognized him as a First Movers Fellow, honoring his pioneering work at Google. The following year, in 2024, he received the Thinkers50 Radar Award, a prestigious recognition that identifies up-and-coming thinkers whose ideas are predicted to make an important impact on management thinking in the future.Prior to Google, he was a management consultant with the Boston Consulting Group and a product manager at Johnson & Johnson.Martin has studied organizational psychology and behavioral science at Columbia University and the London School of Economics. He's a serial immigrant, having lived and worked in New York, Jakarta, Singapore, Taipei, and Manila, where he is originally from. Today he lives in the San Francisco Bay Area with his wife, Bea, and three kids: Noelle, Jaime, and Andrea.

When I think of digital biology, I think of Patrick Hsu—he's the prototype, a rarified talent in both life and computer science, who recently led the team that discovered bridge RNAs, what may be considered CRISPR 3.0 for genome editing, and is building new generative A.I. models for life science. You might call them LLLMs-large language of life models. He is Co-Founder and a Core Investigator of the Arc Institute and Assistant Professor of Bioengineering and Deb Faculty Fellow at the University of California, Berkeley.Above is a brief snippet of our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.Here's the transcript with links to the audio and external links to relevant papers and things we discussed.Eric Topol (00:06):Well hello, it's Eric Topol with Ground Truths and I'm really delighted to have with me today Patrick Hsu. Patrick is a co-founder and core investigator at the Arc Institute and he is also on the faculty at the University of California Berkeley. And he has been lighting things up in the world of genome editing and AI and we have a lot to talk about. So welcome, Patrick.Patrick Hsu (00:29):Thanks so much. I'm looking forward to it. Appreciate you having me on, Eric.The Arc InstituteEric Topol (00:33):Well, the first thing I'd like to get into, because you're into so many important things, but one that stands out of course is this Arc Institute with Patrick Collison who I guess if you can tell us a bit about how you two young guys got to meet and developed something that's really quite unique that I think brings together investigators at Stanford, UCSF, and Berkeley. Is that right? So maybe you can give us the skinny about you and Patrick and how all this got going.Patrick Hsu (01:05):Yeah, sure. That sounds great. So we started Arc with Patrick C and with Silvana Konermann, a longtime colleague and chemistry faculty at Stanford about three years ago now, though we've been physically operational just over two years and we're an independent research institute working at the interface of biomedical science and machine learning. And we have a few different aspects of our model, but our overall mission is to understand and treat complex human diseases. And we have three pillars to our model. We have this PI driven side of the house where we centrally fund our investigators so that they don't have to write grants and work on their very best ideas. We have a technical staff side of the house more like you'd see in a frontier AI lab or in biotech industry where we have professional teams of R&D scientists working cross-functionally on higher level organizational wide goals that we call our institute initiatives.(02:05):One focused on Alzheimer's disease experimentally and one that we call a virtual cell initiative to simulate human biology with AI foundation models. And our third pillar over time is to have things not just end up as academic papers, but really get things out into the real world as products or as medicines that can actually help patients on the translational side. And so, we thought that some really important scientific programs could be unlocked by enabling new organizational models and we are experimenting at the institutional scale with how we can better organize and incentivize and support scientists to reach these long-term capability breakthroughs.Patrick, Patrick and SilvanaEric Topol (02:52):So the two Patrick's. How did you, one Patrick I guess is a multi-billionaire from Stripe and then there's you who I suspect maybe not quite as wealthy as the other Patrick, how did you guys come together to do this extraordinary thing?Patrick Hsu (03:08):Yeah, no, science is certainly expensive. I met Patrick originally through Silvana actually. They actually met, so funny trivia, all three Arc founders did high school science together. Patrick and Silvana originally met in the European version of the European Young Scientist competition in high school. And Silvana and I met during our PhDs in her case at MIT and I was at Harvard, but we met at the Broad Institute sort of also a collaborative Harvard, MIT and Harvard hospitals Institute based in Kendall Square. And so, we sort of in various pairwise combinations known each other for decades and worked together for decades and have all collectively been really excited about science and technology and its potential to accelerate societal progress. Yet we also felt in our own ways that despite a lot of the tremendous progress, the structures in which we do this work, fund it, incentivize it and roll it out into the real world, seems like it's really possible that we'll undershoot that potential. And if you take 15 years ago, we didn't have the modern transformer that launched the current AI revolution, CRISPR technology, single-cell, mRNA technology or broadly addressable LNPs. That's a tremendous amount of technologies have developed in the next 15 years. We think there's a real unique opportunity for new institutes in the 2020s to take advantage of all of these breakthroughs and the new ones that are coming to continue to accelerate biological progress but do so in a way that's fast and flexible and really focused.Eric Topol (04:58):Yeah, I did want to talk with you a bit. First of all before I get to the next related topic, I get a kick out of you saying you've worked or known each other for decades because I think you're only in your early thirties. Is that right?Patrick Hsu (05:14):I was lucky to get an early start. I first started doing research at the local university when I was 14 actually, and I was homeschooled actually until college. And so, one of the funny things that you got to do when you're homeschooled is well, you could do whatever you want. And in my case that was work in the lab. And so, I actually worked basically full time as an intern volunteer, cut my teeth in single cell patch clamp, molecular biology, protein biochemistry, two photon and focal imaging and kind of spiraled from there. I loved the lab, I loved doing bench work. It was much more exciting to me than programming computers, which was what I was doing at the time. And I think these sort of two loves have kind of brought me and us to where we are today.Eric Topol (06:07):Before you got to Berkeley and Arc, I know you were at Broad Institute, but did you also pick up formal training in computer science and AI or is that something that was just part of the flow?Patrick Hsu (06:24):So I grew up coding. I used to work through problems sets before dinner growing up. And so, it's just something that you kind of learn natively just like learning French or Mandarin.New Models of Funding Life ScienceEric Topol (06:42):That's what I figured. Okay. Now this model of Arc Institute came along in a kind of similar timeframe as the Arena BioWorks in Boston, where some of the faculty left to go to Arena like my friend Stuart Schreiber and many others. And then of course Priscilla and Mark formed the Chan Zuckerberg Institute and its biohub and its support. So can you contrast for one, these three different models because they're both very different than of course the traditional NIH pathway, how Arc is similar or different to the others, and obviously the goal here is accelerating things that are going to really make a difference.Patrick Hsu (07:26):Yeah, the first thing I would say is zooming out. There have been lots of efforts to experiment with how we do science, the practice of science itself. And in fact, I've recently been reading this book, the Demon Under the Microscope about the history of infectious disease, and it talks about how in the 1910s through the 1930s, these German industrial dye manufacturing companies like Bayer and BASF actually launched what became essentially an early model for industrial scale science, where they were trying to develop Prontosil, Salvarsan and some of these early anti-infectives that targeted streptococcus. And these were some of the major breakthroughs that led to huge medical advances on tackling infectious disease compared to the more academic university bound model. So these trends of industrial versus academic labs and different structures to optimize breakthroughs and applications has been a through current throughout international science for the last century.(08:38):And so, the way that we do research today, and that's some of our core tenets at Arc is basically it hasn't always been this way. It doesn't need to necessarily be this way. And so, I think organizational experiments should really matter. And so, there's CZI, Altos, Arena, Calico, a variety of other organizational experiments and similarly we had MRC and Bell Labs and Xerox PARCS, NIBRT, GNF, Google Research, and so on. And so, I think there are lots of different ways that you can organize folks. I think at a high level you can think about ways that you can play with for-profit versus nonprofit structures. Whether you want to be a completely independent organization or if you want to be partnered with universities. If you want to be doing application driven science or really blue sky curiosity driven work. And I think also thinking through internally the types of expertise that you bring together.(09:42):You can think of it like a cancer institute maybe as a very vertically integrated model. You have folks working on all kinds of different areas surrounding oncology or immunotherapy and you might call that the Tower of Babel model. The other way that folks have built institutes, you might call the lily pad model where you have coverage of as many areas of biomedical research as possible. Places like the Whitehead or Salk, it will be very broad. You'll have planned epigenetics, folks looking at RNA structural biology, people studying yeast cell cycle, folks doing in vivo melanoma models. It's very broad and I think what we try to do at Arc is think about a model that you might liken more to overlapping Viking shields where there's sort of five core areas that we're deeply investing in, in genetics and genomics, computation, neuroscience, immunology and chemical biology. Now we really think of these as five areas that are maybe the minimal critical mass that you would need to make a dent on something as complicated as complex human diseases. It's certainly not the only thing that you need, but we needed a critical mass of investigators working at least in these areas.Eric Topol (11:05):Well, yeah, and they really converge on where the hottest advances are being made these days. Now can you work at Arc Institute without being one of these three universities or is it really that you maintain your faculty and your part of this other entity?Patrick Hsu (11:24):So we have a few elements to even just the academic side of the house. We have our core investigators. I'm one of them, where we have dually appointed faculty who retain their latter rank or tenured appointment in their home department, but their labs are physically cited at the Arc headquarters where we built out a lab in Stanford Research Park in Palo Alto. And so, folks move their labs there. They continue to train graduate students based on whatever graduate programs they're formally affiliated with through their university affiliation. And so, we have nearly 40 PhD students across our labs that are training on site every day.(12:03):So in addition to our core investigators, we also have what we call our innovation investigators, which is more of a grant program to faculty at our partner universities. They receive unrestricted funding from us to seed a new project or accelerate an existing area in their group and their labs stay at their home campus and they just get that funding to augment their work. The third way is our technical staff model where folks basically just come work at Arc and many of them also are establishing their own research groups focusing on technology R&D areas. And so, we have five of those technology centers working in molecular engineering, multi-omics, complex cellular models, in vivo models, and in machine learning.Discovery of Bridge RNAsEric Topol (12:54):Yeah, that's a great structure. In fact, just a few months ago, Patrick Collison, the other Patrick came to Stanford HAI where I'm on the board and you've summarized it really well and it's very different than the other models and other entities, companies included that you mentioned. It's really very impressive. Now speaking of impressive on June 26, this past few months ago, which incidentally is coincident with the draft genome in the year 2000, the human sequence. You and your colleagues, perhaps the most impressive jump in terms of an Arc Institute contribution published two papers back-to-back in Nature about bridge RNA: [Bridge RNAs direct programmable recombination of target and donor DNA] and [Structural mechanism of bridge RNA-guided recombination.] And before I get you to describe this breakthrough in genome editing, some would call it genome editing 3.0 or CRISPR 3.0, whatever. But what we have today in the clinic with the approval of CRISPR 1.0 for sickle cell and thalassemia is actually quite crude. I think most people will know it's just a double stranded DNA cleavage with all sorts of issues about repair and it's not very precise. And so, CRISPR 2.0 is supposed to be represented by David Liu's contributions and his efforts at Broad like prime and base editing and then comes yours. So maybe you can tell us about it and how it is has to be viewed as quite an important advance.Patrick Hsu (14:39):The first thing I would say before CRISPR, is that we had RNA interference. And so, even before this modern genome editing revolution with programmable CRISPRs, we had this technology that had a lot of the core selling points as well. Any target will now become druggable to us. We simply need to reprogram a guide RNA and we can get genetic access to things that are intracellular. And I think both the discovery of RNA interference by Craig Mello and Andy Fire or the invention or discovery of programmable CRISPR technologies, both depend on the same fundamental biological mechanism. These non-coding guide RNAs that are essentially a short RNA search string that you can easily reprogram to retarget a desired enzyme function, and natively both RNAi and CRISPR are molecular scissors. Their RNA or DNA nucleases that can be reprogrammed to different regions of the genome or the transcriptome to make a cut.