Podcasts about alphafold

En este episodio de Mundo Futuro exploramos cómo la inteligencia artificial está entrando en nuevas capas de la vida cotidiana, la creatividad y la ciencia. Primero hablamos de Text to Song, la tendencia viral que convierte conversaciones reales en canciones usando IA. Chats de WhatsApp, peleas familiares, rupturas amorosas y dramas cotidianos se transforman en música, abriendo una nueva pregunta: ¿la creatividad del futuro será más técnica o más emocional? Después entramos a la historia de Demis Hassabis, fundador de DeepMind, protagonista del libro The Infinity Machine y una de las mentes más importantes de la inteligencia artificial moderna. De los videojuegos y Atari, al ajedrez, Go, AlphaGo, AlphaFold y el Premio Nobel, su historia muestra cómo la IA pasó de ganar juegos a resolver problemas científicos reales. También hablamos de Isomorphic Labs, el nuevo proyecto derivado de DeepMind que busca acelerar el desarrollo de medicamentos con inteligencia artificial. Una empresa que acaba de levantar miles de millones de dólares con una ambición enorme: usar IA para transformar la medicina y, eventualmente, curar enfermedades que hoy parecen imposibles. Un episodio sobre música viral, creatividad artificial, ciencia computacional y el tipo de inteligencia que podría cambiar el futuro de la humanidad. Learn more about your ad choices. Visit megaphone.fm/adchoices

从炼金术士熬煮尿液寻找磷火，到AI在0.96埃的精度内破解蛋白质折叠——化学，这门曾经靠盲目试错、烟熏火燎的古老学科，正经历一场前所未有的智能革命。AlphaFold 2一年完成了人类几万年才能做完的事，药物设计、材料合成、碳中和……微观世界的大门被算力彻底撞开。这不仅是化学的终极篇章，更是属于每一位年轻人的“造物主时代”邀请函。科学没有终点，你的好奇心，就是下一支魔杖。

Can we put the data centers in space? Neil deGrasse Tyson and co-hosts Chuck Nice and Gary O'Reilly map out the future of human habitation, research, and industry in low Earth orbit with Ariel Ekblaw, founder and CEO of the Aurelia Institute. NOTE: StarTalk+ Patrons can listen to this entire episode commercial-free here:  https://startalkmedia.com/show/the-future-of-space-stations-with-ariel-ekblaw/ Thanks to our Patrons Richard Morgan, Kamila B, Douglas L, Izzi, Robert Lee, Alfredo Giachino, Andy Reinhart, Kacie Blu, Kimberly Freshour, Atmosphere327, Chris Rose, Gsjdhdbdh, Michael Nel, Morgan Shatz, Alfredo Morales, Petr Vlk, FMG, BryN S, Gunner Ford, Ori, Kimberly, David Kříž, Brendan Hanson, Catherine Westbrook, CT Vaughan, Jon West, Luc Gauthier, Smlamartina, DetroitLarry, Dave, Maarten Bakker, Monthen, Alixandria Taylor, Joe Maron, Ben Canty, Stephen Harris, Nandini and Nitin, Angel, Sascha975, Jalene Tangen, Courtney, Marcus, Jorge Coria, Emilio Jaen, Matt Tatro, Nicholas LaLonde, Mark Nicholson, Akira Stiebeling, Brandon Hill, Delphini Papadopoulos, Mauricio Valle, Mark Entel, Leif Callesen, Steven Crofts, Anthony Lofgren, Huzaifa Shabur, Kyle Has the Biggest Shlong in Media, Chase Phyfe, Davin, Greg Gray Lord of Hotdogs, Jeff Kolander, Gosh Dane It

How did a teenage video game designer from London become a Nobel Prize-winning scientist behind one of the most consequential technology efforts in history? Sebastian Mallaby is a senior fellow at the Council on Foreign Relations and author of the new book, The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence which provides an in-depth look into one of the greatest minds behind artificial general intelligence. In this episode, Sebastian and Greg discuss how Hassabis's early immersion in game design and neuroscience shaped his unique approach to artificial intelligence, why groundbreaking science is increasingly happening outside academia, and the tension between scientific discovery and corporate strategy.  *unSILOed Podcast is produced by University FM.* Episode Quotes: Why AI is becoming an ‘infinity machine' 03:01: It struck me that two breakthroughs in AI pointed to more to come. And these were AlphaGo and then AlphaFold. And what these two things had in common was—you had a sort of massive combinatorial space in both cases. So with Go, because it's a nineteen-by-nineteen board, the very first move, there's three hundred and sixty-one choices, then there's three-sixty for the second one. If you multiply that out, you pretty soon get to a search space which is sort of, you know, approaching infinity in terms of the number of possible permutations in the game. And with proteins, the way they can fold is even bigger. And so in both of these challenges, effectively, you have a machine that can make sense of near infinity of data, so an infinity machine. And once you have that, I figured, well, it's niche for the moment, but it may not stay niche forever. The “Third Way” that helped Google overcome the innovator's dilemma 44:06: The third way is you have a skunkworks, like DeepMind in London, which is a separate entity, and you're letting them kind of be the new policy in waiting, like the fightback policy in waiting. And you don't activate it. But when the moment comes when your competitor embraces the new technology, and you're in danger of falling foul of the innovator's dilemma, then you've got the answer because you've been keeping it ready, and you bring it in, and then you fight back fast. How DeepMind helped Google catch up in the AI race 42:54: How did they, in the space of two and a half years, go from the merger announcement to Gemini 3.0, which was better than the ChatGPT rivals? The key to it is that DeepMind had that top-down strike-team methodology, which came from the video game development world, and they imposed that on the Mountain View team, which was much more bottom-up and kind of inchoate in the research process. And that's what generated Gemini 3.0. That's how they got ahead. Show Links: Recommended Resources: Sebastian Mallaby | unSILOed AlphaGo AlphaFold Gödel, Escher, Bach by Douglas Hofstadter Geoffrey Hinton Mustafa Suleyman Guest Profile: Senior Fellow Profile at Council on Foreign Relations Professional Profile on LinkedIn Guest Work: The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence  The Power Law: Venture Capital and the Making of the New Future  More Money Than God: Hedge Funds and the Making of a New Elite  The Man Who Knew: The Life and Times of Alan Greenspan Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Editor's note: In our first BioHub pod with Priscilla and Mark they discussed their acquisition of EvoScale, led by Alex Rives, who is now Head of Science at BioHub. With ESM-1 they trained language models on millions of protein sequences drawn from across life, with a simple “next token” objective: predict the amino acids that have been randomly masked out, based on the context of the rest of the sequence. But they soon found that these models also learned biological structure and function, including properties the model had never been explicitly shown AND that this ability scales predictably with compute, leading to ESM2 and ESM3.Today, Alex announced ESMFold 2, an open scientific engine to power prediction, design, and discovery across protein biology.Building on Cryo-EM data (discussed in the CZI pod), ESMFold2 reports state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics, and evidence that inference time scaling is also working across five targets in cancer and immunology.In a nod to that other famous AI x protein folding project, they are also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures, which you can play around with on their website. We are honored to work with them for this huge release!One of the refrains we've heard on the Science pod has been that protein folding, materials design, cellular biology, etc. are very different problems from Language Modeling. They definitely are. Yet Alex Rives and the ESM team at BioHub just released a preprint and model, demonstrating that vanilla BERT-like transformer models trained on sufficiently large and diverse data sets can beat specialized models like AlphaFold3 on some of the hardest protein-related problems. Andrew White had a great segment in our first LS-Science episode that explained how mind blowing AlphaFold2 was when it was released in 2020: it suddenly solved problems on a GPU on your desktop that DESRes had built custom-ASIC supercomputer clusters to solve. John Jumper and Demmis Hassabis received the Nobel Prize in Chemistry for this work.AlphaFold2 took advantage of an very clever observation: if multiple species co-evolve pairs of mutations, this implies that the mutations correspond to parts of the protein that are close in 3d space. This is usually shorthanded as MSAs (multi-sequence alignments), and is the key insight which makes AlphaFold2 so effective.Like other inductive biases, however, it hurts generalization.Scale-pilled before it was coolIf you take a look at the timeline for scaling laws for LLMs and release of structure prediction models, the ESM team notably doubled down on their MSAs-be-damned approach after AlphaFold2 released. This obviously requires a great deal of belief in the scale hypothesis.Why the conviction?ESM developed at a time when many of the scaling laws and the “Bitter Lesson” were proving increasingly correct. AlphaFold2's wild success must have been both exciting and bitterly disappointing. But using MSAs mean that the model is is dependent on training data that contains MSAs in order to be accurate in a given domain. For things like antibodies that don't have MSAs to train on, AlphaFold tends to do poorly.ESM takes a different approach: learn the relationship between different proteins by unsupervised training on as much diversity as you can find (sound familiar?) and then correlate that back to structures know from the Protein Data Bank (PDB) and other sources. In other words, a World Model.World Model for proteins“World Model” is a hype term that I define like this:Use unsupervised training to learn abstract patterns from the data:* The abstraction should be semantic - novel constructions represent things that obey the rules of the real world* The abstraction should be compositional - recombining different patterns leads to novel and often valid constructions* The abstraction should support generalization - it predicts things in the real world it wasn't trained on Once you have a world model, you can attach “heads” to it for downstream tasks: predict properties of a protein, decompose its functional features, or search the representation for proteins that meet design criteria. The two big models BioHub just released under MIT license map directly onto this:* World model → ESMC (a model trained on 2.8 billion sequences)* Structure-prediction head → ESMFold2One of the interesting ways the world model can “predict things” is to generate proteins sequences and then measure the predicted properties, such as binding affinity, in the lab. Alex talks in the episode about validating some of the harder molecules they predicted in the wet-lab. Very cool!Another way is to use mech-interp techniques such as Sparse Auto Encoders (SAEs) to extract semantic features from your model, and then find novel features that predict unknown biology. I won't spoil this part for you: it was one of the highlights of the episode for me!A cell is a computerWe have all heard that genes are like computer programs, but usually the analogy fizzles after that. Of course genes are transcribed into RNA and RNA is translated into proteins, so genes are programs for building proteins, but that carries the analogy only to “binary digits are programs.” Here's a better analogy: you can think of the cell nucleus as a storage device / storage controller, the ribosome as a JIT-compiler and runtime, and the semantic features that we learn from our world model via SAEs as functions, proteins as processes that interact together in workflows (signalling pathways) to produce behaviors and outputs (phenotypes). Like functions, the SAE features have a hierarchical composition from local, secondary and tertiary structures (mimicing protein structure), but also motifs that are conceptual, such as membrane integrations, disordered regions and disulfide bonds. As we learn to compose these features we into novel protein designs, we move further towards programmable biology. Alex goes into much more detail about this in the episode, as well as:* Principles for new data collection* BioHub's vision* Modeling the cellEnjoy!Full Video podcastplease like and subscribe!* X: https://x.com/alexrives* LinkedIn: This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