(15:48):And as bioengineers, we have come up with all kinds of creative ways to leverage the ability to make site specific cuts to do all kinds of incredible things including genome editing or beyond transcriptional up or down regulation, molecular imaging and so on and so forth. And so, the first thing that we started thinking about in our lab was, why would mother nature have stopped only RNAi and CRISPR? There probably are lots of other non-coding RNAs out there that might be able to be programmable and if they did exist, they probably also do more complicated and interesting things than just guide a molecular scissors. So that was sort of the first core kind of intuition that we had. The second intuition that we had on the technology side, I was just wearing my biology hat, I'll put on my technology hat, is the thing that we call genome editing today hardly involves the genome.(16:50):It's really you're making a cut to change an individual base or an individual gene or locus. So really you're doing small scale single locus editing, so you might call it gene level or locus level cuts. And what you really want to be able to do is do things at the genome scale at 100 kb, a megabase at the chromosome scale. And I think that's where I think the field will inevitably go if you follow the technology curves of longer and longer range gene sequencing, longer and longer range gene synthesis, and then longer and longer range gene editing. And so, what would that look like? And we started thinking, could there be essentially recombination technologies that allow you to do cut and paste in a single step. Now, the reason for that is the way that we do gene editing today involves a cut and then a multi-step process of cellular DNA repair that resolves the cut to make the exertion or the error prone deletion or the modification that ends up happening.(17:59):And so, it's very complicated and whether that's nucleases or base or prime editing, you're all generally limited to the small-scale single locus changes. However, there are natural mechanisms that have solved this cut and paste problem, right? There are these viruses or bacterial versions of viruses known as phage that have generally been trying to exert their multi kilobase genomes into bacterial hosts and specialize throughout billions of years. So our core thought was, well, if there are these new non-coding RNAs, what kind of functions would we be excited about? Can we look in these mobile genetic elements, these so-called jumping genes for new mechanisms? They're incredibly widespread. Transposons are thought to be some of the most diverse enzyme mechanisms found in nature. And so, we started computationally by asking ourselves a very simple question. If a mobile element inserts itself into foreign DNA and it's able to somehow be programmable, presumably the inside or something encoded in the inside of the element is predictive of some sequence on the outside of the element.(19:15):And so, that was the core insight we took, and we thought let's look across the boundaries of many different mobile genetic elements and we zoomed in on a particular sub family of these MGE known as insertion sequence (IS) elements which are the most autonomous minimal transposons. Normally transposons have all kinds of genes that they use to hitchhike around the genomic galaxy and endow the bacterial host with some fitness advantage like some ability to metabolize some copper and some host or some metal. And these IS elements have only the enzymes that they need to jump around. And if you identify the boundaries of these using modern computational methods, this is actually a really non-trivial problem. But if you solve that problem to figure out with nucleotide resolution where the element boundaries end and then you look for the open reading frame of the transposases enzyme inside of this element, you'll find that it's not just that coding sequence.(20:19):There are also these non-coding flanks inside of the element boundaries. And when we looked across the non-coding, the entire IS family tree, there are hundreds of these different types of elements. We found that this particular family IS110, had the longest non-coding ends of all IS elements. And we started doing experiments in the lab to try to figure out how these work. And what we found was that these elements are cut and paste elements, so they excise themselves into a circular form and paste themselves back in into a target site linearly. But the circularization of this element brings together two distal ends together, which brings together a -35 and a -10 box that create and reconstitute a canonical bacterial transcriptional promoter. This essentially is like plugging a plug into an electrical socket in the wall and it jacks up transcription. Now you would think this transcription would turn on the transposase enzyme so it can jump around more but it transcribes a non-coding RNA out of this non-coding end.(21:30):We're like, holy crap, are these RNAs actually involved in regulating the transposon? Now the boring answer would be, oh, it regulates the expression. It's like an antisense regulate or something. The exciting answer would be, oh, it's a new type of guide RNA and you found an RNA guided integrase. So we started zooming in bound dramatically on this and we undertook a covariation analysis where we were able to show that this cryptic non-coding RNA has a totally novel guide RNA structure, totally distinct from RNAi or CRISPR guide RNAs. And it had a target site that covaried with the target site of the element. And so we're like, oh wow, this could be a programmable transposase. The second thing that we found was even more surprising, there was a second region of complementarity in that same RNA that recognized the donor sequence, which is the circularized element itself. And so, this was the first example of a bispecific guide RNA, and also the first example of RNA guided self-recognition by a mobile genetic element.Eric Topol (22:39):It's pretty extraordinary because basically you did a systematic assessment of jumping genes or transposons and you found that they contain things that previously were not at all recognized. And then you have a way to program these to edit, change the genome without having to do any cuts or nicks, right?Patrick Hsu (23:05):Yeah. So what we showed in a test tube is when we took this, so-called bridge RNA, which we named because it bridges the target and donor together along with the recombinase enzyme. So the two component system, those are the only two things that you need. They're able to cut and paste DNA and recombine them in a test tube without any DNA repair, meaning that it's independent of cellular DNA repair and it does strand nicking, exchange, junction resolution and religation all in a single mechanism. So that's when we got super excited about its potential applications as bioengineering tool.Eric Topol (23:46):Yeah, it's pretty extraordinary. And have you already gone into in vivo assessment?Patrick Hsu (23:54):Yes, in our initial set of papers, what we showed is that these are programmable and functional or recombinases in a test tube and in bacterial cells. And by reprogramming the target and donor the right way, you can use these enzymes not just for insertion, but also for flipping and cutting out DNA. And so, we actually have in a single mechanism the ability to do bridge editing, if you will, for universal DNA recombination, insertion, excision or inversion, similar to what folks have been doing for decades with Cre recombinase, but with fully programmable recognition sequences. The work that we're doing now in the lab as you can imagine is to adapt these into robust tools for mammalian genome editing, including of course, human genomes. We're excited about this, we're making good progress. The CRISPR has had thousands of labs over the last 10, 15 years working on it to make these therapeutic level potency and selectivity. We're going to work and follow that same blueprint for getting bridge systems to get to that level of performance, but we're on the path and we're very optimistic for the future.Exemplar of Digital BiologyEric Topol (25:13):Yeah, I think it's quite extraordinary and it's a whole different look to what we've been seeing in the CRISPR era for over the past decade and how that's been advancing and getting more specific and less need for repair and being able to be more versatile. But this takes it to yet another dimension. Now, this brings me to the field that when I think of this term digital biology, I think of you and now our mutual acquaintance, Jensen Huang, who everybody knows now. Back some months ago, he wrote and said at a conference, “Where do I think the next amazing revolution is going to come? And this is going to be flat out one of the biggest ones ever. There's no question that digital biology is going to be it. For the first time in human history, biology has the opportunity to be engineering, not science.” So can you critique Jensen? Is he right? And tell us how you conceive the field of digital biology.Patrick Hsu (26:20):If you look at gene therapy today, the core concepts are actually remarkably simple. They're elegant. Of course, you're missing a broken gene, you need to put it back. And that can be curative. Very simple, powerful concept. However, for complex diseases where you don't have just a single gene that goes wrong, in many cases we actually have no idea what to do. And in fact, when you're trying to put in DNA, that's over more than a gene scale. We kind of very quickly run out of ideas. Is it a CAR and a cytokine, a CAR and a cytokine and another thing? And then we're kind of out of ideas. And so, we started thinking in the lab, how can we actually design genomes where it's not just let's reduce the genome into individual Lego blocks, iGem style with promoters and different genes that we just sort of shuffle the Lego blocks around, but actually use AI to design genome sequences.(27:29):So to do that, we thought we would have to first of all, train a model that can learn and decode the foreign language of biology and use that in order to design sequences. And so, we sort of have been training DNA foundation models and virtual cell models at Arc, sort of a major effort of ours where the first thing that we tried was to take a variance of transformer architecture that's used to train ChatGPT from OpenAI, but instead apply this to study the next DNA token, right? Now, the interesting thing about next token prediction in English is that you can actually learn a surprising amount of information by just predicting the next word. You can learn world knowledge is the capital of Azerbaijan, is it Baku or is it London, right? Or if you're walking around in the kitchen, then the next text is, I then left the kitchen or the bathroom, right?(28:33):Now you're learning about spatial reasoning, and so you can also learn translation obviously. And so similarly, I think predicting the next token or the next base and DNA can lead you to learn about molecular biochemistry, is the next amino acid residue, hydrophobic or hydrophilic. And it can teach you about the mechanics of some catalytic binding pocket or something. You can learn about a disease mutation. Is the next base, the sick linked base or the wild type base and so on and so forth. And what we found was that at massive scale, DNA foundation models learn about molecular function, not just at the DNA level, but also at the RNA and the protein. And indeed, we could use these to design molecular systems like CRISPR-Cas systems, where you have a protein and the guide RNA. It could also design new DNA transposons, and we could design sequences that look plausibly like real genomes, where we generate a megabase a million bases of continuous genome sequence. And it really looks and feels like it could be a blurry picture of something that you would actually sequence. This has been a wonderful collaboration with Brian Hie, a PI at Stanford and an Arc investigator, and we're really excited about what we've seen in this work because it promises the better performance with even more scale. And so, simply by scaling up these models, by adding in more compute, more training data or more powerful models, they're going to get sharper and sharper.New A.I. Models in Life ScienceEric Topol (30:25):Yeah. Well, this whole use of large language models for the language of life, whether it's the genome proteins and on and on, actually RNA and even cells has really taken root. And of course, this is really one of the foundations of that field of digital biology, which brings together generative AI, AI tools and trying to push forward our understanding in biology. And also, obviously what's been emphasized in drug discovery, perhaps it's been emphasized even too much because we still have a lot to learn about biology, but that gets me to these models. Like today, AlphaProteo was announced by DeepMind, as we all know, AlphaFold 1, 2, now 3. They were kind of precursors of being able to predict proteins from amino acid 3D structure. And that kind of took the field by a little bit like ChatGPT for life science, but now it's a new model all the time. So you've been working on various models and Arc Institute, how do you see this unfolding? Are we just going to have every aspect of the language of life being approached in all the different interactions? And this is going to help us get to a much more deep level of understanding.Patrick Hsu (31:56):I'll say two things. The first is a lot of models that you just described are what I would call task specific models. A model for de novo design of a binder, a model for protein structure prediction. And there are other models for protein fitness or for RNA structure prediction, et cetera, et cetera. And I think what we're going to move towards are more unifying models where there's different classes of models at different levels of scale. So we will have these atomic level models for looking at generative chemistry or ligand docking. We have models that can unify genomes and their molecules, and then we have models that can unify cells and tissues. And so, for example, if you took an H&E stain of some liver, there are folks building models where you can then predict what the single cell spatial transcriptome will look like of that model. And that's obviously operating at a very different level of abstraction than a de novo protein binder. But in the long run, all of these are going to get, I think unified. I think the reason why this is possible is that biology, unlike physics, actually has this unifying theory of evolution that runs across all of its length scales from atomic, molecular, cellular, organismal to entire ecosystem. And the promise of these models is no short then to make biology a predictive discipline.Patrick Hsu (33:37):In physics, the experimentalists win the big prizes for the theorists when they measure gravitational waves or whatever. But in biology, we're very practical people. You do something three times and do a T-test. And I think my prediction is we can actually gauge the success of these LLMs or whatever in biology by how much we respect theory in this field.The A.I. ScientistEric Topol (34:05):Yeah. Well, that's a really interesting perspective, an important perspective because the proliferation of models, which we're going to get into not just doing the things that you described, but also being able to be “pseudo” scientists, the so-called AI scientist. Maybe you could comment about that concept because that's been the idea that everything from the question that could be asked to the hypothesis and the experiment design and the analysis of data and then the feedback. So what is the role of the scientists, that seems to have been overplayed? And maybe you can put that in context.Patrick Hsu (34:48):So yeah, right now there's a lot of excitement that we can use AI agents not just to do software enterprise workflows, but to be a research assistant. And then over time, itself an autonomous research scientist that can read the literature, come up with an idea, maybe run a bunch of robots in the lab or do a bunch of computational analyses and then potentially even analyze data, conclude what is going on and actually write an entire paper. Now, I think the vision of this is compelling in the long term. I think the question is really about timescale. If you break down the scientific method into its constituent parts, like hypothesis generation, doing an experiment, analyzing experiment and iterating, we're clearly going to use AI of some kind at every single step of this cycle. I think different steps will require different levels of maturity. The way that I would liken this is just wet lab automation, folks have dreamed about having pipetting robots that just do their western blots and do their cell culture for them for generations.(36:01):But of course, today they don't actually really feel fundamentally different from the same ones that we had in the 90s, let's say. Right? And so, obviously they're getting better, but it seems to me one of the trends I'm very bullish about is the explosion of humanoid robots and robot foundation models that have a world model and a sense of physics and proportionate space loaded onto them. Within five years, we're going to have home robots that can fold your clothes, that can organize your kitchen and do all of this while you're sleeping, so you wake up to a clean home every day.Eric Topol (36:40):It's not going to be just Roomba anymore. There's going to be a lot more, but it isn't just the hardware, it's also the agents playing in software, right?Patrick Hsu (36:50):It's the integrated loop of the hardware and the software where the ability to make the same machine generally intelligent will make it adaptable to a broad array of tasks. Now, what I'm excited about is those generally intelligent humanoid robots coming into the lab, where instead of creating a centrifuge or a new type of pipetter that's optimized for your Beckman or Hamilton device, instead you just have robot arms that you snap onto the edge of the bench and then they just work alongside you. And I do think that's coming, although it'll take a lot of hardware and software and computer vision engineering to make that possible.A Sense of HumorEric Topol (37:32):Yeah, and I think also going back to originating the question, there still is quite a debate about the creativity and the lack of any simulation of AGI, whatever that means anymore. And so, the human in the loop part of this is obviously I think it's still of critical nature. Now, the other thing I learned about you is you have a great sense of humor, which is really important by the way. And recently, which is great that you're active on X or Twitter because that's one way we get to see what you're thinking on a day-to-day basis. But I think you put out a poll which was really quite provocative , and it was about, here's what it said, “do more people in the world *truly* understand transformers or health insurance?” And interestingly, you got 49% for transformers at 51% for health insurance. Can you tell us what you're thinking when you put that poll together? Because obviously a lot of people don't understand either of these.Patrick Hsu (38:44):I think the core question is, there are different ways of looking at the world, some of which are very bottom up and some of which are very top down. And one of the very surprising things about transformers is they're taking something that is in principle, an incredibly simple task, which is if you have a string of text, what is the next letter? And somehow at massive, massive scale, you can unlock something that looks an awful lot like reasoning, and you've got these emergent behaviors. Now the bottoms up theory of just the linear algebra that's going on in these models couldn't possibly really help us predict that we have these emerging capabilities. And I think similarly in healthcare, there's a literal set of parts that are operating in some complex way that at massive scale becomes this incredibly confusing and dynamic system for how we can actually incentivize how we make medicines, how we actually take care of people, and how we actually pay for any of this from an economic point of view. And so, I think it was, in some sense if transformers can actually be an explainable by just linear algebra equations, maybe there will be a way to decompose the seemingly incredibly confusing world of healthcare in order to actually build a better way forward.Computing Power and the GPU Arms RaceEric Topol (40:12):Yeah. Well that's great. Now the other thing I wanted to ask you about, we open source and the arms race of GPUs and this whole kind of idea is you touched on the need for coalescing a lot of these tools to exploit the synergy. But we have an issue because many academic labs like here at Scripps Research and so many others, including as I learned even at Stanford, have limited access to GPUs. So computing power of large language models is a problem. And then the models that exist today that can be adopted like Llama or others, and they're somewhat limited. And then we also have a movement towards trying to make things more open source, like for example, recently OpenCRISPR with Profluent Bio that is basically trying to use AI for CRISPR guides. And so, how do you deal with this arms race, computing power, open source, proprietary models that are not easily accessible without a lot of resources?Patrick Hsu (41:30):So the first thing I would say is, we are in the academic science sphere really unprepared for the level of resources that are required for doing this type of cutting edge computational work. There are top Stanford computer science professors or computational researchers who have a single GPU in their office, and that's actually what their whole lab runs off of.(41:58):The UC Berkeley campus, the grid runs on something like 12 megawatts of power and how are they going to build an on-premises GPU clusters, like a central question that can scale across the entire needs? And these are two of the top computer science universities in the world. And so, I think one of our kind of core beliefs at Arc is, as science both experimentally and computationally has gotten incredibly complex, not just in terms of conceptually, but also just the actual infrastructure and machines and know-how that you need to do things. We actually need to essentially support this. So we have a private GPU cloud that we use to train our models, and we have access to significantly large clusters for large burst kind of train outs as necessary. And I think infrastructurally for running genomics experiments or doing scalable brain organoid screens, right, we're also building out the infrastructure to support that experimentally.Eric Topol (43:01):Yeah, no, I think this is one of the advantages of the new model like the Arc Institute because not many centers have that type of plasticity with access to computing power when needed. So that's where a brilliant mind you and the Arc Institute together makes for a formidable recipe for future advances and of course building on the ones you've already accomplished.The Primacy of Human TalentPatrick Hsu (43:35):I would just say, my main skill, if I have one, is to recruit really, really smart people. And so, everything that you're seeing and hearing about is the work of unbelievable colleagues who are curious, passionate, and incredible scientists.Eric Topol (43:53):But it also takes the person who can judge those who are in that category set as a role model. And you're certainly doing that. I guess just in closing, I mean, it's just such a delight to get to meet you here and kind of get your thoughts on what is the hottest thing in life science without question, which brings together the fields of AI and what's going on, not just obviously in genome editing, but this digital biology era that we're still in the early phases of, I mean, I think you could say that it's just going to continue to accelerate the exponential curve. We're still kind of on the bottom of that, I would imagine where we're headed. Any other things that you want to bring up that I haven't touched on that will round out this conversation?Patrick Hsu (44:50):I mean, I think it's very early days here at Arc.Patrick Hsu (44:53):When we founded Arc, we asked ourselves, how do we measure success? We don't have customers or revenue in the way that a typical startup does. And we felt sort of three things. The first was research institutes live and die by their talent. Can we actually hire incredible people when we make offers to people we want to come, do they come? The second was, when those folks do come to Arc, do they feel like they're able to work on important research programs that they couldn't do sort of at their prior university or company? And then longer term, the third thing was, and there's just no shortcut around this, you need to do important work. And I think we've been really excited that there are early signs that we're able to do all three of these things, and we're still, again, just following the same scaling laws that we're seeing in natural language and vision, but for the domain of biology. And so, we're excited about what's ahead and think if there are folks who are interested in learning more about Arc, just shoot me an email or DM.Eric Topol (46:07):Yeah, well I would just say, congratulations on what you've already achieved. I know you're going to keep rocking it because you already have in a short time. And for anybody who doesn't know about Arc Institute and your work and your team, I hope this is going to be putting them on notice actually what can be accomplished outside of the usual NIH funded model, which is kind of a risk-free zone where you basically have to have your results nailed down before you send in your proposal frequently, and it doesn't do great things for young people. Really, I think you actually qualify in that demographic where it's hard for them to break in for getting NIH grants and also for this type of work that you're doing. So we'll look for the next bridge beyond bridge RNAs of your just fantastic efforts. So Patrick, thanks so much for joining us today, and we'll be checking back with you and following all the great work that you'll be doing in the times ahead.Patrick Hsu (47:14):Thanks so much, Eric. It was such a pleasure to be here today. Appreciate the opportunity.*******************Thanks for listening, reading or watching!The Ground Truths newsletters and podcasts are all free, open-access, without ads.Please share this post/podcast with your friends and network if you found it informative!Voluntary paid subscriptions all go to support Scripps Research. Many thanks for that—they greatly help fund our summer internship programs.Thanks to my producer Jessica Nguyen and Sinjun Balabanoff for audio and video support at Scripps Research.Note: you can select preferences to receive emails about newsletters, podcasts, or all I don't want to bother you with an email for content that you're not interested in. Get full access to Ground Truths at erictopol.substack.com/subscribe