How is AI transforming accessibility for indie authors — and why should you care even if you consider yourself able-bodied? What happens when the tools designed to help people with disabilities end up making everyone's creative business better? Jeff Adams, accessibility expert and romance author, explores how AI is opening doors that were previously closed. In the intro, Spotify Audiobook Innovations; The Economics of Convention Life [The Indy Author]; Friction in your Author Business [Self-Publishing with ALLi]. Today's show is sponsored by Draft2Digital, self-publishing with support, where you can get free formatting, free distribution to multiple stores, and a host of other benefits. Just go to www.draft2digital.com to get started. This show is also supported by my Patrons. Join my Community at Patreon.com/thecreativepenn Jeff Adams is the author of YA thrillers and gay romance, and the co-author of Content for Everyone, a practical guide for creative entrepreneurs to produce accessible and usable web content. You can listen above or on your favorite podcast app or read the notes and links below. Here are the highlights and the full transcript is below. Show Notes How ending a long-running podcast made space for more writing — and how to know when it's time to let go of a good thing What accessibility really means for indie authors and why your digital content might be excluding part of your audience How AI agents like Claude Cowork are removing physical and cognitive barriers for authors with disabilities, chronic pain, or limited energy The culture of shame around AI use in the writing community and why blanket anti-AI statements can be ableist Practical tools including NotebookLM, ElevenReader, and ChatGPT for marketing copy, metadata management, and multimodal research Exciting futures in personalised reading, real-time translation, and AI browser agents that could change how everyone interacts online You can find Jeff at JeffAdamsWrites.com. Jeff also now has a SubStack at contentforeveryone.substack.com Transcript of the interview with Jeff Adams Jo: Jeff Adams is the author of YA thrillers and gay romance, and the co-author of Content for Everyone, a practical guide for creative entrepreneurs to produce accessible and usable web content. Welcome back to the show, Jeff. Jeff: Thanks so much, Jo. It's good to be back. Jo: It is. You were last on the show in March 2023, so over three years ago now. Give us a bit of an update on your writing and publishing business and what it looks like at the moment. Jeff: Sure. I think the biggest thing that happened is that my husband Will, who is also a writer, we ended the Big Gay Fiction Podcast at the end of 2024, after 470-something episodes. It was basically time to do that. So we both focused on writing from that point. In 2025 we had some of our biggest successes in getting writing out into the world. I refound my groove—my difficulty in writing went away finally. We talked a little bit about that back in 2023 too. Will started a new pen name and started producing again, and it was really good to be able to move in that direction. Jo: Was this the hockey romance that really hit at the right time? Jeff: You know, I wish I could have capitalised more on Heated Rivalry when it came out, but I did get hockey books out, and I think I did get to ride that wave a little bit there too. Jo: Yes, and if people don't know about that, that was a super popular streaming series. Was that based on a book? Jeff: It was, yes. Rachel Reid was the author of that book and that series that then Jacob Tierney optioned and made into what fairly turned into a global phenomenon at the end of 2025. Jo: Yes, absolutely. Although I particularly liked Red, White and Royal Blue. That was the one I liked. Not so much into hockey. But anyway, I just wanted to ask you about the Big Gay Fiction Podcast. As you say, you did hundreds of episodes over many years. You and I met over podcasting. You've had lots of connections with people. You ended it, and I know you struggled with ending it, but it sounds like it went really well for you. So maybe you could talk a bit about— How do you know when it's time to end something—a good thing rather than something bad? Does that make more space for writing, essentially? Jeff: It absolutely did make more space for writing for both of us, in particular for me because I have a day job. I balance everything on the creative side with the day job. Will and I had been talking about it for over a year. It just was like, it's really time. After nine years, getting to that 470 mark, we thought about trying to get to 10 years and we thought about, if not 10, then getting to 500 and ending on a milestone. As we looked at everything in our creative business, it was like, this is fun, we enjoy it, but we're not getting as much out of it as we might be if we were actually also writing books, which we also really want to do. It became a time thing and what was the best use of the time. We absolutely miss it occasionally. The whole Heated Rivalry thing, I would've loved to have had episodes to talk about that on, but in the long run, it was worth it. Jo: I mean, one of the things with a podcast, particularly around fiction, was that it was a marketing angle for your fiction. This show is a marketing angle mainly for my nonfiction. So what did you replace the podcast with, in terms of book marketing? Jeff: It was really stepped-up email marketing. I'd always had a list. Will started a list, of course, as he started his new pen name. So it was really turning on that, focusing on that, getting some email marketing with a Bargain Booksy and a Fussy Librarian and a BookBub occasionally to do that work. To be honest, even though we covered things in our genre that if you like what we're talking about, you should like our books, there was never as much of a connection there as you'd want there to be. Even from that book marketing angle, these other things that we can do, it's also a better spend of the money to get those types of promos than it was to continue running the show. Jo: Yes, that is interesting. I mean, obviously I think about podcasting a lot since I have this one, and I put Books and Travel on a hiatus and that was meant to help my fiction and definitely didn't help my fiction sales. But I want to bring it back again because I love doing it. Do you have this hankering sometimes? Do you think you'd ever do the podcast again? Because you are also quite into all the technical stuff and all that. Jeff: It's possible. I've toyed with the idea of doing a short accessibility podcast geared towards creatives, tilting to the same audience that Content for Everyone does. Then I come back and look at the time—is my time better served writing new fiction or perhaps starting a Substack, which I also toy with the idea of, for accessibility stuff? So it bounces around in my head to do another show, but I haven't really decided to jump on that yet. Jo: Yes, and I think that waiting is really good. As you say, you quit a big thing and you don't have to rush to fill it again. I love that you guys are writing more books. So I wanted us to talk about that up front because I know people who listen to this show—I encourage people to start podcasts if you want to, but equally it can take a lot of time. So that's fantastic. Now, you mentioned accessibility, and I feel like the word can be quite difficult for people. So let's just start with a definition. What is accessibility? Why do you care and why should we care? Jeff: So accessibility is really about making sure that whatever the thing is, whether it's something out in the physical world or in the online world, that everybody has access to it. Access to the information, access to getting into a building or being able to cross the street appropriately, whatever that is—that the accessibility of the thing is high. So that regardless of who is approaching it, they can interact with whatever the thing is. If we put that into the digital world, it's about making sure that text on a screen can be perceived by anybody, whether they're trying to read it visually or if they're trying to read it through a screen reader or through a braille monitor. Whatever that is, they need to be able to interact with it, get the information they need, do all the functions of whatever it is on the screen. Check out on Amazon, check out at their favourite e-commerce place, be able to get the products in their cart, check out, et cetera. For creatives, it's about the things that we do: the websites that we build for ourselves, the e-commerce platforms that we use, our email marketing, our social media posts. Making all of that as accessible as we can so that we're not perhaps missing a part of our audience or our prospective audience from being able to engage with our work and in turn, hopefully, buy our books and enjoy our books and become a fan. This became important to me because of my day job. I hadn't really considered this—like, I think most people don't—until I started working at UsableNet. It's going to be 15 years I've been at that company come this autumn, and I really started to see the impacts because UsableNet is all about accessibility on the digital front. I really started to learn, being a project manager for them, what all of that meant and how it impacted people who couldn't buy something online, couldn't book a hotel room, couldn't book an airline ticket. It just really became something I got passionate about. I ended up writing the book because I realised that nobody talks to creatives about this. Nobody tells the independent author what they should do to help make their digital stuff accessible so that they don't miss people. I never expected my day job to interact with my creative side so much, but this certainly has over the last few years. Jo: I mean, has it got better? Like we said, you were on here three years ago. We did talk about some of the things around EPUB formats and taking off DRM and what we need to do on our websites—labelling images, for example, and that kind of thing. Do you think accessibility has gotten better? Jeff: I think the awareness of it has improved, both within the creative community and in the broader web ecosphere, that the awareness is better. There's so much knowledge that needs to go into creating something that is accessible. Sometimes there's so much that you have to think about with colours and alt tags on images and all the little bits and pieces, if it doesn't really come to muscle memory, it's easy for it to fall off. There's a survey that's done by WebAIM every year about the top one million homepages out in the universe, and they surveyed those for just the things that an automated scan can detect, which is a small portion of overall accessibility, and the number of errors across that top million actually ticked up this year. Even though there's all these laws around the world—people get sued all the time in the US—the number of errors ticked up for the first time in a few years. So I think the awareness is up, but I think being able to take action on it and make the time to take action on it isn't where it needs to be. Jo: So last time you gave us all those tips. I'll refer people back to that and also to your book Content for Everyone, which has got loads of great stuff in. I wanted to talk to you for this show because I was sitting watching Claude Cowork—now I use Claude Code a lot more—but updating 140 titles on IngramSpark, where me clicking things and there's like 15 clicks per record on IngramSpark updates for pricing, is an absolute nightmare. I was watching the AI do the work and I realised this isn't just saving me time, it's actually saving my wrist and my arm from repetitive strain injury. That's when I thought about this accessibility thing. As you mentioned, for example being physically accessible into a building, say someone's in a wheelchair, they can't necessarily get into a building if there's no ramp. I was thinking that for many years, being an indie author, being a writer online, there's also been these physical barriers because there's a lot of plumbing and clicking for us. So I wondered, starting with an attitude around a shift in who this is opening up to— How is AI starting to help people with these accessibility issues? Jeff: Yes, there's so much opportunity around this. We should note, just to timestamp this, that we're talking on 14th April 2026, because who knows what will change, even in an hour from now. I think Cowork was one of the first things that we saw, and that's only been out since the very top of this year. Being able to do actual agentic tasks. Other things have sort of gotten there, but Cowork really opened it up. You mentioned the repetitive stress that you would've had clicking all of those forms on IngramSpark across 140 books. But there's that type of stress, chronic pain, cognitive drain for somebody who may have some cognitive disability and trying to work through that form. The cognitive energy just might drain out and maybe knock them out for several days after trying to get through that, or the tasks take them multiple days to do. Someone who has lower vision, someone who's trying to work through that form with a screen reader—all of that draws energy, draws focus. Now we've got something where, with plain language, we could say something like: here's all my pricing information, I've logged into IngramSpark, go update these books. Obviously the prompt's going to be a little more than that, but in broad terms, that's what we're going to tell it. Jo: Hmm. Jeff: And being able to have it go through and do the thing. If it gets stuck, have it come back and say, “Hey, I've got trouble with this. Please help me.” That can just free up so much of the drains that people can have—the things that can take them out of doing the part of the work that they need to do for an author business. They can go write the book through whatever process you're going to use to do that, rather than getting caught up in something like having to update all those books on IngramSpark. Jo: You mentioned writing the book there. I have this real sense of being an able-bodied indie author in terms of my computer use and my ability to write a whole book, a 70,000-word thriller that I write regularly. We're all special in some way, but I do have a reasonably normal brain where I can do this work without too much strain. It's hard work, but I can do it. I meet people who are now using AI to help them write, to help them organise their work—maybe someone has dyslexia or ADHD or cognitive issues or pain—there's just so many things that I take for granted that don't affect me. I hear from people who, at this point in time in the community, are almost shamed for using AI to write. So I wanted to bring this up to discuss it under the terms of accessibility. Do you have any thoughts on that? Jeff: I have real difficulty with people who will say anything in the broad range of, “I don't need to use this thing, and therefore you should not either.” Which is adjacent to indie anti-AI speak that there is out there. Certainly we're living right now at probably the highest point that it's ever been, where more and more there's a sentiment towards not using AI for whatever the reason is. I totally respect that people can have concerns about the environment and about energy use and water use, et cetera. Not to mention all the other things that are on the more difficult side of AI. To shame someone who may not be able to put their story out there without the use of that AI, whichever one they're using, or to shame them because they're using AI to run part of their business—updating IngramSpark, doing other things like that—I think it can come down to there being some ableism there. Ther is some privilege behind that too, where they're just like, “I don't need this, and you shouldn't have it either.” I want to give people just a sliver of an idea of what this can mean for someone who is disabled and what AI can unlock for them. There is a person on LinkedIn that I follow whose name is Hannah Desmond. She's an ADHD coach and a former software developer, and very recently she posted this on LinkedIn. This is a paraphrase of what she said, but: having something that can meet you where you are and help you bridge that gap is what I think I have found so helpful about using AI. Here's what I keep coming back to. Without that support, I wasn't more motivated or more capable. I was just stuck. That's the bit that gets lost. We've been taught that struggling is how you know you're doing it properly. So when something reduces the struggle, it can feel wrong—even when it's the thing that actually makes the work possible. Because there's a difference between avoiding thinking and being able to think at all. I think that rounds it up. She's talking about her time as a software developer, but you can apply that to any realm of AI when we're thinking about trying to shame someone for why they may be using it. We may not know that they have a disability because we don't always share that part of ourselves. So I really feel strongly about that and how we are in this culture of shame. Jo: Yes. It drives me up the wall, actually. But I will also say: you don't have to have a disability or accessibility issues in order to use AI in whatever way you personally decide is okay—talking to the listeners now. I think Orna Ross from the Alliance of Independent Authors says it well, which is you should have your own AI policy. So you personally decide where your lines are, how it helps you, what you want to keep for you, and what you want help with. I was also thinking in terms of accessibility around money. Again, for many of us, professional cover design, professional editing, professional human-level translation, these are things that are pretty pricey for many people. So again, this makes it more accessible. One of the reasons we got into the indie way and being indie authors was to try and remove the barriers to entry to people who have been excluded from the environment of publishing. So, yes, it is really hard to talk about this, and yet that's why I wanted to talk about it, because— There's so many variables for each individual and there's no situation that's the same, really, is there? Jeff: No, not at all. The things that I may need to do my work in the most efficient way possible is different from the way that you're going to work, is different than the way my husband's going to work, is different than every other person and the way that they're going to work. Which is why any kind of blanket statement about “I don't need something and therefore you shouldn't need it either” can just be so problematic, because we have no idea what someone else is going through. Either it's a permanent part of their lives or maybe it's something that is happening temporarily with them where they might need to leverage other tools. Jo: Yes. Talking about that temporary, I think I really got the first sense of this when I had COVID the first time, which was really bad. I remember I was so sick, the only thing I could do was listen to an audiobook. I couldn't think, I couldn't read. It was really probably months of not having my brain back. Then the other thing that's happened as I age, as women age, is menopause kicks in and the brain fog is a real thing. I've heard from other people too who've said having Claude or whoever, an AI tool, to help with the brain fog is so important because otherwise I just wouldn't be able to gather my thoughts. Again, as you said— Even if we don't need these things now, it's quite likely we're going to need them at some point, given ageing, given the potential for injury and disease. I mean, we don't escape this alive, do we? Jeff: Yes, that's a great point because unless we're extremely lucky as individuals, we're all likely to have some sort of a disability in our lives at some point. I know for me, as I age and my eyes get more and more tired after being in front of a screen all day for work, and then whatever creative stuff I do in the afternoon on a book—when it comes near bedtime and I do want to read, I probably want to do that with an audiobook, much more audio, especially for any long reading project. That can also be like, if I have a long document or a long article to read, I am likely to give it to ElevenReader, let it load itself up, and then listen to it, because I take the information in better than trying to follow words across a screen. Jo: Yes. Jonathan, my husband, now also listens to a lot of academic papers on ElevenReader. Most of us will know it as where we publish some audiobooks from ElevenLabs, or you can also publish other things there. So it is super useful to think about what we can do with ElevenReader. Another thing that I found really useful recently is NotebookLM. On NotebookLM, there is a free tier. You can put various things in there and then create a custom audio. So this is something I've been doing as part of research. You can put in, say, 10 YouTube videos or some PDFs or your book or whatever, and then you can create a custom audio. Then I'll go for a walk and I'll listen to the custom audio, and then I'll go back and look at the detail of what it was. It gives me the framework of whatever I'm thinking about on a broader level, and then I can come back to the details. So again, it's this multimodal approach that can help us manage our energy, I guess. Jeff: And it's all about the managing of the energy, I think, too. That is a great way to think about the accessibility of it all. You mentioned a great use there for NotebookLM. That could also be putting your book in there and having it help you build a world bible or something like that. Or building marketing materials off of that. There's a lot of things now that NotebookLM can do in terms of helping you create FAQs maybe for a newsletter or for your website, and building video stuff off of the material that it has. So there's a lot of options there, and ever-growing options that can be useful for someone to manage any number of the things that they may need in their creative business. Jo: Yes. In fact, talking about Claude, there are a lot of Claude plugins now, skills and integrations. Shopify just released a Claude plugin and many of us now have Shopify stores. I have a lot of products with a lot of different variations and the metadata. There's so much metadata. And again, I'm just so pleased now that I can work with Cowork and get it to actually update directly into Shopify. In fact, coming back, you mentioned updating alt tags earlier. That's something again that AI could help you update—the back list of your alt tags on a website. I've now got my Cowork doing EPUBs so I could finally update all my EPUBs with back matter and all of this kind of thing. So I feel like perhaps we could go beyond accessibility to talk about amplification. All the things that we didn't do because it was too tiring and we just couldn't be bothered, or it would just be way too much work, that now it's opened up as a possibility because of these tools. Jeff: Absolutely. I mean, you look at a backlist as large as yours and the things that you're now able to do. I didn't know that Claude had a Shopify plugin. So the abilities that we have now to maybe do things in the business that we hadn't before. One of the things I've been working with Claude on is rewriting my website and creating a more proper website for Will. I'm really making sure that it is not only SEO prepared but also GEO prepared, with all the metadata and all the backend code schema that it needs so that LLMs can find me, can understand what I do, can understand the books, branch out to the other areas that it needs to. Doing that through WordPress would've been so much more difficult, even with Claude, that to be able to rewrite the site in a way that is going to let me manage it better so that I will do it on a more consistent basis. Whatever that thing is, we're now able to do these things. That could be updating keywords in Amazon or making sure we're aligned across all of the sales platforms that we might be on and things like that, that Claude can do and do well. Jo: Yes, I think marketing is just the killer app really for people, isn't it? I think most authors do not enjoy marketing. I find Claude better for creative work, for strategic work, for doing work through Cowork or Code, but— ChatGPT with marketing copy is very, very good. So I've actually been using that as we record this. I've got a Kickstarter launching next week, so I've been getting it to do ad copy and social media copy and all that kind of thing. This is stuff when you have to produce—give me 20 taglines, give me 20 hooks, give me another 20 and another 20. I mean, we just cannot do it as humans, right? Jeff: Yes, I have found GPT wildly helpful. I mentioned trying to get Bargain Booksy and Fussy Librarian promos. Jo: Mm. Jeff: And you have to give it the marketing hook, and it can't just be the blurb that's on Amazon—it's got to be something fresh, and they each have slightly different requirements. Having GPT—here's the blurb, give me a dozen different options—and then I may take pieces of all of them and create one of my own. But it reworks that much faster than my brain was ever going to try to find the right thing I want to give to Bargain Booksy. Jo: Yes, you are right. Or it says write this in 300 characters or less. Jeff: Yes. Jo: I do exactly the same. That kind of transformative work can be really good. In fact, there was somebody I know who has been rampantly anti-AI for years and then said, “Would this help me? I have to do a synopsis for an agent, so I've got this 100,000-word book and it needs to be a 10-page synopsis. How would I do that with AI?” So I was encouraging her to take each chapter and ask it to summarise the chapter, and of course read through it and everything. But I mean, doing a synopsis once you've actually written a book—that can be super useful. So I think what we're saying is— There are levels of need in terms of both the author and the audience. Then there are levels of your personal use from one end of the spectrum to the other in terms of how far you want to go in every area of the business. And in that way, it's just different for everyone. Jeff: Yes, and I think getting to that mindset shift that we were talking about a little bit—it can be so easy to dip your toes in. That one author came to you and said, “Do you think it could do this?” And I think that's the beginning exploratory area for perhaps anyone. People are going to hear us talk about this and it might inspire them to go try something that we've talked about. But these things, whether it's Claude or GPT or Gemini or whichever one it is, you can come to it and say, “I'm an author, I have X, Y, Z going on in my life”—whether that's a disability, whether that's a time constraint because you have a day job and maybe you have kids and a family that need your attention—”I have these time constraints, I want to do X, Y, and Z in my business. How can you help me with that?” It's going to tell you what it can do to help you with that. I would even say, if you have the ability to have multiples of these, you could ask the same question to GPT and Claude, and they're going to give you similar answers in some instances, but they may also have different ones because of the abilities that the different platforms have around these things as well. That can help you make that mindset shift of, “Well, now I see that it can do that. Could it also do this?” And then ask it if it could do that. Because I know for me, Jo, I've taken so much from you and your journey with Cowork that it's like, “Oh, she did that. I wonder if I could do this.” And all of that piles on top of itself. Then eventually I think your brain starts to think on its own, “Oh, I have to do this task. Can Claude maybe do this for me? Let's go find out.” Jo: Yes, and if it couldn't do it for you yesterday, you never know, it might be able to do it tomorrow. Jeff: Right? Because I haven't tested yet its new ability to actually use your computer. Jo: Mm. Jeff: And I'm curious what that might open up. Because one of the things that I've seen that I wish it would do is be able to take the EPUB that's on my drive and actually put it into a platform I'm trying to upload to. Cowork on its own hasn't been able to cross that barrier, but I wonder if with computer use added to that, if it could. Like, “here's the EPUB, upload that over there,” be able to pick it from the file picker, essentially. Jo: Yes. I think, well, a little tip for everyone: I wouldn't give access to your entire file system to the AI. Jeff: That's a good point too. Jo: Yes. I have a Claude folder in my drive and it only has access there. So if you put files in that drive, it might be able to do that. But I know what you mean. I have been using it to help me publish things in German on KDP. Now I can use the browser, so you can actually do that. In terms of uploading the actual file, I know what you mean. These things will change. As we record this, again middle of April, we are almost about to get the next models being Mythos, which might be Claude 4.7 Opus, or also ChatGPT has a new model coming, and these models are getting very powerful. With every shift they can do more things. So as you say, the very first thing to do is ask it, “I want to do this—what are my options?” And some of them, for example, doing an AI-narrated audiobook, ChatGPT and Claude don't do that. You want ElevenLabs or one of the other services for that, but they can tell you what your options are. So that's one thing, but I wondered if you have any thoughts on the gaps that you are seeing. You mentioned one there around file uploads, but— What do you hope might come and some of the things that might be exciting if they arrive? Because you never know, they might be here already. Jeff: There's certainly some movement in some areas. One of the things I'll share is, in March I was at the 2026 CSUN Assistive Technology Conference—CSUN is California State University, Northridge—and they've run this conference for some 40 years now. One of the sessions I went to was from Tara Maisel—I hope I'm pronouncing her last name right. She's a senior project manager in books accessibility at Amazon, and she was doing a session specifically on readability. She had all kinds of statistics and information about what goes into making something readable. One of the things she talked about with AI was the future of personalised reading. If you think about the Kindle app, for example, there's a lot of settings you can make there—font size, colours, brightness, text spacing. There's a lot of tools in there. She was pointing out that potentially readers don't even know what they actually need for the optimised visual reading experience. She sees a world where AI can perhaps do an analysis of your reading behaviour and then help you find the optimal settings. Maybe even multiple optimal settings for, say, if you were reading in a room that had daylight versus at bedtime, and the ways you might shift it. I was almost thinking of this like when you're at the optometrist and they're like, “Which lens is better—this one or that one?” Jo: Oh, sometimes that is very hard. Jeff: Yes. It's that AI could step you through that a little bit to help you find that optimal reading experience in that moment. And then it might even notice, potentially, if you're changing something in the way that you're moving through a page, that it might flag to say, “Hey, do we need to adjust something?” Some other areas that I think are really exciting, for everyone and perhaps particularly for people who are disabled and needing the support of some assistive technology, is what we're seeing in the browsers. OpenAI's Operator has been out for quite a while now, since sometime I think autumn of last year. Perplexity Comet has been around even longer. Then we've got browser extensions from Gemini and Claude that are available, that can let you just type natural language. You know, “Please go find for me jeans in this size that are on sale on this website. Find me the best price for blue jeans on this site and this size,” and it'll just go do it. Which can certainly speed things up for people in the disabled community to find things quickly, to spend time navigating less, and maybe ending up with the AI coming back and saying, “I found these five things. Which one would you like me to buy for you?” Or, “I found this one thing that you do need and it's waiting for you in your shopping cart.” The ability for that on the horizon is an amazing jump from an accessibility point of view. But really it's one of those things that accessibility will then help everyone because we can all just shop that way, if we choose to. These are early days for these browsers and these extensions. The other side of it comes back to basic web accessibility too, because I've seen these types of activities not work so well on a site that may not actually be accessible on its own. A great example is something I ran into with Claude Cowork about a month ago. I was testing to see if it could help me navigate and get things uploaded together for a site where I wanted to upload books, knowing again that it's not going to upload the actual file, but it could fill in the metadata from my master database of metadata stuff. There were areas on the site that it actually couldn't hit the button, because the site itself was also not functional to a screen reader. So there are gaps there. It's early days, but I really see that as an interesting future that'll really help people with disabilities—but again, help everybody too, just manage time better. Jo: I know exactly what you mean there. I've done some collaborative work with Claude Code when it's like, “I can't click the button,” and I'm like, well, I'll click the button—you fill in everything else. Jeff: Exactly. Jo: It's actually quite a funny situation. But goodness, coming back to IngramSpark again—these things need APIs. We need better functions. It's funny because I think a lot of traditional publishers have these APIs or backend upload things that you can do. I'm like, well, we need to get to that with these systems. But I think things will change. Another thing that I think has also shifted is the use of voice. Voice for dictation—it used to be with dictation that you would have to say “comma,” “open quote,” “new line,” and all of that. And you'd also have to make sense. Whereas now I feel like you can just dictate a whole load of things to these AIs and then say, “Tidy that up,” and they will do a lot more than the old situation. So I think voice will also help. Also automatic translation. I don't know if you know this about X, and if you're on X anymore, but just this week they've made it multi-language. So I can read tweets by people who've posted in another language in English. I can read something from Korean or read something that someone French has posted and it gets translated. It has made a huge difference to the content I'm seeing, which is fascinating because I don't think we've ever had this kind of automatic “everything is translated into your language” situation. It's really got me thinking about how [automatic translation] might work for eBooks or other things if the rights are there. I don't know. Have you seen stuff like that? Jeff: There's so much available now with voice and the ability to not have to speak all the other stuff that went with it—comma, full stop, next line. It was a little mind-bending sometimes, trying to think about quote marks and all that stuff. And now it's so good. Different platforms do it to different degrees of ability. Even being able to speak your prompts into the very platforms themselves without having to type all of it. Chronic pain comes to mind, any kind of mobility thing—all the typing would be a drain or maybe even impossible. So the voice ability is so powerful there and unlocks more things. At the same time, those translation abilities—I believe AirPods now have the ability, if you've got the right stuff on your phone, that you could be talking to somebody, they may speak back to you in a language you don't speak, but your AirPods will give it to you in your language. Jo: Hmm. Jeff: Google has, I believe, a live captioning app that you can use. I think there's even a split screen—I don't know if that's available now or something in their future—where you could put the phone on the table and tell it who's looking at what side of the screen, and it'll put the language that I need on my side and the language the other person needs on the other. So there continues to be such a shift in how we're being able to translate stuff that really opens up communication and can open up our books to so many more people. I'm very interested to see—I haven't pulled the trigger on this yet—but how Amazon's auto-translation rolls out and how that's received in terms of the accessibility around our books and being able to put it in someone's hands who doesn't speak—I think it's only English to other languages right now—but who doesn't speak the language it was written in but wants to read that book. We could never, as indies, or really even big five publishers, wouldn't have the money to create custom translations everywhere. But if the AI can help do that and spread those books around so that everybody could have the story they want to read, I think that's such a win for the reading audience. Jo: Yes, I think it's so exciting to think what might be coming, and that's what I want to stay on the side of on the AI discussion. There's enough negativity out there and you can get that information somewhere else, but for me I want us to stay on the positive side of how this helps both the author and the reader. And hopefully the community, to create more and read more and enjoy being human more. Right? Because I find that I do get out more and listen to stuff, or I'm out walking instead of at my desk, and I mean, that's what it's about. I'm pretty excited about the future. How about you? Jeff: I am. I think there are, quite honestly, some scary things that could be out there in the future. I mean, there's been a lot of talk about what Mythos is capable of. But on the other side of it, there are all these advances. I also look back at Google and AlphaFold and what DeepMind was able to do there for science. There's more of that stuff out there, and individually for each of us, spending a little bit of time—and I do have to say, I think you need to spend time on a paid plan because the free stuff doesn't give you the idea of what these platforms are actually capable of. So if you only drop in, even briefly, to experiment on one of the $20-a-month plans and give it your situation, ask it what it can do for you, I think you'll see where, on a personal level, AI will help you unlock some things. It can help you move some things to the next level in your business that for whatever reason you haven't been able to do. You don't have to use it for everything. You may decide that it's still not for you for whatever reason, and that's fine. But I think there's so much to explore here and to let your curiosity run for a little bit to see what's possible and what you might unlock with it. Jo: Brilliant. So where can people find you and your books and everything you do online? Jeff: So pretty much everything lives at JeffAdamsWrites.com. Jo: Well, thanks so much for your time, Jeff. That was great. Jeff: I loved it, Jo. Thanks for having me..The post Accessibility And AI: How New Tools Are Opening Doors For Indie Authors With Jeff Adams first appeared on The Creative Penn.