Martin Gonzalez is the creator of Google's Effective Founders Project, a global research program that uses people analytics to uncover what makes the best startup founders succeed and shares their success formula with the world. He has run leadership courses for thousands of tech startup founders across seventy countries in the Americas, Asia, Africa, and Europe. He is a frequent lecturer on entrepreneurship, organization design, and people analytics at Stanford, Wharton, and INSEAD. He is also the author of the bestselling book, The Bonfire Moment: Bring Your Team Together to Solve the Hardest Issues Startups Face. Martin is a principal of organization and leadership development at Google. He works with Google's senior leaders to shape team culture, develop their people, and expand their leadership, so they can build cool things that matter. In his ten years there, he's worked with leaders across Google Research, DeepMind, Technology & Society, Responsible AI, Pixel, Fitbit, YouTube, Search, Maps, Android, and Chrome, to name a few.  In 2023, The Aspen Institute recognized him as a First Movers Fellow, honoring his pioneering work at Google. In 2024, he was featured on the Thinkers50 Radar List, a prestigious recognition dubbed the "Oscars of management thinking" by the Financial Times, highlighting emerging thinkers expected to significantly influence future management thinking. Before Google, he was a management consultant with the Boston Consulting Group and a product manager at Johnson & Johnson.  Martin has studied organizational psychology and behavioral science at Columbia University and the London School of Economics. He's a serial immigrant, having lived and worked in New York, Jakarta, Singapore, Taipei, and Manila, where he is originally from. Today, he lives in the San Francisco Bay Area with his wife, Bea, and three kids: Noelle, Jaime, and Andrea.A Quote From This Episode"Startups have a people problem."Resources Mentioned in This EpisodeWebsite/Book - The Bonfire Moment Book - Creative Construction by Gary PisanoAbout The International Leadership Association (ILA)The ILA was created in 1999 to bring together professionals interested in studying, practicing, and teaching leadership. Register for ILA's 26th Global Conference in Chicago, IL - November 7-10, 2024.About  Scott J. AllenWebsiteWeekly Newsletter: The Leader's EdgeBlogMy Approach to HostingThe views of my guests do not constitute "truth." Nor do they reflect my personal views in some instances. However, they are views to consider, and I hope they help you clarify your perspective. Nothing can replace your reflection, research, and exploration of the topic.

Professor Hannah Fry is joined by Jeff Dean, one of the most legendary figures in computer science and chief scientist of Google DeepMind and Google Research. Jeff was instrumental to the field in the late 1990s, writing the code that transformed Google from a small startup into the multinational company it is today. Hannah and Jeff discuss it all - from the early days of Google and neural networks, to the long term potential of multi-modal models like Gemini.Thanks to everyone who made this possible, including but not limited to: Presenter: Professor Hannah FrySeries Producer: Dan HardoonEditor: Rami Tzabar, TellTale Studios Commissioner & Producer: Emma YousifMusic composition: Eleni Shaw Camera Director and Video Editor: Tommy BruceAudio Engineer: Perry RogantinVideo Studio Production: Nicholas DukeVideo Editor: Bilal MerhiVideo Production Design: James BartonVisual Identity and Design: Eleanor TomlinsonCommissioned by Google DeepMind Want to share feedback? Why not leave a review on your favorite streaming platform? Have a suggestion for a guest that we should have on next? Leave us a comment on YouTube and stay tuned for future episodes.

News includes the upcoming signed installers for Livebook and Elixir on Windows, the release of Telemetry v1.3 with improved documentation, LiveView Native 0.3.0's announcement ahead of ElixirConf, Google Research introducing an alternative SQL syntax with a pipe, a Livebook leveraging LLMs and FFMPEG for media conversion, legal updates on the US non-compete agreements ban, and potential antitrust actions against Google, and more! Show Notes online - http://podcast.thinkingelixir.com/218 (http://podcast.thinkingelixir.com/218) Elixir Community News - https://x.com/josevalim/status/1825954736094457943 (https://x.com/josevalim/status/1825954736094457943?utm_source=thinkingelixir&utm_medium=shownotes) – The next versions of Livebook and Elixir will have signed installers on Windows, thanks to the Erlang Ecosystem Foundation and Wojtek Mach. - https://x.com/wojtekmach/status/1826521109476344035 (https://x.com/wojtekmach/status/1826521109476344035?utm_source=thinkingelixir&utm_medium=shownotes) – Wojtek Mach discusses the challenges of packaging Livebook into a .msix for the Windows Store and asks for contributions from those familiar with the process. - https://hexdocs.pm/telemetry/1.3.0/readme.html (https://hexdocs.pm/telemetry/1.3.0/readme.html?utm_source=thinkingelixir&utm_medium=shownotes) – Telemetry v1.3 is out with improved documentation, rewritten to ExDoc from Erlang edoc, thanks to contributions from Wojtek Mach and Andrea Leopardi. OTP 27 is required. - https://x.com/bcardarella/status/1826266402631889091 (https://x.com/bcardarella/status/1826266402631889091?utm_source=thinkingelixir&utm_medium=shownotes) – LiveView Native 0.3.0 is now released with the official announcement at ElixirConf. Blog posts, tutorials to follow. - https://x.com/bcardarella/status/1826279303623082421 (https://x.com/bcardarella/status/1826279303623082421?utm_source=thinkingelixir&utm_medium=shownotes) – Additional details about the LiveView Native 0.3.0 release. - https://twitter.com/simonw/status/1827482890680332386 (https://twitter.com/simonw/status/1827482890680332386?utm_source=thinkingelixir&utm_medium=shownotes) – Google Research released a paper on an alternative SQL syntax with a pipe, similar to Ecto querying syntax. - https://simonwillison.net/2024/Aug/24/pipe-syntax-in-sql/ (https://simonwillison.net/2024/Aug/24/pipe-syntax-in-sql/?utm_source=thinkingelixir&utm_medium=shownotes) – More details on the new SQL syntax introduced by Google for ZetaSQL. - https://twitter.com/ac_alejos/status/1794105872680972458 (https://twitter.com/ac_alejos/status/1794105872680972458?utm_source=thinkingelixir&utm_medium=shownotes) – A Livebook that uses LLMs and FFMPEG to simplify the process of converting videos or audio by suggesting the right flags and switches. - https://github.com/acalejos/CinEx (https://github.com/acalejos/CinEx?utm_source=thinkingelixir&utm_medium=shownotes) – Detailed information on using LLMs within Livebook for conversion tasks. - https://www.reuters.com/legal/us-judge-strikes-down-biden-administration-ban-worker-noncompete-agreements-2024-08-20/ (https://www.reuters.com/legal/us-judge-strikes-down-biden-administration-ban-worker-noncompete-agreements-2024-08-20/?utm_source=thinkingelixir&utm_medium=shownotes) – A US Judge struck down the FTC's ban on non-compete agreements, stating the FTC lacks legal authority and the ban is too wide-reaching. - https://www.nytimes.com/2024/08/13/technology/google-monopoly-antitrust-justice-department.html (https://www.nytimes.com/2024/08/13/technology/google-monopoly-antitrust-justice-department.html?utm_source=thinkingelixir&utm_medium=shownotes) – The US government is considering ordering Google to be broken up following antitrust allegations. - https://www.macrumors.com/2024/08/22/apple-eu-default-app-update/ (https://www.macrumors.com/2024/08/22/apple-eu-default-app-update/?utm_source=thinkingelixir&utm_medium=shownotes) – Apple might allow EU residents to delete apps currently blocked from removal, addressing app store issues in the EU. - Living in a time when industry rules are being challenged creates opportunities for new businesses and markets, as highlighted by ongoing legal issues with major tech companies like Google and Apple. Do you have some Elixir news to share? Tell us at @ThinkingElixir (https://twitter.com/ThinkingElixir) or email at show@thinkingelixir.com (mailto:show@thinkingelixir.com) Find us online - Message the show - @ThinkingElixir (https://twitter.com/ThinkingElixir) - Message the show on Fediverse - @ThinkingElixir@genserver.social (https://genserver.social/ThinkingElixir) - Email the show - show@thinkingelixir.com (mailto:show@thinkingelixir.com) - Mark Ericksen - @brainlid (https://twitter.com/brainlid) - Mark Ericksen on Fediverse - @brainlid@genserver.social (https://genserver.social/brainlid) - David Bernheisel - @bernheisel (https://twitter.com/bernheisel) - David Bernheisel on Fediverse - @dbern@genserver.social (https://genserver.social/dbern)

In April, Google DeepMind published a paper that boasts 57 authors, including experts from a range of disciplines in different parts of Google, including DeepMind, Jigsaw, and Google Research, as well as researchers from academic institutions such as Oxford, University College London, Delft University of Technology, University of Edinburgh, and a think tank at Georgetown, the Center for Security and Emerging Technology. The paper speculates about the ethical and societal risks posed by the types of AI assistants Google and other tech firms want to build, which the authors say are “likely to have a profound impact on our individual and collective lives.” Justin Hendrix the chance to speak to two of the papers authors about some of these issues:Shannon Vallor, a professor of AI and data ethics at the University of Edinburgh and director of the Center for Technomoral Futures in the Edinburgh Futures Institute; andIason Gabriel, a research scientist at Google DeepMind in its ethics research team.