Torkel Klingberg är läkare och professor i kognitiv neurovetenskap vid Karolinska Institutet. Hans forskning handlar om hjärnans utveckling och plasticitet, särskilt arbetsminne, uppmärksamhet och lärande hos barn. Han har varit professor sedan 2007 och är även ledamot av Nobelförsamlingen.00:00 Varför ska vi lära oss något i AI-tider?02:20 Hotet mot barns lärande när AI gör jobbet05:19 Därför behöver hjärnan tränas – skola, IQ och kognitiv utveckling08:36 Betyg, läxor och skola när elever kan använda AI10:31 AI som lärare: från fuskverktyg till personlig coach12:19 Arbetsminne, koncentration och varför fokus är en superkraft16:35 Neuro, Cogmed och träning av arbetsminne17:54 Kognitiv friktion: läs svåra böcker och träna hjärnan20:47 Skärmtid är fel fråga – sociala medier vs dataspel23:35 Huberman, TikTok och opium-liknelsen25:30 Blomstringens psykologi: PERMA, mening och utveckling30:01 Blir AI början på mänsklig blomstring – eller fördumning?32:47 AlphaFold, Nobelpris och AI som accelererar vetenskap34:02 AI och jobben: juniora roller, programmerare och framtidens arbetsmarknad37:13 Blir människan domesticerad av AI?38:07 Kognitiv offloading, Flynn-effekten och risken att vi blir dummare40:00 AI-genererat innehåll, filterbubblor och kampen om sanningen45:49 Ska sociala medier förbjudas för barn?49:56 Synergimodellen: growth mindset, stress och motivation54:52 Framtidstro, barnafödande och varför unga inte får ge upp59:26 Utvecklingsoptimism, Abundance och vad AI kan hjälpa oss lösa01:04:10 Vad politiker borde göra åt AI och sociala medier01:05:18 Praktiska takeaways: träna hjärnan och minska distraktioner01:06:00 Vad händer när unga vuxna minskar mobilen?01:08:45 Avslutning med Torkel Klingberg

Artificial intelligence is fundamentally redefining scientific research and medicine by accelerating discovery cycles and automating complex experimentation. These sources describe a transition from traditional data analysis to a "digital biology" era where AI models like AlphaFold predict protein structures to streamline drug development and clinical diagnostics. Innovations such as symbolic regression allow researchers to uncover interpretable mathematical laws directly from physical data, while automated laboratories enhance productivity. However, the integration of these technologies introduces significant ethical risks, including data privacy concerns, model hallucinations, and high environmental costs. Consequently, experts emphasize the need for rigorous oversight and transparent frameworks to ensure AI serves as a responsible partner in human innovation.

Czy zautomatyzowane agenty AI są gotowe na zarządzanie krytyczną infrastrukturą? W dzisiejszym odcinku Tomek, Wojtek i Sebastian omawiają głośną katastrofę w PocketOS, bezwzględne kulisy procesu Elon Musk vs OpenAI, wielki powrót opłaty reprograficznej w Polsce oraz tajną operację Google, które wgrało model AI na dyski milionów użytkowników.W tym odcinku Brew™️ między innymi:

Hey, Alex here, just got back from the sunny Shoreline Theater in Mountain view, so let me catch you up! This week was definitely Google heavy, we are covering Google's IO conference for the third year in a row, and today we have a special guest, Logan Kilpatrick, is joining to discuss the announced Gemini 3.5 Flash, Google Omni model, and the new Managed Agents offerings. Plus, this week, for the first time, OpenAI announced that AI solved a Math problem that humans couldn't solve for 80 years, Cursor is showing off Composer 2.5 which is partly trained on XAI data, Karpathy joins Anthropic and much more! Let's dive in! P.S - We've announced our upcoming hackathon, Weavehacks-4, June 6-7, I'll be there, we're expecting the seats to run out very soon so register nowThursdAI - We'd love to have your subscription, and if you're already subscribed, please hit that bell on YT to never miss an episode!Google I/O 2026 - Google goes agentic everywhereI went to cover Google I/O for the third year in a row, shoutout to the DeepMind team for inviting ThursdAI again, and folks, this one felt different.Last year, Google I/O was still very model-centric. This year, the story was not “here is another benchmark chart.” The story was: Google is putting Gemini into everything, and the agentic layer is becoming the product layer. Search, Gemini app, Android, Workspace, YouTube, AI Studio, Cloud, Antigravity, Flow, managed agents, smart glasses, all of it is now orbiting around one pretty clear strategy: Gemini is the intelligence, Antigravity is the agent harness, Google's products are the distribution. I saw many reactions that were milquetoast, as in, “we expected more” and those seem to dominate the X feed. But I think the distribution is the part that many folks on X are missing. Yes, we can argue about Gemini 3.5 Flash pricing. Yes, we can argue whether “Flash” still means what Flash used to mean. But when Google says the Gemini app itself has 900 million monthly active users, before even counting Search, Gmail, YouTube, Docs, Drive, Android, and the rest of the Google surface area, that's massive! OpenAI ChatGPT is supposedly stagnated at ~900M, I don't remember them crossing a 1B. Meanwhile Google is gaining traction. And they just updated all those folks with a new model!Wolfram said it really well on the show: his mother is not sitting there reading model cards. She just uses her Pixel, voice unlocks Gemini, asks for help, and suddenly the default intelligence available to her goes up. Antigravity 2.0 - the agent harness takes center stageThe biggest strategic signal from Google I/O for me was Antigravity.Remember, Antigravity was an IDE that came from the Windsurf acquisition saga. Part of the Windsurf team went to Google, part went to Cognition, and now Google is very clearly putting Antigravity in the middle of its agentic future. And I mean very clearly. Sundar mentioned it. Demis mentioned it. Varun Mohan the co-founder was on stage immediately after them! If you've ever watched a Google I/O keynote, you know how carefully every minute is allocated. Google has YouTube, Search, Gmail, Android, Cloud, Ads, Workspace, and a thousand VP-level products that could be on stage. The fact that Antigravity was that prominent should tell you everything.Logan Kilpatrick joined us and framed this in a way I loved: Gemini became the through-line across Google products, and now the Antigravity agent harness is becoming the through-line for agentic experiences.The new Antigravity 2.0 is a complete overhaul, showing only an agentic interface (which was previously just a separate window called Agent Manager) and separating the IDE layer completely into its own app and showing a Codex like agent-first interface, which got a few folks furious. This move may be weird to some folks, but if you follow along where everyone's going, this seems to be the way of the future, coding is no longer about lines of code, it's about managing fleets of agents. The new Gemini 3.5 absolutely shines inside the new Antigravity, the model was trained with this harness in mind, and is currently offered at an incredible speed (12x), so I'm definitely going to try it! Gemini 3.5 Flash - fast, determined, and maybe not the old “Flash”The most debated model release of the week was Gemini 3.5 Flash.Some folks saw the pricing and token usage and immediately went “this is not Flash.” I get that reaction. Flash used to mean cheap, fast, lightweight chat model. But Logan's framing on the show was important: Flash is now being built for the agentic era.In a chat era, you optimize for one user message and one model answer. In an agentic era, the real token volume is in tool loops, intermediate reasoning, retries, file reads, web searches, code execution, and self-correction. That's a different product profile.Wolfram already ran Gemini 3.5 Flash through WolfBench, and the results were fascinating. With the Hermes agent harness, Gemini 3.5 Flash hit an 87% ceiling on Terminal Bench 2.0, meaning across runs it could solve more of the benchmark than even GPT-5.5 extra high in that setup. The variance was higher with the simpler Terminus harness, but with a real agent harness, the model looked much stronger.That tracks with what Nisten saw in his “Martian railgun from Olympus Mons” test. Gemini 3.5 Flash went extremely detailed, almost too determined, kept correcting itself, overcorrecting itself, and built a whole game-like simulation. Logan laughed and basically said: yeah, this model is very determined, possibly an overcorrection from the “Gemini is lazy” feedback. It also tracks with the mismatch in other benchmarks, in some, Gemini 3.5 flash shines (like the above Apex-agents from AA) and in some, it doesn't match the other frontiers. In my tests, it was definitely over-eager to use a million and a half tool calls, read tons of files, to just help me review this draft inside antigravity. It's like a super eager robotic golden retriever! Gemini Omni - Nano Banana for video, but actually more than thatThe biggest update from last year IO was Veo 3! This year, the biggest wow factor was also visual, but it wasn't VEO 4, it was a new model that is multimodal, trained end-to-end they call Omni. Google is calling this their first “create anything from anything” model, and the first version, Gemini Omni Flash, starts with conversational video editing. The easy description is: Nano Banana for video. You upload or create a video, then talk to it. Change this character. Replace this person. Add an object. Make this scene claymation. Keep the scene, but change the environment.I played with it live and showed a few examples. I asked for a claymation explainer of protein folding, then gave it my face and asked it to replace the character with me. It did it. I uploaded pictures of Sonia, my cat, and it generated a talking cat video with the right kind of cat teeth, which is weirdly important because so many pet generations accidentally add human teeth and become nightmare fuel.The failure modes are still there. I asked it to make Sonia a Russian-speaking female cat, and it only partly switched languages and didn't really change the voice. Audio upload support is also not fully productized yet, even though the underlying model is multimodal. But the direction is very clear.This is not just “Veo with a chat model glued on.” I asked Jeff Dean - Google's chief scientist about this at I/O, and he explained that Omni is trained end-to-end. The intelligence and the generative media capabilities are part of the same model family, not a hacky two-model pipeline. He also said the intelligence is around a recent Flash-level model, which is a big deal when you think about video editing as reasoning over physics, identity, scene continuity, and intent.A lot of people compared Omni to Seedance 2.0, and I think that's the wrong comparison. Seedance is amazing at cinematic generation (lkaregly due to lack of copyright concerns from Bytedance). Omni's unlock is iterative editing on real footage and coherent multi-turn creative control. Other Google IO 2026 releases I found notableThis was a concentrated effort of a huge company to insert AI into every product surface they have so of course I can't cover ALL of it here, but the most notable things for me were: * Gemini Spark - a new agentic experience from Google, to help you with tasks across Gmail, Drive and more. It should support skills, and is a de-facto OpenClaw/Hermes alternative from Google for regular folks. It's not “yet” live so we'll talk more about it when I can test it out* Managed Agents in the Gemini API - We chatted with Logan about this one, Google is re-imagining how agents are going to get built, and are offering 1 api call to spin up an agent in a full Linux env, with security and sandboxing in mind. I'll expand more on this in a next episode, as I recorded a complete conversation about this with Ali Çevic, a PM for Google APIs* AI overhaul of Google Search - AI Overviews will not expand into AI mode, and the iconic Google search box itself will change, for the first time in 25 years to include AI mode! * SynthID expantion and OpenAI collab - Google showed off that OpenAI is joining in marking all AI generate imagery and video with an invisible SynthID watermark. I think this is amazing and more companies should adopt this standard* AI Glasses! We got Google Glasses demos - Together with Warby Parker and Gentle Monster, Google finally showed off their answer to Meta Raybans/Oakleys. They look like regular glasses too, but can hear and talk to you, with the full power of Gemini multimodality. Available in the fall sometime! * Demis Hassabis “we're on the cusp of the singularity” closer - CEO and Co-Founder of DeepMind, Demis Hassabis, closed the show with his remarks about the positive future and that we are nearing this Singularity point after which the future is very uncertain. I found it to be very inspiring and closed our show with that clip as well! * Personally, I got to chat to: Demis Hassabis, have breakfast with Jeff Dean, ask Josh Woodward a bunch of questions, and pester about 20 other great folks on a live stream, and had a lot of fun! Huge thanks to the DeepMind folks, Lucie, Dimple, JD and many others for the continued belief in ThursdAI and invite me to cover this great event. OpenAI LLMs solve an 80yo math problem - Erdős Unit Distance ConjectureOutside of Google I/O, the biggest story of the week was OpenAI announcing that a general-purpose reasoning model made progress on the Erdős planar unit distance problem.This problem goes back to 1946. For nearly 80 years, mathematicians believed the best constructions looked roughly like square grids. OpenAI's model found a new family of constructions with a polynomial improvement, using algebraic number theory ideas that humans apparently had not explored in this context. The above is a representation of it! Important caveat: this does not fully solve every version of the asymptotic Erdős conjecture. Some mathematicians are pushing back on the framing, and fair enough. Precision matters. But even with the caveat, this is still a huge moment.The reason it matters is not that I personally understand the math. I absolutely do not. The reason it matters is that this was not a special-purpose IMO model fine-tuned only for math competitions. This was a general-purpose reasoning model exploring a real open problem, generating candidates, verifying them, and finding a path humans hadn't taken. Extrapolate this to other sciences, Physics for example? This means an amazing future. LDJ pointed out that mathematicians have been skeptical because there have been previous false alarms. But this one landed differently. When Fields Medalist-level mathematicians verify the proof, the discourse changes from “lol stochastic parrot” to “wait, what does this mean for my PhD?”My answer is: yes, still study math. Please study math. The mathematicians who use these tools will do much more than people who don't understand the domain. Same with software engineering. Senior engineers with Codex, Claude Code, Hermes, Antigravity, Cursor and other agents are becoming dramatically more effective because they can steer, evaluate, and recover the work.This being published a day after Demis's “foothills of the singularity” is a great conjecture. Cursor Composer 2.5 - Opus 4.7 performance model from Cursor, at 10x better efficiencyCursor dropped Composer 2.5, and folks, this is a serious release.Composer 2.5 is built on Moonshot's Kimi K2.5 base, like Composer 2, but Cursor scaled the post-training dramatically. They used 25x more synthetic tasks and introduced targeted textual feedback during RL rollouts, where the model gets hints inserted at the point of failure instead of only getting a noisy final reward.The benchmark story is strong: around 69.3 on Terminal Bench 2.0, basically neck and neck with Opus 4.7 in Cursor's chart, and strong results on SWE-bench multilingual and CursorBench. The pricing is the part that makes this especially interesting: $0.50 per million input tokens and $2.50 per million output tokens, with a faster variant at $3 / $15. That is much cheaper than the frontier models it is trying to replace for day-to-day coding work.Cursor engineers are reportedly dogfooding Composer 2.5 heavily and rarely switching away. That matters more to me than any single benchmark. If the people building Cursor can use it as a daily driver, that is a very real signal.The wild part is what comes next. Cursor is partnering with SpaceXAI to train a much larger model from scratch using 10x more compute on Colossus 2. Cursor has the workflow data. xAI has enormous compute. If this works, Cursor stops being just the IDE company and becomes a coding-model lab.We've been saying for months that coding agents are the path toward general agents. Anthropic has Claude Code. OpenAI has Codex. Google has Antigravity. xAI has Grok Build. Cursor has Composer. I'm looking forward to seeing how well it performs on our own benchmarks! Anthropic, xAI, Karpathy, and the compute warsThe compute story this week was bonkers.The SpaceX IPO filing reportedly revealed that Anthropic is paying SpaceXAI $1.25B per month for AI compute at the Memphis Colossus facility. Per month. That's about $15B a year, through May 2029, for access to more than 220,000 NVIDIA GPUs including H100s, H200s and GB200s.This is apparently inference compute for Claude Pro, Max and API users, not training. And it explains a lot of the recent quota changes. Anthropic doubled some Claude usage limits, and suddenly the product feels less constrained.Also, can we just acknowledge the comedy here? Elon Musk publicly called Anthropic “misanthropic,”, went off against every competitor to XAI, is now selling spare GPU time to Cursor and Anthropic? Who's next, OpenAI? The bigger point is that the AI capex story is no longer just NVIDIA. It's also whoever owns the data centers, power, cooling, networking, and GPU clusters. Compute is becoming the land under the AI economy.Also, Andrej Karpathy joined Anthropic. Karpathy could work anywhere. He co-founded OpenAI, led Tesla Autopilot vision, taught half the AI world how neural nets work, and now he's going back into frontier LLM R&D at Anthropic.Open source LLMs - Cohere, Qwen, NousOpen source had a strong week too.Cohere released Command A+, a 218B total parameter sparse MoE model with only 25B active parameters per token, under Apache 2.0. This is their first model that unifies reasoning, vision, multilingual, tool use and citations in one package.The hardware story is great: W4A4 quantization can run on 2 H100s or a single B200. Cohere says it supports 48 languages, 128K input context, 64K output, and gets big jumps over Command A Reasoning, including Tau-squared Bench Telecom from 37% to 85% and Terminal-Bench Hard from 3% to 25%.Cohere is one of those labs that doesn't always chase the loudest consumer hype, but they are very serious on enterprise and multilingual. Apache 2.0 makes this one especially useful.Alibaba also dropped Qwen 3.7-Max, positioned as an agentic frontier model. The headline from their testing is wild: 35 hours of continuous autonomous operation with more than 1,000 tool calls. They also showed it controlling a physical robot inside Alibaba offices and finding an umbrella after about 20 minutes of agent interaction.This digital-to-physical bridge is where things start feeling very real. An agent loop that can write code and use tools can also navigate physical tasks if you give it the right robotics stack.And our friends at Nous Research released Lighthouse Attention, a sparse attention method for long-context pretraining. At 512K context, they report a 17x faster forward+backward pass than standard attention on a single B200, and the recovered checkpoints actually beat dense-from-scratch final loss at the same token budget.The clever part is that the selection logic sits outside the attention kernel, so you still use regular FlashAttention on a gathered dense subsequence. No custom sparse kernel nonsense. If this holds up, this could matter a lot for long-context training.Tools and agentic engineering - X subscriptions, Grok Build, Codex MobileOne really practical tool update: Hermes and OpenClaw can now use your X subscription directly.This is more important than it sounds. You can connect your X Premium subscription and get access to semantic X search and Grok-related tooling without using sketchy browser automation or unofficial APIs that might get you banned. Wolfram already used this to have his agent go through his likes and bookmarks from the past week and send me news items for the show. That is exactly the kind of “small but real” agent workflow that becomes addictive.xAI also launched Grok Build, their agentic CLI coding tool, in early beta for SuperGrok Heavy subscribers. Early users are already running parallel Grok Build agents through tmux supervisors and using it for more than coding: fleet data triage, security patching, training label work, and general automation.The pricing being discussed is aggressive, around $1 per million input tokens and $2 per million output tokens for the API. The model version is grok-build-0.1, and folks have already wired it into Hermes with a 256K context window.And then there's Codex Mobile, which OpenAI shipped inside the ChatGPT mobile apps. This is one of those releases that sounds small until you start using it. You can control Codex sessions remotely from your phone, connected to your machine, and because Codex has native connectors to Gmail, Calendar and other surfaces, it sometimes feels faster and more reliable than local CLIs duct-taped to third-party integrations.I ported Wolfred into Codex with skills and everything, and I've been comparing the same tasks in Hermes and Codex. Codex is often faster, not necessarily because the model is always smarter, but because the connectors and harness are cleaner. Harness matters. We keep coming back to this.This Week's Buzz - W&B, CoreWeave, WolfBench and roboticsThis week in the Buzz, Wolfram walked us through a few things from the Weights & Biases / CoreWeave world.CoreWeave is a gold sponsor at ICRA 2026 in Vienna, the International Conference on Robotics and Automation. NVIDIA is also going big there with a keynote on generalist humanoid robots, 17 accepted papers and workshops around sim-to-real, robot foundation models, autonomous driving, manipulation, and physical AI.Wolfram will be there later in the week, after speaking at the AI Developer event in Cologne about WolfBench. If you're in Europe and into robotics or agent evals, find him.We also looked at WolfBench results for Gemini 3.5 Flash, which honestly became one of the more interesting empirical points of the episode. The model looks variable in simple harnesses, but very capable in better agent loops. That's the whole thesis of measuring model + harness together instead of pretending the model card tells the whole story.The water discourse, almonds, and data center realityWe also got into the data center water discourse, because this talking point is everywhere right now.There are real infrastructure questions around AI. Power, land, cooling, grid capacity, permitting, local impact, all of that matters. But the “AI is stealing drinking water” version of the argument is often wildly detached from scale.The stat I brought up on the show: California almonds use roughly 3 to 5.5 million acre-feet of water per year, multiple times more than all North American data centers combined in 2025. Nisten and LDJ added the important cooling nuance: many large data centers use closed-loop cooling, and evaporative cooling is not universal. Some data centers can avoid water use almost entirely, but at the cost of higher electricity usage.This doesn't mean “no concerns are valid.” It means if we're going to regulate or pause data centers, let's be honest about the actual tradeoffs. AI compute is becoming the substrate for medicine, robotics, science, logistics, software, education and every other productivity layer. We should build responsibly, but not based on viral fear math.Closing thoughts - foothills of the singularityDemis closed I/O saying we're in the foothills of the singularity, and I know how that lands when you write it down. But I was in the room, and after the keynote he told me something I haven't been able to shake: he thinks AI is going to be 10x as impactful as the Industrial Revolution, and 10x as fast. Basically 100x. This is the AlphaFold guy. Not someone loose with his words.Then look at the week. A general reasoner cracked an 80-year-old math problem. Cursor is training near-frontier coding models on a fraction of the big-lab budget. Anthropic is paying Elon $15B a year for inference. Karpathy left education to go back into pre-training. Google rolled out an intelligence uplift to a billion people who don't even know a model dropped.If you put that on a whiteboard in 2023, it reads like a sci-fi pitch.LDJ's mathematician friends are asking if they should keep doing their PhDs. My answer hasn't changed: yes, please keep going. The people who combine domain taste with these tools are going to ship more in 5 years than the previous generation did in 50. The tool doesn't replace the taste. It just removes the bottleneck.That's the whole reason ThursdAI exists. Not to hype every drop, not to dunk for engagement, but to give you a shot at being one of the people who knows what's happening, with the receipts.This week, a lot changed.See you next Thursday.TL;DR and Show Notes* Hosts and Guests* Alex Volkov - AI Evangelist at Weights & Biases / CoreWeave, @altryne* Co-hosts: @WolframRvnwlf, @nisten, @ldjconfirmed* Guest: Logan Kilpatrick, MTS at Google DeepMind / AI Studio, @OfficialLoganK* Google I/O 2026* Google went all-in on agents across Search, Gemini, Antigravity, Workspace, Android, Cloud and YouTube (I/O site, Alex thread)* Antigravity 2.0 became the central agentic coding harness across Google (Sundar, Google OS demo)* Gemini 3.5 Flash launched as a fast, determined workhorse model for agentic loops (Logan, Noam Shazeer, Jeff Dean)* Gemini 3.5 Flash is rolling out across the Gemini app, Search AI Mode, Gemini API, Google AI Studio, Antigravity and Gemini Enterprise Agent Platform (Koray Kavukcuoglu)* Google Search is getting new Gemini 3.5 Flash-powered agentic capabilities, including a new AI-powered Search box and background information agents (Sundar)* Gemini Spark was announced as a 24/7 personal AI agent that can proactively work across Google surfaces (News from Google)* Google teased Gemini-powered Android XR smart glasses with eyewear partners Gentle Monster and Warby Parker (Google, Alex live reaction)* Google AI Studio and the Gemini API got major agentic developer updates, including Managed Agents (Google AI Developers)* Vision & Video* Google DeepMind launched Gemini Omni, a “create anything from anything” multimodal model starting with conversational video editing (DeepMind, Google DeepMind on X)* Omni is available in the Gemini app, Google Flow and YouTube, with API support coming soon (Logan, Gemini App, Sundar)* Key distinction: Omni is not just text-to-video, it is an iterative multi-turn video editing model that combines Gemini intelligence, world knowledge, multimodal inputs and generative media (Google)* Big CO LLMs + APIs* OpenAI announced a general-purpose reasoning model made progress on the Erdős planar unit distance problem, challenging an 80-year-old mathematical belief (OpenAI, X)* Cursor launched Composer 2.5, built on Kimi K2.5, with Opus-class coding performance at much lower cost (Cursor blog, X)* Alibaba released Qwen 3.7-Max, an agentic frontier model with long autonomous runs and robotics demos (Qwen blog, X, robot demo)* Andrej Karpathy joined Anthropic to work on frontier LLM R&D (X)* SpaceX IPO filing revealed Anthropic is paying $1.25B/month for AI compute at the Memphis Colossus facility (Axios, Sawyer Merritt)* The jury in Musk v. Altman found Musk's OpenAI claims barred by statute of limitations, with Musk saying he will appeal (Elon Musk, Sawyer Merritt, Max Zeff)* Open Source LLMs* Cohere released Command A+, a 218B MoE model with 25B active parameters under Apache 2.0 (Cohere, Nick Frosst, HF W4A4, HF BF16)* Nous Research released Lighthouse Attention, a sparse attention method for long-context pretraining with major speedups (Blog, X, arXiv, GitHub)* Tools & Agentic Engineering* Google launched Managed Agents in the Gemini API, letting developers spin up hosted Antigravity agents with Linux sandboxes and persistent state (Docs, X)* xAI launched Grok Build, an agentic CLI coding tool in beta for SuperGrok Heavy users (xAI CLI, X)* Hermes and OpenClaw can now use X subscription auth for semantic search and Grok tooling (Alex)* OpenAI Codex Mobile is now available in the ChatGPT mobile apps for remote agent workflows (OpenAI)* Anthropic doubled Claude usage outside peak hours for a limited period, including Claude Code and other Claude surfaces (Claude)* This Week's Buzz - W&B / CoreWeave* Weights & Biases by CoreWeave is at ICRA 2026 in Vienna, with robotics and automation taking center stage (ICRA, W&B event page)* NVIDIA heads to ICRA 2026 with robotics work around generalist humanoids, physical AI and sim-to-real systems (NVIDIA Robotics, NVIDIA ICRA)* Wolfram is speaking about WolfBench at the AI Developer event in Cologne before heading to ICRA in Vienna (Wolfram)* Other Topics* Data center water usage discourse came up again, including why comparisons need real scale and context rather than viral fear math* The broader theme of the week: coding agents are becoming general agents, and the major labs are now competing on the full stack of model, harness, tools, context and compute This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit sub.thursdai.news/subscribe

In this episode, Jure Leskovec, co-founder and chief scientist at Kumo and professor of computer science at Stanford, joins us to explore two fronts of his work: AI for science and relational deep learning. We begin with AI Virtual Cell, a multiscale effort to learn data-driven representations from proteins to cells to patients using single-cell RNA-seq data, protein language models like ESM, and structure models like AlphaFold—without hand-encoding biology. Jure then dives into relational deep learning, reframing enterprise databases as graphs and training neural networks directly on raw multi-table data. He explains Kumo's Relational Foundation Model (RFM2), which performs in-context learning over subgraphs to make accurate predictions on new databases and tasks with no training, and how this approach benchmarks against RelBench and other multi-table datasets. We also discuss real-world deployments at companies like Reddit, DoorDash, and Coinbase, explainability via attention over tables and columns, integration with agentic systems, deployment options, and practical limitations. The complete show notes for this episode can be found at https://twimlai.com/go/768.

Michael I. Jordan, described by Science magazine as the most influential computer scientist alive, has never thought of himself as an AI researcher. In this conversation he explains why that distinction matters.SPONSOR:---Cyber Fund built the Monastery to help founders ship products that were impossible a year ago. Applications for Batch 1 are now open.Apply now: https://cyber.fund---Jordan trained as a statistician and cognitive scientist, and his career has been spent building machine learning systems that work in the real world: supply chains, commerce, healthcare, and large economic systems. When the field rebranded itself as AI and then AGI, he did not follow. Instead he argues that the framing is wrong. AI is better understood as a collective economic system than as a race to build a disembodied superintelligence.We talk about why AGI is mostly a PR term, what machine learning achieved before the LLM hype cycle, and why the assistant-on-your-shoulder vision may be less compelling than it sounds. Jordan explains why explanations need to be actionable, not merely mechanistic; why AlphaFold's missing error bars matter; how prediction-powered inference changes the picture; and why drug discovery is an incentive-design problem rather than a pure pattern-matching problem.ERRATA: Science magazine ranked him the most influential computer scientist, not Nature---TIMESTAMPS:00:00:00 Cold open: A demoralizing message to young builders00:02:04 CyberFund sponsor read00:02:50 From symbolic AI to machine learning systems00:05:42 Why AGI is mostly a PR term00:08:48 A collectivist, economic perspective on AI00:11:33 Why LLMs need system design, not hype00:14:50 Predictability beats faux understanding00:17:55 AlphaFold, bias, and prediction-powered inference00:21:48 Stop anthropomorphizing intelligence00:27:44 Drug discovery as an incentive problem00:32:29 The three-layer data market00:38:07 Social knowledge, markets, and culture00:45:39 Creator economics beyond Spotify00:48:30 How science-fiction AI narratives mislead young builders00:51:45 AI should improve humans, not replace them00:56:42 Safety is a property of the whole system00:58:12 Silicon Valley gurus and the cream off the top01:00:47 Game theory, mechanism design, and contracts01:04:39 Conformal prediction, e-values, and anytime inference01:08:11 A new liberal arts triangle for the AI era01:11:30 The Bayesian duck and markets as uncertainty reductionReScript (transcript, PDF, refs etc) - https://app.rescript.info/public/share/fb68f94af29d3745c6cf6125e01328b5---REFERENCES:person:[00:02:50] Michael I. Jordan (homepage)https://people.eecs.berkeley.edu/~jordan/paper:[00:06:01] A Collectivist, Economic Perspective on AIhttps://arxiv.org/abs/2507.06268[00:18:09] AlphaFoldhttps://www.nature.com/articles/s41586-021-03819-2[00:20:36] Prediction-Powered Inferencehttps://arxiv.org/abs/2301.09633[00:33:47] On Three-Layer Data Marketshttps://arxiv.org/abs/2402.09697[01:04:39] Conformal Prediction with Conditional Guaranteeshttps://arxiv.org/abs/2107.07511[01:04:51] A Tutorial on Conformal Predictionhttps://www.jmlr.org/papers/v9/shafer08a.html[01:06:00] E-Values Expand the Scope of Conformal Predictionhttps://arxiv.org/abs/2503.13050[01:08:23] Computational Thinkinghttps://www.cs.cmu.edu/~CompThink/papers/Wing06.pdfother:[00:28:20] How Should the FDA Test?https://rdi.berkeley.edu/events/sbc-assets/pdfs/Summit%20session%20speaker%20slides%20submission%20form-s1-5%20%28File%20responses%29/Slides%20in%20PDF%20%28Please%20name%20the%20submitted%20file%20as%20_firstname_-_lastname_-slides.pdf%29.%20%28File%20responses%29/27-Michael%20Jordan-Session%20V.pdf#page=15[00:28:40] Michael I. Jordan Session V Slides

Oubliez les LLM, Entalpic agence les atomes

Sir Demis Hassabis (geboren am 27. Juli 1976 in London) ist ein britischer KI-Forscher, Neurowissenschaftler und Unternehmer. Er ist Mitbegründer und CEO von Google DeepMind sowie Gründer von Isomorphic Labs. Im Jahr 2024 wurde ihm gemeinsam mit John Jumper der Nobelpreis für Chemie für die Entwicklung von AlphaFold verliehen, einem System zur Vorhersage von Proteinstrukturen.Hassabis gilt als vielseitiges Genie mit Hintergründen in den Bereichen Spiele, Informatik und Neurowissenschaften.Schach und Spiele: Er war ein Wunderkind im Schach, erreichte im Alter von 13 Jahren das Master-Niveau und führte mehrere englische Jugendteams an. Mit 17 Jahren war er federführend an der Programmierung und dem Design des millionenfach verkauften Simulationsspiels Theme Park beteiligt.Akademische Ausbildung: Er schloss sein Informatikstudium an der University of Cambridge 1997 mit Auszeichnung ab und promovierte 2009 in kognitiven Neurowissenschaften am University College London (UCL). Seine Forschung über den Zusammenhang zwischen Gedächtnis und Vorstellungskraft wurde vom Fachjournal Science als einer der zehn wichtigsten wissenschaftlichen Durchbrüche des Jahres 2007 gewürdigt.Hassabis gründete DeepMind im Jahr 2010 mit der Mission, „Intelligenz zu lösen“ und dieses Wissen zur Lösung globaler wissenschaftlicher Probleme einzusetzen.AlphaGo: Unter seiner Leitung besiegte das Programm AlphaGo 2016 den Weltmeister Lee Sedol im komplexen Spiel Go, was als Meilenstein der KI-Geschichte gilt.AlphaFold: Das Team um Hassabis und Jumper löste mit AlphaFold ein seit 50 Jahren bestehendes Problem der Biologie: die präzise Vorhersage der 3D-Struktur eines Proteins allein anhand seiner Aminosäuresequenz. Diese Technologie wurde der Fachwelt kostenlos in einer Datenbank mit über 200 Millionen Strukturen zur Verfügung gestellt.Wissenschaftliche Vision: Hassabis betrachtet die Biologie als ein Informationsverarbeitungssystem und sieht KI als ultimatives Werkzeug, um wissenschaftliche Entdeckungen in Bereichen wie der Arzneimittelforschung und Materialwissenschaft massiv zu beschleunigen.Hassabis ist ein prominenter Mahner für eine verantwortungsvolle KI-Entwicklung. Er plädiert für intensive Sicherheitsforschung und unterzeichnete eine Erklärung, die das Risiko eines KI-bedingten Aussterbens auf eine Stufe mit Pandemien oder einem Atomkrieg stellt. Gleichzeitig lehnt er eine Pause der KI-Entwicklung ab, da die potenziellen Vorteile für Gesundheit und Klima zu bedeutend seien.Neben dem Nobelpreis erhielt er zahlreiche weitere Ehrungen:Er wurde 2024 für seine Verdienste um die künstliche Intelligenz zum Ritter geschlagen.Er erhielt den Albert Lasker Award (2023) und den Breakthrough Prize in Life Sciences (2023).Das Time Magazine listete ihn mehrfach unter den 100 einflussreichsten Personen der Welt.Werdegang und frühe LeistungenDeepMind und AlphaFoldSicherheit und EthikAuszeichnungen

Sir Demis Hassabis (geboren am 27. Juli 1976 in London) ist ein britischer KI-Forscher, Neurowissenschaftler und Unternehmer. Er ist Mitbegründer und CEO von Google DeepMind sowie Gründer von Isomorphic Labs. Im Jahr 2024 wurde ihm gemeinsam mit John Jumper der Nobelpreis für Chemie für die Entwicklung von AlphaFold verliehen, einem System zur Vorhersage von Proteinstrukturen.Hassabis gilt als vielseitiges Genie mit Hintergründen in den Bereichen Spiele, Informatik und Neurowissenschaften.Schach und Spiele: Er war ein Wunderkind im Schach, erreichte im Alter von 13 Jahren das Master-Niveau und führte mehrere englische Jugendteams an. Mit 17 Jahren war er federführend an der Programmierung und dem Design des millionenfach verkauften Simulationsspiels Theme Park beteiligt.Akademische Ausbildung: Er schloss sein Informatikstudium an der University of Cambridge 1997 mit Auszeichnung ab und promovierte 2009 in kognitiven Neurowissenschaften am University College London (UCL). Seine Forschung über den Zusammenhang zwischen Gedächtnis und Vorstellungskraft wurde vom Fachjournal Science als einer der zehn wichtigsten wissenschaftlichen Durchbrüche des Jahres 2007 gewürdigt.Hassabis gründete DeepMind im Jahr 2010 mit der Mission, „Intelligenz zu lösen“ und dieses Wissen zur Lösung globaler wissenschaftlicher Probleme einzusetzen.AlphaGo: Unter seiner Leitung besiegte das Programm AlphaGo 2016 den Weltmeister Lee Sedol im komplexen Spiel Go, was als Meilenstein der KI-Geschichte gilt.AlphaFold: Das Team um Hassabis und Jumper löste mit AlphaFold ein seit 50 Jahren bestehendes Problem der Biologie: die präzise Vorhersage der 3D-Struktur eines Proteins allein anhand seiner Aminosäuresequenz. Diese Technologie wurde der Fachwelt kostenlos in einer Datenbank mit über 200 Millionen Strukturen zur Verfügung gestellt.Wissenschaftliche Vision: Hassabis betrachtet die Biologie als ein Informationsverarbeitungssystem und sieht KI als ultimatives Werkzeug, um wissenschaftliche Entdeckungen in Bereichen wie der Arzneimittelforschung und Materialwissenschaft massiv zu beschleunigen.Hassabis ist ein prominenter Mahner für eine verantwortungsvolle KI-Entwicklung. Er plädiert für intensive Sicherheitsforschung und unterzeichnete eine Erklärung, die das Risiko eines KI-bedingten Aussterbens auf eine Stufe mit Pandemien oder einem Atomkrieg stellt. Gleichzeitig lehnt er eine Pause der KI-Entwicklung ab, da die potenziellen Vorteile für Gesundheit und Klima zu bedeutend seien.Neben dem Nobelpreis erhielt er zahlreiche weitere Ehrungen:Er wurde 2024 für seine Verdienste um die künstliche Intelligenz zum Ritter geschlagen.Er erhielt den Albert Lasker Award (2023) und den Breakthrough Prize in Life Sciences (2023).Das Time Magazine listete ihn mehrfach unter den 100 einflussreichsten Personen der Welt.Werdegang und frühe LeistungenDeepMind und AlphaFoldSicherheit und EthikAuszeichnungen

En marzo de 2024, un riñón de cerdo empezó a funcionar dentro de un ser humano en un quirófano de Boston. En 2020, una inteligencia artificial resolvió en meses un problema que la biología llevaba 50 años sin poder resolver. Un pájaro de 20 gramos cruza Europa y África usando mecánica cuántica en sus ojos. Y una señal de 5 milisegundos viajó 8.000 millones de años para llegar a nuestros detectores. Cuatro fronteras que parecían imposibles. Cuatro historias que ya no lo son. Bienvenido a Ciencia Fascinante 1x02: En los límites de lo imposible. ▶ EN ESTE EPISODIO: El alfabeto de la vida — Durante 50 años, nadie supo cómo se doblan las proteínas. AlphaFold lo resolvió. Nobel 2024. 200 millones de estructuras ahora disponibles gratis. Esto cambia la medicina entera. El órgano prestado — Richard Slayman, 62 años, recibió un riñón de cerdo con 69 ediciones genéticas. Funcionó. La barrera entre especies, cruzada en quirófano. Susurros del cosmos — Las ráfagas de radio rápidas (FRBs): las explosiones más energéticas del universo. Duraron 5 milisegundos. Resolvieron el problema de la materia perdida del cosmos. La vida cuántica — El petirrojo navega por entrelazamiento cuántico. La fotosíntesis usa superposición cuántica. Tus enzimas hacen túnel cuántico. La naturaleza lleva 3.000 millones de años usando física que nosotros acabamos de descubrir. Síguenos en Redes Twitter: https://twitter.com/radioelrespeto Instagram: https://www.instagram.com/radioelrespeto/ Facebook: https://www.facebook.com/radioelrespeto Redes Sociales del Equipo: | Pablo Fuente | https://www.instagram.com/pablofuente/ | Nacho Sevilla | https://twitter.com/nachorsevilla | Fernando Sierra | https://twitter.com/Peeweeyo1

Can AI move from predicting proteins to actually designing new drugs? Isomorphic Labs is trying to answer one of the biggest questions in science.In this episode of The Neuron, Corey Noles and Grant Harvey talk with Rebecca Paul, Head of Medicinal Drug Design at Isomorphic Labs, and Michael Schaarschmidt, Foundational AI Research Lead.They explain why drug discovery is so slow, expensive, and failure-prone—and why AI drug design is much more complicated than “generate a molecule and ship it.” The conversation covers AlphaFold, structure prediction, molecule generation, binding models, clinical failure rates, human trust in AI systems, and the long-term hope of designing drugs for targets once considered “undruggable.”In this episode:Why drug discovery can take more than a decadeWhat people misunderstand about “AI-designed drugs”How medicinal chemists actually use AI modelsWhy biology is harder than text, images, or codeWhat it would take to make drug discovery faster and cheaperThe dream of designing a drug candidate in one iterationWhy “undruggable” proteins may not stay undruggable foreverAdditional resources:Technical report blog Best resource for learning about the capabilities that we are buildingIsomorphic Labs websiteBest destination for learning more about Iso and joining our team in London, Lausanne or Cambridge, MASubscribe for more grounded conversations on how AI is changing science, work, and the world.For more practical, grounded conversations on AI systems that actually work, subscribe to The Neuron newsletter at https://theneuron.ai.

Join Paul Steven Conyngham, Co-founder of Core Intelligence Technologies and a veteran data scientist with 17 years of experience, for a conversation that redefined the boundaries of "citizen science." In 2026, Paul stunned the global medical and tech communities by doing the unthinkable: designing a personalized mRNA cancer vaccine for his rescue dog, Rosie, after a terminal diagnosis. In this episode, we discuss how Paul applied the rigors of machine learning and data strategy to the complex world of genomics, utilizing AI to turn a death sentence into a landmark recovery.

Demis Hassabis, co-founder and CEO of Google DeepMind and 2024 Nobel laureate in chemistry for AlphaFold, joins Sequoia partner Konstantine Buhler at AI Ascent 2026 for a wide-ranging conversation about the path to AGI and what comes after. He explains why he believes AGI is achievable by 2030, why drug discovery could collapse from ten years to days, and why we should think of information, not matter or energy, as the most fundamental substance in the universe. Also: what Einstein would tell us about the limits of today's models, and why the next year or two will be critical for humanity.