A 3D Map of the Human Brain has been created by a collaboration between Harvard researchers and Google Research analysing 1,400 terabytes of data.

Aimar Bretos entrevista a Pilar Manchón, directora senior de estrategia de investigación de Google Research.

Aimar Bretos entrevista a Pilar Manchón, directora senior de estrategia de investigación de Google Research.

Aimar Bretos entrevista a Pilar Manchón, directora senior de estrategia de investigación de Google Research.

Google researcher Blaise Agüera y Arcas spends his work days developing artificial intelligence models and his free time conducting surveys for fun. He tells Steve how he designed an algorithm for the U.S. Navy at 14, how he discovered the truth about printing-press pioneer Johannes Gutenberg, and when A.I. first blew his mind. SOURCE:Blaise Agüera y Arcas, fellow at Google Research. RESOURCES:Who Are We Now?, by Blaise Agüera y Arcas (2023)."Artificial General Intelligence Is Already Here," by Blaise Agüera y Arcas and Peter Norvig (Noema Magazine, 2023)."Transformer: A Novel Neural Network Architecture for Language Understanding," by Jakob Uszkoreit (Google Research Blog, 2017)."Communication-Efficient Learning of Deep Networks from Decentralized Data," by H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas (arXiv, 2016)."How PhotoSynth Can Connect the World's Images," by Blaise Agüera y Arcas (TED Talk, 2007)."Has History Been Too Generous to Gutenberg?" by Dinitia Smith (The New York Times, 2001). EXTRAS:"'My God, This Is a Transformative Power,'" by People I (Mostly) Admire (2023)."How to Think About A.I.," series by Freakonomics Radio (2023)."Satya Nadella's Intelligence Is Not Artificial," by Freakonomics Radio (2023)."Yul Kwon (Part 2): 'Hey, Do You Have Any Bright Ideas?'" by People I (Mostly) Admire (2021)."Yul Kwon: 'Don't Try to Change Yourself All at Once,'" by People I (Mostly) Admire (2021).

In this episode of ACM ByteCast, Rashmi Mohan hosts 2021 ACM Fellow Edward Y. Chang, an Adjunct Professor in the Department of Computer Science at Stanford University. Prior to this role, he was a Director of Google Research and President of HTC Healthcare, among other roles. He is the Founder and CTO of Ally.ai, an organization making groundbreaking moves in the field using Generative AI technologies in various applications, most notably healthcare, sales planning, and corporate finance. He's an accomplished author of multiple books and highly cited papers whose many awards and recognitions include the Google Innovation Award, IEEE Fellow, Tricorder XPRIZE, and the Presidential Award of Taiwan. Edward also also credited as the inventor of the digital video recorder (DVR), which replaced the traditional tape-based VCR in 1999 and introduced interactive features for streaming videos. Edward, who was born in Taipei, discusses his career, from studying Operations Research at UC Berkeley to graduate work at Stanford University, where his classmates included the co-founders of Google and where his PhD dissertation focused on on a video streaming network that became DVR. Later, at Google, he worked on developing the data-centric approach to machine learning, and led development of parallel versions of commonly used ML algorithms that could handle large datasets, with the goal of improving the ML infrastructure accuracy to power Google's multiple functions. He also shares his work at HTC in Taipei, which focused on healthcare projects, such as using VR technology to scan a patient's brain; as well as his current interest, studying AI and consciousness. He talks about the challenges he's currently facing in developing bleeding edge technologies at Ally.ai and addresses a fundamental question about the role of human in a future AI landscape.

Join us for another exciting episode of Roofing Road Trips® as Megan Ellsworth visits with Jeffrey Steuben, Cool Roofing Rating Council (CRRC) executive director, and Stuart Ruis, CRRC board chair, for a preview of what to expect at their 2024 Annual Meeting taking place June 5, 2024, at the Palms Casino in Las Vegas, Nevada. Jeffrey and Stuart will share information about the Annual Meeting guest presenters from Google Research, The Western Coatings Technology Center at Cal Poly in San Luis Obispo, and the City of Phoenix, along with more details about what to expect. This one-day event is for CRRC members and non-members who want to obtain timely, accurate and valuable information regarding the latest innovations, trends and discussions relevant to reflective building materials. Get the inside scoop here!   Learn more at RoofersCoffeeShop.com!    Are you a contractor looking for resources? Become an R-Club Member today! https://www.rooferscoffeeshop.com/rcs-club-sign-up    Follow Us!   https://www.instagram.com/rooferscoffeeshop/?hl=en  https://www.facebook.com/rooferscoffeeshop/  https://www.linkedin.com/company/rooferscoffeeshop-com  https://www.tiktok.com/@rooferscoffeeshop    #RoofersCoffeeShop #CRRC #RoofingProfessionals #RoofingContractors #RoofingIndustry   

On this episode of The AI Moment, we discuss two emerging Gen AI trends: Microsoft Copilot's AI revenue potential and LLM research. We also celebrate our latest Adults In the Generative AI Rumpus Room.  The discussion covers: With the most used enterprise software and operating system in the world, Microsoft placed a significant bet on AI with the introduction of Copilot to enterprise users in September 2023. Now Microsoft has unleashed Copilot, making it available to nearly every 365 user. What will the impact be? Is Microsoft poised to generate material revenues from AI in 2024?  LLMs are evolving at lightning speed, in part due to a copious amount of academic research and what it means for the market. More Adults in the Generative AI Rumpus Room: Non-profit Fairly Trained, Google Research.  