Michael Jewett is a pioneer of cell-free biotechnology. Instead of using living microbes as factories, he uses their internal molecular machinery to make valuable proteins, medicines, diagnostics, and other chemicals. Jewett recently used the technique for vaccine production in an approach that could produce up to 150,000 doses from one liter. He believes cell-free biotech could democratize the production of essential medicines, improve water safety, and help convert atmospheric carbon into useful products, among other promising possibilities. “It's just-add-water biotechnology,” Jewett tells host Russ Altman on this episode of Stanford Engineering's The Future of Everything podcast. Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to thefutureofeverything@stanford.edu. Episode Reference Links: Stanford Profile: Michael Christopher Jewett Connect With Us: Episode Transcripts >>> The Future of Everything Website Connect with Russ >>> Threads / Bluesky / Mastodon Connect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / Facebook Chapters: (00:00:00) Introduction Russ Altman introduces Mike Jewett, a professor of bioengineering and chemical engineering at Stanford University. (00:03:23) What Is Cell-Free Biotechnology? Using the internal machinery of cells without the cells themselves. (00:04:20) Removing “Evolutionary Baggage” Why cells' natural priorities can conflict with engineering goals. (00:07:41) Advantages of Cell-Free Systems From large-scale production to decentralized, on-demand manufacturing. (00:11:40) Making Proteins Outside Cells How DNA instructions are used to produce functional proteins. (00:13:49) Biosensors for Water Safety Detecting contaminants like lead using engineered proteins. (00:17:05) Engineering Better Sensors Improving sensitivity and selectivity through protein design. (00:20:33) AI in Bioengineering How data and models accelerate discovery and design. (00:23:22) Sustainability & Carbon Capture Turning atmospheric carbon into useful chemicals. (00:26:18) Building New Biological Pathways Combining chemistry and biology to create novel production systems. (00:27:54) From Molecules to Materials How acetyl-CoA enables fuels, plastics, and other products. (00:30:51) Teaching Biotechnology Making biotech accessible through hands-on, “just-add-water” kits. (00:33:12) Future In a Minute Rapid-fire Q&A: innovation, collaboration, and the future of biotech. (00:35:32) Conclusion Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

This is one of my favorite books over recent years. Sebastian Mallaby is the Paul A. Cocker Senior Fellow for International Economics at the Council of Foreign Relations and author of 6 bestselling books. THE INFINITY MACHINE tells the story of AI's progress over the past 15 years largely, but not exclusively, from Demis Hassabis as the protagonist and leader of DeepMind', with its 2010 mission statement to achieve superintelligence by 2030. It's a rich, informative, page turner.What We Discussed:—What is an Infinity Machine?—Influence of Claude Shannon's Information Theory and Douglas Hofstadter's Pulitzer Prize winning book Gödel, Escher, Bach—Origin of DeepMind in 2010. Prescient. Charter, business plan, included use of agents. How Demis Hassabis was made for the mission!—Contrasts with Sam Altman and the other AI leaders, the Oligopoly (cover of The Economist this week). For example, Nature papers vs white papers on company websites. —In March 2016, the same day when DeepMind's AlphaGo beat Lee Sedol, Hassabis says it's time to do protein folding (later known as AlphaFold).—Symbolic AI (historic, deductive, rule-based) vs Deep Learning (Toronto tribe) and Reinforcement Learning (Alberta tribe).—The Big Miss: DeepMind's lack of early recognition of the importance of transformer models (leading to ChatGPT), creating a big opening for OpenAI. And why was this missed? The Comeback Story. Is this happening again with coding (not in the book)?—The AI Arms Race and Hyperscaling—How the complex relationship between Google and DeepMind evolved —The Double Cross —With the dangers anticipated (parallels to Oppenheimer, Manhattan Project, and the atomic bomb), how to promote AI safety?—Is the major build up of data centers justified?Thank you Bob Fleischman, Jeanie, Ruben Max, FelonBroke America, Seitzinator ❌

-Breve: Tomas falsas del doblaje de la entrevista AlphaFold (05:00)-Artemisa II sigue su curso (15:00) Hosted on Acast. See acast.com/privacy for more information.

Artificial intelligence is everywhere — but what does it mean for us as humans, as embodied creatures, and as people of faith? In this episode of The UpWords Podcast, host Dan Johnson sits down with Noreen Herzfeld, a computer scientist turned theologian who has been thinking seriously about AI and humanity since the 1980s. Together they explore why we are driven to create AI in our own image, what Christian theology says about embodiment and relationship, and why the church should be cautious about AI.WHAT YOU WILL LEARNWhy humans are compelled to create AI in their own image — and what that reveals about usHow the Imago Dei (image of God) shifts from intellect to relationship in 20th-century theology — and why it matters for AIWhat Christianity's strong theology of embodiment means in a world increasingly dominated by language and the cloudWhy AI chatbot "relationships" are fundamentally different from — and inferior to — human relationshipsWhere AI has real, appropriate uses (narrow, domain-specific tools like AlphaFold) and where it falls dangerously shortWhy Noreen sees limited good use for AI in ministry — and significant risks in pastoral care and counseling settingsHow large language models differ fundamentally from earlier AI — and why they hallucinateThe collision course between AI energy consumption and climate changeWhy Noreen would advise most people: don't use it at allGUEST BIONoreen Herzfeld is one of the rare scholars who holds advanced degrees in both computer science and Christian theology. She earned her M.S. and M.A. from Penn State, took a sabbatical to study why humans want to build AI in our image, and ended up earning a Ph.D. in Theology from the Graduate Theological Union at Berkeley. She has been teaching and writing at the intersection of technology and faith for over two decades. Her books include In Our Image: Artificial Intelligence and the Human Spirit (Fortress, 2002), Technology and Religion: Remaining Human in a Co-Created World (Templeton, 2009), and The Artifice of Intelligence: Divine and Human Relationship in a Robotic World (Fortress, 2023). She also directs the Benedictine Spirituality and Ecotheology Program at St. John's School of Theology and Seminary and is a Senior Research Associate at the Institute for Philosophical and Religious Studies in Koper, Slovenia.RESOURCES & LINKSNoreen Herzfeld's faculty page: csbsju.edu/sot/person/noreen-herzfeld/In Our Image: Artificial Intelligence and the Human Spirit — (Fortress Press, 2002)Technology and Religion: Remaining Human in a Co-Created World — (Templeton, 2009)The Artifice of Intelligence: Divine and Human Relationship in a Robotic World — (Fortress, 2023)AlphaFold (DeepMind protein folding AI) — deepmind.google/technologies/alphafoldSherry Turkle, MIT sociologist — referenced in discussion of chatbot relationshipsSend us Fan MailCONNECT WITH USSubscribe to The UpWords Podcast wherever you listen to podcasts and visit slbf.org/studio to learn more about our work at the intersection of faith, the academy, and the marketplace.This episode was created by the SLBF STUDIO at Upper House.Produced by Daniel Johnson and Dave ConourEdited by Dave Conour

Demis Hassabis is the Co-Founder & CEO of Google DeepMind - working on AGI, responsible for AI breakthroughs such as AlphaGo, the first program to beat the world champion at the game of Go; and AlphaFold, which cracked the 50-year grand challenge of protein structure prediction and was recognised with the 2024 Nobel Prize in Chemistry. Demis is revolutionising drug discovery at Isomorphic Labs. Ultimately, trying to understand the fundamental nature of reality. AGENDA: 00:04:00 — What Actually Counts as AGI; and Where Are We Today? 00:05:00 — What Are the Biggest Bottlenecks Holding AI Back Today? 00:06:00 — Have We Hit the Limits of Scaling Laws? 00:07:00 — Where Is AI Ahead of Expectations; and What's Still Missing? 00:07:30 — Why Can't AI Systems Learn Continuously Like Humans? 00:08:30 — How Did DeepMind Go from Behind to Leading the Pack? 00:11:00 — Are We Heading Toward Model Commoditization; or Winner-Takes-All? 00:12:00 — What Does the Future of Open Source Really Look Like? 00:13:00 — What Does a Post LLM World Look Like? 00:14:45 — Can AI Really Fix Drug Discovery—and Cut the 10-Year Timeline? 00:17:00 — What Does "Good" AI Regulation Actually Look Like? 00:18:00 — Who Should Be the Ultimate Arbiter of Truth in an AI World? 00:19:30 — If Demis Had One Shot to Fix AI Safety, What Would He Do? 00:21:00 — Is This Time Different for Jobs; or Will History Repeat Itself? 00:22:00 — Is AGI Bigger Than the Industrial Revolution; and Faster? 00:23:00 — Are We Underestimating AI Despite All the Hype? 00:23:30 — Does AI Lead to Massive Inequality; or Universal Prosperity? 00:24:30 — How Do We Solve the Energy Crisis Created by AI? 00:26:00 — Why Stay in the UK Instead of Moving to Silicon Valley? 00:28:00 — Will Europe Ever Build a Trillion-Dollar Tech Giant? 00:29:30 — Meeting Elon Musk for the First Time? 00:31:00 — What Big Questions About AI Is No One Talking About? 00:31:30 — What Does Demis Want His Legacy to Be?    

Een nieuw #nerdland maandoverzicht! Met deze maand: Bizarre hybride wezens! Insectenzenuwstelsels! De SLS-Raket! Meta! Tennisrobots! Geavanceerde chipproductiemachines! Hinniktonen! PISSSTREAM! En veel meer... Gepresenteerd door Lieven Scheire met Hetty Helsmoortel, Bart Van Peer, Marian Verhelst, Kurt Beheydt en Peter Berx. Opname, montage en mastering door Jens Paeyeneers. Check ook https://podcast.nerdland.be/nerdland-maandoverzicht-april-2026/ (00:04:07) Herhaald klonen van hetzelfde dier levert nu een reeks van 57 generaties klonen op, wat vragen oproept over effecten op gezondheid en erfelijkheid (00:08:34) Het brein van een vlieg is tot in detail nagebouwd in een model om insectenzenuwstelsels te begrijpen (00:18:05) Muizenhersenen blijken een “cryosleep” te kunnen overleven en nadien weer activiteit te vertonen (00:19:18) De Ig Nobelprijzen verhuizen naar Europa uit vrees voor problemen met Amerikaanse reisvisa (00:21:25) NASA moest een ruimtestation tijdelijk evacueren wegens een medisch probleem bij astronaut Mike Fincke (00:25:50) De VS verschuiven hun ruimtebeleid om minder op ruimtestations en meer op een volwaardige maanbasis te focussen (00:32:03) De SLS-raket staat opnieuw op het lanceerplatform klaar voor een volgende test of missie (00:36:26) Menselijke hersencellen op een chip leerden in een week het computerspel Doom spelen (00:45:51) IMEC in Leuven installeert de meest geavanceerde chipproductiemachine ter wereld (00:55:51) Een hondenbaasje gebruikte ChatGPT en AlphaFold om zelf een experimentele behandeling voor de kanker van zijn hond te ontwerpen (01:05:10) Silicon Valley Nieuws (01:05:24) Een incident rond militair gebruik van Anthropic's AI Claude voor nucleaire dreigingsanalyse zorgt voor grote commotie (01:09:58) Iran voert wraakacties uit door datacenters aan te vallen met bombardementen (01:10:52) Meta's slimme bril zou externe medewerkers toegang geven tot gebruikersbeelden, wat grote privacyzorgen oproept (01:15:29) Rechtszaak tegen Meta omdat Facebook opzettelijk verslavend gemaakt is (01:23:33) Chimpansees vinden glimmende kristallen fascinerend, maar de reden voor die voorkeur is nog onduidelijk (01:28:21) Een bizar hybride wezen combineert kenmerken van kikker, mug en bij in één ontwerp (01:34:45) Paarden produceren twee tonen tegelijk bij het hinniken, één met de stembanden en één via een fluitachtig structuurtje in het strottenhoofd (01:38:45) Een Chinese tennisrobot, Latent, toont opvallend vloeiende en mensachtige tennisvaardigheden (01:45:45) Slimme laptopstand “Slapmac” koppelt schermhoek aan sensoren om je houding en gebruik te optimaliseren (01:51:42) Trace deminges: PISSSTREAM die scherm vult (01:53:01) Mieren zetten uitgestoten CO2 om in harde, beschermende “pantser”-structuren in hun lichaam (01:57:43) Pokémon Go-technologie wordt gebruikt om bezorgrobots een extreem nauwkeurige kaart van de omgeving te geven (02:02:48) Aankondigingen (02:02:58) In memoriam: Julie Leurs (02:03:49) Nerdland Festival: vrijwilligers@nerdlandfestival.be (02:08:05) Bijenhotel Puyenbroeck (02:14:52) Op 23 april organiseert de KU Leuven een Dag van de Insecten, volledig gewijd aan insectenonderzoek (02:15:38) Nerdland voor Kleine Nerds op Ketnet (02:16:30) Meteoor gezien (02:20:41) Rechtzetting VAIA (02:21:09) Sponsor MAP: Mercator Museum Sint-Niklaas. Correctie: het museum is wekelijks open van dinsdag tot en met zondag!

After 22 years at IBM, where he rose to senior vice president and director of IBM Research, Dr. Dario Gil now leads one of the most ambitious science and technology initiatives in a generation. As the Department of Energy's (DOE's) Under Secretary for Science and director of the Genesis Mission, Gil is orchestrating a convergence of high-performance computing, artificial intelligence (AI), and quantum computing aimed at transforming how America does science and engineering. The Genesis Mission rests on a straightforward premise: a computing revolution is underway, and the U.S. should harness it to double the productivity of its trillion-dollar-a-year research and development engine within a decade. The initiative is built on three pillars: a platform for accelerating discovery anchored in high-performance computing, AI supercomputing, and quantum computing; a portfolio of national challenges in energy, physical sciences, and national security; and a university engagement effort to rethink how future scientists and engineers are educated in the age of AI. Gil offered fusion energy as a prime example of how AI can compress timelines. By training neural networks on validated simulation data, researchers can build surrogate models that run thousands to tens of thousands of times faster, allowing engineers to iterate on reactor designs in hours rather than months. AI is also being applied to real-time plasma control through collaborative work involving Google DeepMind and Commonwealth Fusion Systems. On the grid, Gil shared two striking examples. The DOE's Office of Electricity is developing AI agents to help developers fix deficient interconnection applications—which account for 80% to 90% of submissions—potentially accelerating studies by up to a year. Meanwhile, Brookhaven National Laboratory's Grid FM emulator can speed power flow calculations by 100x, compressing what would be 20 years of conventional analysis of the Texas transmission grid into roughly two months. Gil was candid about the tension between AI as an energy solution and AI as a source of surging electricity demand, noting that planned data centers now reach gigawatt scale. The path forward, he said, involves optimizing the existing grid, accelerating nuclear energy, investing in fusion, and driving major efficiency gains in AI hardware. New supercomputing infrastructure is already being built through the Genesis Consortium, a partnership of 27 industrial players. Argonne and Oak Ridge National Laboratories are each standing up large GPU clusters this year, with a 100,000-GPU system planned for Argonne in 2027—the largest science-oriented cluster in the world. Asked what success looks like, Gil pointed to the AlphaFold story: 50 years of work produced 200,000 protein structures, then AI predicted 200 million in two years. Success, he said, will mean 50 to 100 comparable breakthroughs across all domains of science within three to five years.

Hvorfor ender europeiske super-ideer opp i hendene på amerikanske tech-milliardærer? Og hva skjer når kommersielle selskaper får tilgang til tankene våre?I ukens episode av Shifters podcast møter vi Ishita Barua – lege, AI-forsker, gründer og nå aktuell med boken «Tech Bros – og hvordan Europa kan lære å elske innovasjon».Med utgangspunkt i den alarmerende Draghi-rapporten tar hun et oppgjør med Europas risikovegring og manglende evne til å kommersialisere banebrytende teknologi.Barua forteller historien om hvordan skaperne bak verdens viktigste AI-modell for biologi (AlphaFold) måtte trygle om penger i USA, og advarer om geopolitiske konsekvenser når vi lar de amerikanske «tech-oligarkene» styre showet.I denne episoden diskuterer vi blant annet:Kulturkræsjen: Den enorme forskjellen på å lufte en startup-idé i akademia i Norge kontra på Harvard.DeepMind-eksodusen: Hvorfor Europas største AI-gjennombrudd måtte reddes av Elon Musk og Google.Brain-Computer Interfaces (BCI): Neuralink, hjernehacking og teknologien som lar hjernen snakke direkte med datamaskiner. Hva skjer når teknologien skal selges til funksjonsfriske på apoteket?Løsningen for Europa: Hvordan vi kan bygge økosystemer og skaffe risikokapitalen som trengs for å holde de kloke hodene hjemme.Programleder er Shifter-journalist Joakim Birkeli Jacobsen.

Investigación de Biohacking (Links de YouTube): Nate Gentile: Cómo vivir para siempre Dot CSV: Neuralink y el futuro de la IA DW Documental: ¿Vida eterna con biohacking? Neuralink Progress: Pacientes reales 3. Datos y Ética: Bryan Johnson: Proyecto Blueprint (2M$/año). Quiebra Ética: Second Sight (pacientes abandonados con ojos biónicos sin soporte). Mito Disney: Walt fue incinerado. El real es James Bedford en Alcor. AlphaFold 4 (DeepMind): Ya no solo predice cómo se doblan las proteínas, sino que ahora diseña proteínas sintéticas desde cero para bloquear el envejecimiento celular. Es como tener un arquitecto molecular trabajando 24/7. Gemelos Digitales (Digital Twins): Empresas como Q Bio están usando IA para crear un modelo 3D exacto de tus órganos. Pueden simular cómo te afectará un fármaco o una dieta específica antes de que te la tomes. ¡Cero riesgos de ensayo y error! BCI (Interfaces Cerebro-Computadora): No solo Neuralink. Synchron está logrando que personas con ELA controlen dispositivos domésticos con el pensamiento de forma comercial, integrando IA para "predecir" lo que el usuario quiere escribir o hacer. Empresas que lideran la carga Altos Labs: Sigue siendo el titán. Con el respaldo de Bezos, están usando IA para identificar los "factores de Yamanaka" exactos que pueden rejuvenecer tejidos sin convertirlos en tumores. Insilico Medicine: Han logrado llevar el primer fármaco diseñado íntegramente por IA a fase 3 de ensayos clínicos. Han reducido el tiempo de desarrollo de 10 años a menos de 3. Humanity Inc: Una app que monitoriza tus biomarcadores en tiempo real y usa algoritmos para decirte exactamente cuántos "días de vida" estás ganando o perdiendo según tu sueño, estrés y comida. Shinya Yamanaka: Premio Nobel por descubrir los "Factores de Yamanaka". Son 4 genes que, si se activan, pueden convertir cualquier célula adulta en una célula madre. Él es asesor científico senior (sin sueldo, por ética). Juan Carlos Izpisúa Belmonte: El genio español. Es famoso por sus experimentos de integración de células humanas en embriones de otras especies. En Altos, lidera la aplicación práctica de la reprogramación en animales vivos. Rejuvenecimiento de Tejidos Específicos: Ya no intentan rejuvenecer a un ratón entero (porque eso suele causar tumores, los llamados teratomas). Ahora se centran en órganos diana: hígados envejecidos que recuperan funciones de órganos jóvenes y retinas que vuelven a ver. 1. China: "El Dragón Genético" (BGI Group) Si Altos Labs es la "Estrella de la Muerte", BGI Group (en Shenzhen) es la fábrica de clones. El Enfoque: Son el centro de secuenciación genética más grande del mundo. Mientras en EE. UU. debaten sobre la ética de editar embriones, en China están usando IA y CRISPR de forma masiva para identificar "super-genes" de centenarios chinos. ​ 2. Arabia Saudita: "Hevolution Foundation" (El Oro por Vida) ​Es la fundación del Príncipe Heredero Mohammed bin Salman. ​El Presupuesto: Tienen asignados 1.000 millones de dólares AL AÑO a perpetuidad. Es el fondo soberano más grande dedicado exclusivamente a la longevidad. (El Gigante Silencioso) 3. Calico Life Sciences (Google/Alphabet) No podemos olvidarnos de los primeros que empezaron esto en 2013. El Perfil: Es la empresa más secreta de todas. Mientras Bezos hace ruido, Google (Alphabet) lleva años estudiando a la rata topo desnuda (un animal que no tiene cáncer y no envejece como nosotros) mediante IA masiva. Noticia 2026: Se rumorea que han encontrado una ruta metabólica para "congelar" el deterioro celular en tejidos nerviosos, pero como son Google, no soltarán prenda hasta que tengan la patente cerrada. 4. Alemania: "Rejuvenate Bio" (La Vía Veterinaria) Aunque operan en varios sitios, tienen un fuerte ADN europeo de rigor científico. El Biohack: Su enfoque es brillante: terapia génica para perros. Han logrado curar la insuficiencia cardíaca en perros ancianos mediante una sola inyección.

【欢迎订阅】 每天早上5:30，准时更新。 【阅读原文】 标题：Man's dog was riddled with tumors and dying. He used ChatGPT to design a custom cancer vaccine, stunning researchers正文：When veterinarians told tech entrepreneur Paul Conyngham that his rescue dog Rosie had months to live, he didn't accept the prognosis. Instead, the data scientist turned to ChatGPT to build a personalized cancer vaccine. Rosie had been diagnosed with advanced mast cell cancer in 2024.Chemotherapy couldn't shrink her tumors, so Conyngham spent $3,000 to have Rosie's DNA sequenced at the University of New South Wales (UNSW). He used AI tools, including AlphaFold, to pinpoint mutations driving her cancer and identify drug targets.知识点：veterinarians n. /ˌvet.ər.ɪˈner.i.ənz/ doctors who treat animals 兽医 • The veterinarians worked through the night to save the injured horse. 兽医们彻夜工作以挽救那匹受伤的马。 • She has wanted to become a veterinarian since she was a child. 她从小就梦想成为一名兽医。获取外刊的完整原文以及精讲笔记，请关注微信公众号「早安英文」，回复“外刊”即可。更多有意思的英语干货等着你！ 【节目介绍】 《早安英文-每日外刊精读》，带你精读最新外刊，了解国际最热事件：分析语法结构，拆解长难句，最接地气的翻译，还有重点词汇讲解。 所有选题均来自于《经济学人》《纽约时报》《华尔街日报》《华盛顿邮报》《大西洋月刊》《科学杂志》《国家地理》等国际一线外刊。 【适合谁听】 1、关注时事热点新闻，想要学习最新最潮流英文表达的英文学习者 2、任何想通过地道英文提高听、说、读、写能力的英文学习者 3、想快速掌握表达，有出国学习和旅游计划的英语爱好者 4、参加各类英语考试的应试者（如大学英语四六级、托福雅思、考研等） 【你将获得】 1、超过1000篇外刊精读课程，拓展丰富语言表达和文化背景 2、逐词、逐句精确讲解，系统掌握英语词汇、听力、阅读和语法 3、每期内附学习笔记，包含全文注释、长难句解析、疑难语法点等，帮助扫除阅读障碍。

Materials science is the unsung hero of the science world. Behind every physical product you interact was decades of research into getting the properties of materials just right. Your gym clothes contain synthetic fibers developed over decades. The glass screen, diodes, and chip substrate technology needed to read this blog post were only viable due to many teams of material scientists.Our guest Prof. Heather Kulik was one of the first material scientists to realize that there was alpha in combining computational tools with data driven modeling — she did AI for science before it was cool. She has a hard-fought perspective for how to succeed in this field. Yes, she believes the wins are real. To get there you must work hard to deeply integrate domain expertise with AI techniques, and also maintain a discriminating mind. Ultimately what matters is you succeed in the lab, and nature doesn't care about how hyped a model is. These lessons personally resonated with the Latent.Space Science team and our own experience.This episode is a must watch for all aspiring AI for science practitioners. A few highlights:Designing new polymers with AI: Heather's group recently used AI to design new polymers that are significantly stronger. These materials were created and tested in the lab, and the scientists who built them were surprised by the designs. The AI had figured out certain building blocks could break in a novel way. The AI discovered a purely quantum mechanical effect, and after convincing their lab collaborators to actually synthesize it, the material turned out to be four times tougher!The twenty-two-atom ligand challenge: When asked about the role and need of human scientists, Heather points out that AI has a strong understanding of academic chemistry, but is still lacking intuition. Every time an LLM is updated, Heather asks it to design a ligand that contains exactly twenty-two heavy atoms. She has yet to find one that can succeed at this seemingly simple task that any expert could do in a second! Is this the chemistry counterpart to counting ‘r's in strawberry?Side note: Heather joked that this comment would date itself immediately, so we decided to see if this was still true three months after recording. We found some interesting results! We asked both Claude and ChatGPT to design a 22 atom ligand for both a metal-organic framework (MOF) and a Kinase protein. * For the Kinase, both models got it right: Claude pulled out RDKit in a python script and iterated on several designs, whereas ChatGPT just one-shotted it. * For MOFs, both models got it wrong, generating ligands with 21, 23, or 24 atoms, yet stubbornly not getting 22 atoms. Is there something different about how LLMs reason in the materials and bio domains?Materials vs biology: The two biggest domains of AI in science have been biology and materials. We asked Heather if there could be an AlphaFold moment for materials. Her answer reframes how we should think about the field:* First, the datasets in material science are woefully lacking in comparison to the bio world. The closest to ground truth in most cases are noisy DFT datasets. These are just approximations to the real world! The datasets that are accurate are all boring, as Heather quipped “We have really good datasets for really boring chemistry.” Furthermore, good experimental structures are hard to come by and require interpretation. So generating generating high-quality, novel datasets at scale would really drive the field forward.* More philosophically, AlphaFold is making predictions in a fairly limited space: there are just twenty amino acids. Sure, even here AlphaFold doesn't get everything right, but it seems plausible that one could learn the entire design space. For materials, each element is a new set of interactions and chemistry, with little to no transferability. This is a massive open problem in material science that we hope some of the smartest AI scientists will want to work on!The difficulties of trusting the literature: Heather's team has spent the last few years using NLP and later LLMs to extract data from literature. Even a few thousand data points from these papers can be valuable for guiding her group's work. One surprising result: sometimes the reported values for a property (say temperature) do not match up with the graphs in the papers! So there's lots of potential in using LLMs to mine data from the literature, just do it with care.The role of academia in an ever-changing world: One theme that has been running through many of our conversations has been the changing role of the academic — and the scientist — in science. When startups are raising $100s of millions and hyperscalers and Big Pharma are all ramping up AI-for-science efforts, the academic researcher needs both resources and judgement about problems to chase more than ever.Resources include data that is organized for machine learning, access to high throughput experimentation labs, and compute resources. These are all things that academics can build together. More importantly, Heather emphasizes curiosity about problems that haven't hit the radar of the heavily capitalized AI companies. After so many years on the forefront of AI for Science, Heather's judgement that Chemical Engineering and Material Science still need curious people asking questions with no clear path to money is a welcome beacon in the AI fog.Full Video podcast Is on Youtube! This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

La tertulia semanal en la que repasamos las últimas noticias de la actualidad científica. En el episodio de hoy: Cara A: -Acast, nuevo partner de CB:SyR (5:00) -Evento cientófilo para ver el eclipse del 12 de Agosto (6:00) -Entrevista 10 años de DeepMind: Pushmeet Kohli y Thore Graepel (13:00) Este episodio continúa en la Cara B. Contertulios: María Ribes, Alberto Aparici, Juan Carlos Gil, Ignacio Crespo, Francis Villatoro, Héctor Socas. Imagen de portada realizada con Midjourney. Todos los comentarios vertidos durante la tertulia representan únicamente la opinión de quien los hace... y a veces ni eso

La tertulia semanal en la que repasamos las últimas noticias de la actualidad científica. En el episodio de hoy: Cara B: -Compuestos volátiles revelan la composición de los materiales para embalsamamiento en el antiguo Egipto (47:45) -Teorías de la consciencia (1:18:45) -Señales de los oyentes (1:42:15) Este episodio es continuación de la Cara A. Contertulios: María Ribes, Luisa Achaerandio, Alberto Aparici, Juan Carlos Gil, Ignacio Crespo, Francis Villatoro, Héctor Socas. Imagen de portada realizada por Mayra Schwarzschild. Todos los comentarios vertidos durante la tertulia representan únicamente la opinión de quien los hace... y a veces ni eso

In this episode of Partnering Leadership, Mahan Tavakoli sits down with Steve Brown, a leading AI futurist and former executive at organizations including Intel and DeepMind. Brown brings a rare combination of technical depth and leadership perspective, shaped by decades at the forefront of technological change and his work advising leaders around the world on the implications of artificial intelligence.The conversation centers on Brown's book, The AI Ultimatum, and the core argument behind it: AI is not simply another productivity tool or IT upgrade. It represents a fundamental shift in how intelligence is created, scaled, and applied inside organizations. Leaders who treat AI as incremental technology risk missing the much larger transformation underway.Brown explains why he believes we are entering an “intelligence age,” comparable in scope to the Industrial Revolution, but unfolding at a dramatically faster pace. As the cost of intelligence approaches zero, organizations will face new strategic choices about workforce design, value creation, leadership identity, and ethical responsibility. These choices, Brown argues, cannot be delegated or delayed without consequence.Throughout the episode, Mahan challenges Brown to bridge theory and practice. They explore real organizational examples, from AI agents working alongside humans to scientific breakthroughs like AlphaFold, and examine how leaders can shift from efficiency-driven thinking toward value creation, judgment, and human amplification.This is not a conversation about tools or trends. It is a candid discussion about leadership responsibility in a period of accelerated change, and what CEOs and senior executives must rethink now to ensure their organizations remain relevant, resilient, and human-centered.Actionable TakeawaysYou'll learn why delaying AI decisions is itself a leadership choice, and how waiting for clarity can quietly erode organizational value.Hear how the “intelligence age” differs from previous technology shifts, and why its speed changes the role of senior leadership.You'll learn why AI should be viewed as a digital workforce, not just software, and what that means for strategy, structure, and accountability.Hear how leaders must shift from being the source of answers to guiding exploration, judgment, and learning in uncertain conditions.You'll learn why cost-cutting is the weakest use of AI, and where leaders should instead focus to create new value.Hear how AI changes the relevance of experience, narrowing gaps while raising expectations for judgment and insight.You'll learn why ethics, bias, and responsibility do not belong to algorithms, but remain firmly in the domain of leadership.Hear how AI can amplify human capability rather than replace it, when leaders design work intentionally.Connect with Steve BrownSteve Brown Website Steve Brown LinkedInThe AI Ultimatum: Preparing for a World of Intelligent Machines and Radical TransformationConnect with Mahan Tavakoli: Mahan Tavakoli Website Mahan Tavakoli on LinkedIn Partnering Leadership Website

Tara breaks down the most shocking developments in politics, government fraud, and cutting-edge science. From Obama-era birthright citizenship loopholes that could let 1.2 million U.S. citizens raised in China vote by 2030, to Republican Senate obstruction blocking Trump-era reforms, and trillions lost to fraud in federal spending. Plus, a jaw-dropping human interest story: an Australian entrepreneur cures his dog's terminal cancer using AI and RNA therapy, proving innovation thrives when bureaucracy doesn't get in the way.

A tech entrepreneur with no biology background used ChatGPT, AlphaFold, and Grok to design a cancer vaccine for his dog (tumor shrank 75%), Pokemon Go players unknowingly generated 30 billion images now used to train delivery robots, and 4D Gaussian splats are streaming in real time in your browser.00:00 Introduction01:50 AI-Designed Cancer Vaccine for a Dog23:29 Pokemon Go Secretly Trained AI Robots44:03 4D Gaussian Splats Streaming in BrowserPowered by Dell Pro Precision : https://creatorfolio.co/badxstudiohttps://creatorfolio.co/badxstudio3

In this episode of Data in Biotech, host Ross Katz sits down with Ben Locwin, Vice President at Reliant Life Sciences, to explore the evolving landscape of artificial intelligence in biotechnology.  Join us as we discuss why nearly every biotech claims to use AI but few actually do, examine successful applications like AlphaFold, and explore the challenges of implementing AI across drug development, manufacturing, and regulatory processes. Ben shares insights on maintaining healthy skepticism, understanding data provenance, and looking ahead to what this year may bring for AI in life sciences. What you'll learn in this episode:  >> The AI hype problem in biotech and why most companies claim to use AI but few actually do. >> AlphaFold as the gold standard and how DeepMind's protein structure prediction model represents the most successful application of AI in biotech >> Data quality over algorithmic sophistication and the critical importance of data provenance, examining primary sources, and understanding that data quality matters more than the complexity of the AI model >> The balance between optimism and evidence-based decision-making, distinguishing between sophisticated AI and advanced statistical modeling Meet our guest: Ben Locwin is a healthcare and life sciences executive and medical scientist known for helping bring pharmaceuticals, vaccines, and medical devices to market faster and with higher quality. A TEDx speaker and seasoned leader, he's worked across major biotech hubs and has deep expertise in global regulatory pathways, having collaborated with the FDA, EMA, MHRA, PMDA, and more. Connect with Ben Locwin on LinkedIn  About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Ross Katz on LinkedIn Connect with us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode! Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.