New Year's Resolutions: We're Doing It Wrong This month, Kathy and Jyl take a deep dive into their New Year's Resolutions. Just kidding, no diving necessary as we learned something about each other: Neither of us approaches the New Year with an unachievable punch list that will put us under unnecessary pressure. You shouldn't either. Why? Check out this research from the Google: Research suggests that only 9% of Americans who make resolutions complete them.  Research shows that 23% of people quit their resolution by the end of the first week, 43% quit by the end of January, and all but that dwindling 9% quit. Why? Many resolutions are created based on tradition rather than need. "Need" is much more motivating and that need may not come until a random date in the future like July 23rd. Wait until then. Obstacles. For many, obstacles are a dream killer when it comes to resolutions. A missed day at the gym due to snow, a sale on Ben & Jerry's and suddenly, wait, what was your resolution? Size matters. Many create resolutions that are simply too big. Life updates should be done in small chunks so that one might feel several hints of success over a longer period rather than continue eyeing some unlikely win. Accountability. Who needs it? Resolutions are for those still full of hope and energy and the ability to stick to something. Not that we're not, but, well... Thoughts from Jyl: I realized that New Year's resolutions were yet another way I could spend months beating myself up for not reaching success. My current approach is tiered with a few goals that are slam dunks, a couple that are challenging, and one stretch goal, such as writing a book (or in this 2024's case, publishing an audiobook). Thoughts from Kathy: I put a twist on the traditional resolution this year by digging into things I want to rid myself of in 2024. This was a group activity with friends and, after we wrote down the items/people/etc that we would be happier without, we burned those lists as we said goodbye to those things that were going to drag us down. What we agreed on the most? Forgiveness. Yes, even (or especially) when it comes to resolutions. Forgive and Forget, you are under no pressure to perform. If you've made a resolution and it isn't working out, set it aside for a month and see if it's still important. No? Then leave it behind. You didn't need that resolution anyway. All that being said? Of course, we did give ourselves a couple of dreams for 2024. Jyl: Publish Audiobook (stretch goal!) | Lose the riff-raff | Wear the jewelry | Clean the pantry | Career Casual Kathy: Start doing voiceovers | Focus on health but not in an obsessive way | Organize the freezer | Leave work at work This episode was not sponsored by ⁠Any Adventure or Pixie & Pan Vacations⁠ so absolutely check them out! Mentions: Any Adventure and Pixie & Pan Vacations Norwegian Bliss Alaskan Cruise (yes, you should come along) Quail Ridge Bookstore Sing When You Win by Jon Hume (Welcome to Wrexham) What to Expect When You Weren't Expecting by Jyl Barlow Can't get enough of us? Well, you're one of very few. Get to know us!  ⁠⁠Jyl Barlow⁠⁠ has all things Jyl! Also, it's pronounced, “jill.” ⁠⁠Which Way's Up⁠⁠ is Jyl's blog, home of weekly epiphanies and often overshares ⁠⁠What to Expect When You Weren't Expecting⁠⁠ is Jyl's best-selling memoir about her hilarious struggles as a (step)mother. Buy it online at Amazon, Barnes & Noble, Goodreads, and Target! ⁠⁠Kathy Crowley⁠⁠'s Thought for the Day (accessorized with a favorite timepiece and signature scent) can be found on ⁠Instagram⁠. Watch videos of all our Nonsor products on ⁠⁠YouTube⁠⁠ or⁠ ⁠TikTok⁠⁠! Wine & Whine is part of Bearlow Productions and is created Jyl Barlow and Kathy Crowley.

Today Kevin and Laura talk with Fergus Hurley. Fergus is the is the co-founder of Google's AI-powered compliance intelligence platform, Checks. We chat about Google's new AI platform, Gemini. We learn what multimodal AI is and how this is different from Bard and OpenAI. We discuss using Gen AI in the entrainment industry and the writer's strike. We chat about AI bias and even find out about Fergus's favorite Irish whiskey. Fergus spearheaded the creation of the platform by developing a product that leverages Google's sophisticated AI technology to streamline privacy compliance for some of the industry's leading digital applications such as Miniclip, Headspace, and Rovio. Fergus is an AI expert and enthusiast with an extensive background spanning over 15 years in the realms of mobile apps, and digital product development. In his prior role, he served as a zero-to-one focused product manager at Google Play, where he played a pivotal part in enhancing the Android user experience on a global scale. Preceding this, he made significant contributions to Google Research, focusing his expertise on Google Assistant and Waze Carpool.Fergus holds a Master's degree in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology (MIT) and a Bachelor's degree in Electrical Engineering from University College Cork in Ireland.Please visit checks.google.com to learn more about Fergus' current platform.

For the last paper read of the year, Arize CPO & Co-Founder, Aparna Dhinakaran, is joined by a Dat Ngo (ML Solutions Architect) and Aman Khan (Product Manager) for an exploration of the new kids on the block: Gemini and Mixtral-8x7B. There's a lot to cover, so this week's paper read is Part I in a series about Mixtral and Gemini. In Part I, we  provide some background and context for Mixtral 8x7B from Mistral AI, a high-quality sparse mixture of experts model (SMoE) that outperforms Llama 2 70B on most benchmarks with 6x faster inference Mixtral also matches or outperforms GPT3.5 on most benchmarks. This open-source model was optimized through supervised fine-tuning and direct preference optimization. Stay tuned for Part II in January, where we'll build on this conversation in and discuss Gemini-developed by teams at DeepMind and Google Research. Link to transcript and live recording: https://arize.com/blog/a-deep-dive-into-generatives-newest-models-mistral-mixtral-8x7b/To learn more about ML observability, join the Arize AI Slack community or get the latest on our LinkedIn and Twitter.

With all the buzz surrounding AI,  we're missing an understanding of how recent AI advancements affect those in the global South. I talk to Rida Qadri about ways in which generative AI fails to represent those in the Global South, what the implications of these failures are, and what's needed to do better. Rida Qadri is an interdisciplinary scholar focusing on the cultural impacts of generative AI for people and communities in the global south. She is a Research Scientist at Google Research, and has a PhD in Computational Urban Science and Masters in Urban Studies from MIT.Both Rida and I are speaking in our private capacities, and neither Rida's nor my views expressed in this episode necessarily represent those of our respective employers. 

In episode 99 of The Gradient Podcast, Daniel Bashir speaks to Professor Martin Wattenberg.Professor Wattenberg is a professor at Harvard and part-time member of Google Research's People + AI Research (PAIR) initiative, which he co-founded. His work, with long-time collaborator Fernanda Viégas, focuses on making AI technology broadly accessible and reflective of human values. At Google, Professor Wattenberg, his team, and Professor Viégas have created end-user visualizations for products such as Search, YouTube, and Google Analytics. Note: Professor Wattenberg is recruiting PhD students through Harvard SEAS—info here.Have suggestions for future podcast guests (or other feedback)? Let us know here or reach us at editor@thegradient.pubSubscribe to The Gradient Podcast:  Apple Podcasts  | Spotify | Pocket Casts | RSSFollow The Gradient on TwitterOutline:* (00:00) Intro* (03:30) Prof. Wattenberg's background* (04:40) Financial journalism at SmartMoney* (05:35) Contact with the academic visualization world, IBM* (07:30) Transition into visualizing ML* (08:25) Skepticism of neural networks in the 1980s* (09:45) Work at IBM* (10:00) Multiple scales in information graphics, organization of information* (13:55) How much information should a graphic display to whom? * (17:00) Progressive disclosure of complexity in interface design* (18:45) Visualization as a rhetorical process* (20:45) Conversation Thumbnails for Large-Scale Discussions* (21:35) Evolution of conversation interfaces—Slack, etc.* (24:20) Path dependence — mutual influences between user behaviors and technology, takeaways for ML interface design* (26:30) Baby Names and Social Data Analysis — patterns of interest in baby names* (29:50) History Flow* (30:05) Why investigate editing dynamics on Wikipedia?* (32:06) Implications of editing patterns for design and governance* (33:25) The value of visualizations in this work, issues with Wikipedia editing* (34:45) Community moderation, bureaucracy* (36:20) Consensus and guidelines* (37:10) “Neutral” point of view as an organizing principle* (38:30) Takeaways* PAIR* (39:15) Tools for model understanding and “understanding” ML systems* (41:10) Intro to PAIR (at Google)* (42:00) Unpacking the word “understanding” and use cases* (43:00) Historical comparisons for AI development* (44:55) The birth of TensorFlow.js* (47:52) Democratization of ML* (48:45) Visualizing translation — uncovering and telling a story behind the findings* (52:10) Shared representations in LLMs and their facility at translation-like tasks* (53:50) TCAV* (55:30) Explainability and trust* (59:10) Writing code with LMs and metaphors for using* More recent research* (1:01:05) The System Model and the User Model: Exploring AI Dashboard Design* (1:10:05) OthelloGPT and world models, causality* (1:14:10) Dashboards and interaction design—interfaces and core capabilities* (1:18:07) Reactions to existing LLM interfaces* (1:21:30) Visualizing and Measuring the Geometry of BERT* (1:26:55) Note/Correction: The “Atlas of Meaning” Prof. Wattenberg mentions is called Context Atlas* (1:28:20) Language model tasks and internal representations/geometry* (1:29:30) LLMs as “next word predictors” — explaining systems to people* (1:31:15) The Shape of Song* (1:31:55) What does music look like? * (1:35:00) Levels of abstraction, emergent complexity in music and language models* (1:37:00) What Prof. Wattenberg hopes to see in ML and interaction design* (1:41:18) OutroLinks:* Professor Wattenberg's homepage and Twitter* Harvard SEAS application info — Professor Wattenberg is recruiting students!* Research* Earlier work* A Fuzzy Commitment Scheme* Stacked Graphs—Geometry & Aesthetics* A Multi-Scale Model of Perceptual Organization in Information Graphics* Conversation Thumbnails for Large-Scale Discussions* Baby Names and Social Data Analysis* History Flow (paper)* At Harvard and Google / PAIR* Tools for Model Understanding: Facets, SmoothGrad, Attacking discrimination with smarter ML* TensorFlow.js* Visualizing translation* TCAV* Other ML papers:* The System Model and the User Model: Exploring AI Dashboard Design (recent speculative essay)* Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task* Visualizing and Measuring the Geometry of BERT* Artwork* The Shape of Song Get full access to The Gradient at thegradientpub.substack.com/subscribe

This episode is sponsored by Celonis ,the global leader in process mining. AI has landed and enterprises are adapting. To give customers slick experiences and teams the technology to deliver. The road is long, but you're closer than you think. Your business processes run through systems. Creating data at every step. Celonis recontrusts this data to generate Process Intelligence. A common business language. So AI knows how your business flows. Across every department, every system and every process. With AI solutions powered by Celonis enterprises get faster, more accurate insights. A new level of automation potential. And a step change in productivity, performance and customer satisfaction Process Intelligence is the missing piece in the AI Enabled tech stack. Go to https:/celonis.com/eyeonai to find out more. Welcome to episode 146 of the Eye on AI podcast. In this episode, host Craig Smith sits down with Viren Jain, a leading Research Scientist at Google in Mountain View, California. Viren, at the helm of the Connectomics team, has pioneered breakthroughs in synapse-resolution brain mapping in collaboration with esteemed institutions such as HHMI, Max Planck, and Harvard.   The conversation kicks off with Jain introducing his academic journey and the evolution of connectomics – the comprehensive study of neural connections in the brain. The duo delves deep into the challenges and advancements in imaging technologies, comparing their progression to genome sequencing. Craig probes further, inquiring about shared principles across organisms, the dynamic behavior of the brain, and the role of electron microscopes in understanding neural structures.   The dialogue also touches upon Google's role in the research, Jain's collaborative ventures, and the potential future of AI and connectomics. Viren also shares his insights into neuron tracing, the significance of combining algorithm predictions, the zebra finch bird's song-learning mechanism, and the broader goal of enhancing human health and medicine.   Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Preview, Introduction and Celonis (06:45) Viren's Academic and Professional Journey (13:17) AI's Technological Progress and Challenges (22:20) Deep Dive into Connectomics (39:20) Google's Role in AI (44:16) Natural Learning vs. AI Algorithms (57:32) Brain Mapping: Present and Future (01:00:33) Brain Studies for Medical Advancement (01:06:05) Final Reflections and Celonis ad  

In the latest episode of Tell Me Why, Jill Blickstein, VP and Chief Sustainability Officer talks about sustainability and why it's so important for our airline. We're on a path to net-zero emissions by 2050, and there is a lot to be excited about, including a recent study American participated in with Google Research and Breakthrough Energy on avoiding contrails in flight.

Abe Murray is a visionary founder, accomplished builder, and distinguished product engineering lead with a remarkable track record in the tech industry. Currently serving as a General Partner at AlleyCorp Robotics, Abe leverages his expertise to drive innovation and excellence in the field of robotics and technology. Abe was previously a product and engineering leader at Alphabet. He launched Play Books and Magazines at Android, delivered $XXXM in business while building the Verily Life Science product teams and Boston office, and shipped AI /ML/computer vision across Google while building the Google Research product team. Before Google, he built a Web 2.0 startup and worked on UAVs in the defense industry.  Abe has some shiny degrees (HBS MBA, WPI MS EE, URI BS Comp Eng) but says he learned the most when he dropped out of high school to run fishing boats and factory lines in the family aquaculture business. In his free time, Abe enjoys being a parent and partner, building all kinds of things with his kids, and staying as healthy as he's able. About VSC Ventures: For 20 years, our award-winning ⁠⁠PR agency VSC⁠⁠ has worked with innovative startups on positioning, messaging, and awareness and we are bringing that same expertise to help climate startups with storytelling and narrative building. Last year, general partners Vijay Chattha and Jay Kapoor raised a $21M fund to co-invest in the most promising startups alongside leading climate funds. Through the conversations on our show CLIMB by VSC, we're excited to share what we're doing at VSC and VSC Ventures on climate innovation with companies like ⁠⁠Ample⁠⁠, ⁠⁠Actual⁠⁠, ⁠⁠Sesame Solar⁠⁠, ⁠⁠Synop⁠⁠, ⁠⁠Vibrant Planet⁠⁠, and ⁠⁠Zume⁠⁠ among many others.

Today's guest is one of the pioneers in generative AI having spent nine years at Google Research building teams that developed breakthrough technologies that led to innovations like the transformer architecture behind ChatGPT.Jad Tarifi co-founded Integral AI in 2021 after a distinguished career in AI roles as a researcher and leader. He received his PhD in Computer Science and AI from the University of Florida and did his undergrad at the University of Waterloo.Thanks to great former guest and friend of the podcast Hina Dixit from Samsung NEXT for the intro to Jad.Listen and learn: Can machines learn common sense? Do humans have common sense? Why Integral AI is providing a “base model for the world” Can machines ever learn as quickly as humans? How to improve the efficiency of LLMs with better algorithms Why the current transformer architecture is poorly designed for next word prediction How to use AI and robotics to create “magic wands” and “crystal balls” How to use AI to do “science at scale” What are the ethical implications of bots that can change the human life span How AGI is related to objective morality Jad's four tenets of a new definition of “freedom” References in this episode… Integral.ai Blake Lemoine and the “sentience” debate Podcastle, generative AI for podcasts (a technology nobody needs)

What if AI could revolutionize healthcare with advanced language learning models? Sarah and Elad welcome Karan Singhal, Staff Software Engineer at Google Research, who specializes in medical AI and the development of MedPaLM2. On this episode, Karan emphasizes the importance of safety in medical AI applications and how language models like MedPaLM2 have the potential to augment scientific workflows and transform the standard of care. Other topics include the best workflows for AI integration, the potential impact of AI on drug discoveries, how AI can serve as a physician's assistant, and how privacy-preserving machine learning and federated learning can protect patient data, while pushing the boundaries of medical innovation. No Priors is now on YouTube! Subscribe to the channel on YouTube and like this episode. Show Links: May 10, 2023: PaLM 2 Announcement April 13, 2023: A Responsible Path to Generative AI in Healthcare March 31, 2023: Scientific American article on Med-PaLM February 28, 2023: The Economist article on Med-PaLM KaranSinghal.com Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @thekaransinghal Show Notes: [00:22] - Google's Medical AI Development [08:57] - Medical Language Model and MedPaLM 2 Improvements [18:18] - Safety, cost/benefit decisions, drug discovery, health information, AI applications, and AI as a physician's assistant. [24:51] - Privacy Concerns - HIPAA's implications, privacy-preserving machine learning, and advances in GPT-4 and MedPOM2. [37:43] - Large Language Models in Healthcare and short/long term use.

Welcome to the newest episode of The Cloud Pod podcast! Justin, Ryan and Matthew are your hosts this week as we discuss all the latest news and announcements in the world of the cloud and AI - including what's new with Google Deepmind, as well as goings on over at the Finops X Conference. Join us!  Titles we almost went with this week:

Today we're joined by Vinodkumar Prabhakaran, a Senior Research Scientist at Google Research. In our conversation with Vinod, we discuss his two main areas of research, using ML, specifically NLP, to explore these social disparities, and how these same social disparities are captured and propagated within machine learning tools. We explore a few specific projects, the first using NLP to analyze interactions between police officers and community members, determining factors like level of respect or politeness and how they play out across a spectrum of community members. We also discuss his work on understanding how bias creeps into the pipeline of building ML models, whether it be from the data or the person building the model. Finally, for those working with human annotators, Vinod shares his thoughts on how to incorporate principles of fairness to help build more robust models.  The complete show notes for this episode can be found at https://twimlai.com/go/617.