José Gonzáles makes quiet music full of loud ideas. I sat down with him in person to trace his journey from playing in hardcore punk bands to the intimate arpeggios that turned Veneer and Heartbeats into global touchstones. Jose opens up about writing “humble accusations,” using minimal sound to deliver maximal ideas, and how a scientist's method—shaped by his biochemistry background—helps him build tension, release, and meaning inside quiet music.It all gets a bit Dawkins: Jose unpacks meme complexes, the cultural building blocks that replicate from brain to brain, and shows how his work recombines influences from Latin America, Sweden, and shelves of philosophy and science audiobooks. We explore the thread that runs from early relationship sketches to pointed critiques of dogma, moral relativism, and “doomsday dudes,” all while keeping the songs spacious enough to live at dinner tables and in headphones after midnight.We talk about how on his new album –  Against the Dying Light, José connects Enlightenment values—reason, empiricism, individual liberty—to today's urgent questions around AI, engineered risks, and human flourishing. He celebrates breakthroughs like AlphaFold while calling for alignment, transparency, and calm public reasoning. The result is a rare balance: optimism without naivety, warning without hysteria, and art that invites you to think without telling you what to think.I've re-activated the show's Substack newsletter. Give it a follow for extra bits about the guests, thoughts on music culture and creativity and whatever else. Nothing is behind a paywall yet, so it's a great time to get on board.If you enjoy Lost and Sound and want to help keep it thriving, the best way to support is simple: subscribe, leave a rating, and write a quick review on your favourite podcast platform. It really helps others find the show. You can do that here on Apple Podcasts or wherever you like to listen.José Gonzáles on Instagram:www.instagram.com/jose.gonz.musicJosé Gonzáles on Bandcamp:https://josgonzlez.bandcamp.com/album/against-the-dying-of-the-lightHuge thanks to Audio-Technica – makers of beautifully engineered audio gear and sponsors of Lost and Sound. Check them out here: Audio-TechnicaMy book Coming To Berlin is a journey through the city's creative underground, and is available via Velocity Press.You can also follow me on Instagram at @paulhanford for behind-the-scenes bits, guest updates, and whatever else is bubbling up.

This week's stories: Sinclair's This Is the Test: Are we about to see age reversal in humans? At the World Governments Summit 2026 in Dubai, Harvard geneticist David Sinclair told world leaders that ageing could soon be reversible and said the first human clinical trials of epigenetic reprogramming therapies are moving forward. The core idea is that ageing is partly an information problem, how cells read DNA, not just cumulative damage, and that partial reprogramming could restore youthful function without turning tissues into tumors. Dave frames this as a rare binary moment for longevity: either early, localized human trials (starting with tightly controlled tissue targets like the eye) show meaningful functional rejuvenation with acceptable safety, or the field has to recalibrate fast. Either way, the next couple of years will heavily influence where money, regulators, and serious researchers place their bets. • Sources: – World Governments Summit: https://www.worldgovernmentssummit.org/media-hub/news/detail/ageing-could-soon-be-reversible-says-harvard-scientist-at-wgs-2026 – NAD / Life Biosciences coverage: https://www.nad.com/news/fda-greenlights-life-biosciences-human-study-setting-up-pivotal-test-for-aging-theory-from-harvards-david-sinclair AlphaFold 4 in a locked box: DeepMind's private AI drug design engine Isomorphic Labs, DeepMind's drug discovery company, unveiled a proprietary drug design engine that outside scientists are comparing to an AlphaFold 4 moment, but for designing drugs, not just predicting structures. The big shift is that this system is closed: no public weights, no open database, and access appears to flow through partnerships with pharma companies. Dave breaks down why that matters for the longevity world: if AI makes early discovery cheaper and faster, we might see more serious shots on ageing targets over the next decade, but a closed model can also mean less transparency, bigger IP moats, and no guarantee that faster discovery leads to cheaper drugs. • Sources: – Nature: https://www.nature.com/articles/d41586-026-00365-7 – Isomorphic Labs: https://www.isomorphiclabs.com/articles/the-isomorphic-labs-drug-design-engine-unlocks-a-new-frontier Peptides in the freezer: El Mencho's anti aging stash and the dark side of wellness After reports and images from the final hideout linked to Jalisco New Generation Cartel leader Nemesio Oseguera Cervantes (El Mencho), coverage highlighted a detail that feels uncomfortably familiar to anyone in the modern wellness internet: injectable vials stored in a freezer with a schedule attached, including Tationil Plus, a glutathione based injectable marketed in some places for “cellular health,” cosmetic effects, and anti ageing. Dave uses the absurdity as a narrative wedge, not cartel gossip, to talk about how normalized gray market injectables have become, and how marketing (“detox,” “cellular reset”) often outruns evidence and safety. The segment pivots into a practical filter: which compounds are real therapeutics under medical supervision, and which are expensive folklore with sourcing risk and unknown long term downsides. • Sources: – New York Post: https://nypost.com/2026/02/25/world-news/inside-the-luxurious-love-nest-where-mexican-drug-lord-el-mencho-spent-his-final-days/ – Sky News (Reuters photos referenced): https://news.sky.com/story/inside-the-mexican-villa-where-feared-drug-lord-el-mencho-spent-final-hours-13511954 – Reuters photo gallery: https://www.reuters.com/pictures/el-menchos-last-hideout-inside-villa-where-cartel-leader-spent-final-hours-2026-02-25/W7DK5WEXS5IMLLZQO2P3CXGXFM The disease we thought was dead: measles comes roaring back Measles cases have surged in early 2026, with reporting citing at least 588 cases in the U.S. by late January, already more than many full year totals, and additional updates showing continued acceleration into February. Dave reframes this as a healthspan floor issue: you can argue about peptides and mitochondria all day, but measles is so contagious that once community immunity drops, outbreaks move fast and hit the most vulnerable first, especially infants and immunocompromised people. He also flags the systems problem: many clinicians have never seen measles, which increases the odds of delayed recognition and wider exposure in waiting rooms. The actionable move is boring and high ROI: verify MMR status for you and your family and close gaps before outbreaks get closer to home. • Sources: – AMA Morning Rounds (Week of Feb. 2, 2026): https://www.ama-assn.org/about/publications-newsletters/top-news-stories-ama-morning-rounds-week-feb-2-2026 – ABC News (CDC case count coverage): https://abcnews.com/Health/588-us-measles-cases-reported-january-cdc/story?id=129699078 – CIDRAP (case tracking context): https://www.cidrap.umn.edu/measles/us-measles-cases-soar-588-so-far-year-south-carolina-confirms-58-new-infections DC vs your health: Trump's State of the Union health reset President Donald Trump's 2026 State of the Union included a cluster of healthcare themes that function as a directional signal for agencies and payers this year, including drug pricing rhetoric, price transparency, and broader coverage and affordability framing. Dave translates the politics into a practical heuristic for biohackers: federal posture quietly determines what becomes easy versus painful to access in the legitimate system, from GLP 1 coverage rules and prior auth behavior to how friendly the environment is for telehealth, at home diagnostics, and eventually whatever “real longevity medicine” looks like. You do not need every policy detail in a weekly rundown, just the weather report: reimbursement and enforcement trends shape what stays niche, what scales, and what gets friction. • Sources: – Advisory Board: https://www.advisory.com/daily-briefing/2026/02/25/health-policy-roundup – Healthcare Dive: https://www.healthcaredive.com/news/trump-state-of-the-union-healthcare-2026/812962/ – This Week in Public Health analysis: https://thisweekinpublichealth.com/blog/2026/02/25/the-2026-state-of-the-union-what-it-means-for-health-and-public-health/ All source links are provided for direct access to the original reporting and research. This episode is designed for biohackers, longevity seekers, and high-performance listeners who want mechanism-level clarity on circadian biology, neurodegeneration signals, cognitive training, caffeine strategy, and supplement regulation. Host Dave Asprey connects emerging science, behavioral data, and policy shifts into practical frameworks you can use to build a resilient, adaptable health stack. New episodes every Tuesday, Thursday, Friday, and Sunday. Keywords: David Sinclair age reversal, epigenetic reprogramming therapy, Yamanaka factors OSK, Life Biosciences clinical trial, human rejuvenation trial 2026, biological age reset, longevity breakthrough news, DeepMind Isomorphic Labs, AlphaFold 4 drug design, AI drug discovery engine, geroprotective drug development, peptide gray market risks, injectable glutathathione Tationil Plus, GLP-1 regulation FDA warning, wellness industry regulation, measles outbreak 2026 US, MMR vaccine status adults, vaccine trust public health, health policy 2026 State of the Union, GLP-1 access and reimbursement, telehealth longevity care, biohacking news, anti-aging research update Thank you to our sponsors! Resources: • Get My 2026 Clean Nicotine Roadmap | Enroll for free at https://daveasprey.com/2026-clean-nicotine-roadmap/ • Get My 2026 Biohacking Trends Report: https://daveasprey.com/2026-biohacking-trends-report/ • Dave Asprey's Latest News | Go to https://daveasprey.com/ to join Inside Track today. • Danger Coffee: https://dangercoffee.com/discount/dave15 • My Daily Supplements: SuppGrade Labs (15% Off) • Favorite Blue Light Blocking Glasses: TrueDark (15% Off) • Dave Asprey's BEYOND Conference: https://beyondconference.com • Dave Asprey's New Book – Heavily Meditated: https://daveasprey.com/heavily-meditated • Join My Substack (Live Access To Podcast Recordings): https://substack.daveasprey.com/ • Upgrade Labs: https://upgradelabs.com Timestamps: 0:00 - Introduction 0:30 - Story #1: David Sinclair 2026 2:13 - Story #2: Google Drug Discovery 3:48 - Story #3: El Mencho Biohacking5:30 - Story #4: Measles Outbreak 6:51 - Story #5: Trump State of the Union 8:00 - Weekly Roundup 9:10 - Closing See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

EPISODE 157 | Bohemian Books: Gigas, Voynich & Soyga Some very old books have an air of mystery and intrigue about them. Partly, that's because they are literally hundreds of years old, and partly because of the weird things they contain.  Today, we'll take a look at three, all of which have a connection to the Czech Republic and Prague: the biggest book in the world, the Codex Gigas (also known as the Devil's Bible and which features heavily [no pun intended] in Dan Brown's latest schlock fest), the utterly baffling Voynich Manuscript, which is not written in any recognizable language; and the mysterious Book of Soyga, which disappeared for nearly 400 years, and some say that if you can decipher the final puzzles in the book, you will die. Like what we do? Then buy us a beer or three via our page on Buy Me a Coffee.  Review us here or on IMDb. And seriously, subscribe, will ya? Like, just do it.  SECTIONS 02:11 - The Codex Gigas - That's a big book, contents, legend of origin, Sweden gets it, defenestrations, the Sedlec Bone Church, The Secret of Secrets 11:00 - The Voynich Manuscript - WTF is this thing?, ownership relay, who maybe wrote it, what maybe it says, aspects of Voynichese, obscure languages, steganography, glossolalia, outsider art, a hoax, radiocarbon dating, those who have claimed decipherment, ciphers, people see what they want to, goropism, the Sun Language Theory, recent videos about Alphafold and protein folding, maybe a work of proto-fiction 43:32 - The Book of Soyga - John Dee, Edward Kelley, cryptic puzzles, 400 years lost, found in 1994 Music by Fanette Ronjat More Info The Codex Gigas – Devil's Bible on the National Library of Sweden website The Devil's Bible: My Deep Dive into the Weirdest Book I've Ever Seen Devil's Bible: Codex Gigas in Klementinum on Prague.net from 2007 loan Inside the ‘Devil's Bible,' the Largest Medieval Manuscript Ever Made on ArtNet EPISODE 109 | What's in a Name? The Shakespeare Authorship Debate with Scott Jackson EPISODE 135 | On Shakey Ground: More Shakespeare Authorship with Scott Jackson What Shakespeare Can Teach Us About Communicating with Jennifer King on the Digital Signage Done Right podcast Yale Library webpage on the Voynich Manuscript, with images The riddle of the Voynich Manuscript on the BBC Unsolved Mystery: The Voynich Manuscript An entire website about the Voynich Manuscript The Voynich Manuscript revealed: five things you probably didn't know about the Medieval masterpiece on The Art Newspaper THE VOYNICH MANUSCRIPT - "The Most Mysterious Manuscript in the World" - NSA report (PDF) Another NSA report on titled The Voynich Manuscript: An Elegant Enigma written in 1978 (PDF) A PDF of the actual Voynich Manuscript Headcanon: The Voynich Manuscript actually doesn't contain any cohesive text and is just a prank done by someone in the past on r/medieval A Scholar Has Cracked the Mystery of the Voynich Manuscript, the Encrypted Medieval Artwork That Defeated Codebreakers for Years on ArtNet Article on the Voynich manuscript on Brazilian website Revista Pesquisa Fapesp The Voynich Wiki How an Emperor Trapped a Con Man - blog on Edward kelley Magic and Mystery: Decoding the Secrets of the Book of Soyga on Discovery The Book of Soyga translated by Jane Kupin (PDF) Decoding the Book of Soyga: A Living Project of Esoteric Discovery The Book of Soyga | Literary History on House of Cadmus Soyga: the book that kills on Blog of Wonders Holy Conversations: The Impact of the Mysterious Book of Soyga on Ancient Origins Book of Soyga on the Voynich Wiki Follow us on social: Facebook X (Twitter) Other Podcasts by Derek DeWitt DIGITAL SIGNAGE DONE RIGHT - Winner of a Gold Quill Award, Gold MarCom Award, AVA Digital Award Gold, Silver Davey Award, and Communicator Award of Excellence, and on numerous top 10 podcast lists.  PRAGUE TIMES - A city is more than just a location - it's a kaleidoscope of history, places, people and trends. This podcast looks at Prague, in the center of Europe, from a number of perspectives, including what it is now, what is has been and where it's going. It's Prague THEN, Prague NOW, Prague LATER 

This episode explores the vision of Demis Hassabis, CEO of Google DeepMind and recipient of the 2024 Nobel Prize in Chemistry. Hassabis argues that 2026 marks a pivotal turning point in human history, as we enter what he describes as an “AI Renaissance”—an era whose impact could be ten times greater than the Industrial Revolution, unfolding at ten times the speed. He predicts that Artificial General Intelligence (AGI) could be achieved before 2030, while cautioning that today's AI systems remain in a state of “jagged intelligence,” still lacking robust reasoning and long-term planning capabilities. As the industry enters a phase of consolidation, Hassabis is focused on transforming AI into a scientific engine. Through breakthroughs such as AlphaFold and initiatives like Isomorphic Labs, he aims to reshape drug discovery, while collaborations with the U.S. Department of Energy—such as the “Genesis Project”—seek to accelerate progress in energy innovation. At the core of his vision is the concept of “Radical Abundance.” As AI drives the marginal cost of healthcare and energy toward near zero, society may begin to transition into a post-scarcity era. To navigate this shift, Hassabis proposes new social mechanisms, including a “Global Abundance Dividend,” and emphasizes that AI governance must extend beyond technologists, requiring international cooperation to ensure these technologies benefit all of humanity.本集的內容將帶您深入探索 Google DeepMind 執行長、2024 年諾貝爾化學獎得主 戴米斯·哈薩比斯 (Demis Hassabis) 的遠見。哈薩比斯指出 2026 年是人類歷史的轉折點，我們正進入一個「AI 文藝復興」時代，其影響力將是工業革命的十倍，且發展速度快上十倍。 哈薩比斯預測通用人工智能 (AGI) 可能在 2030 年前實現，但警告現今 AI 仍處於「參差不齊的智能」狀態，必須克服基礎推理與長期規劃的缺陷。隨著行業進入「洗牌期」，他致力於將 AI 轉化為科學引擎，透過 AlphaFold 與 Isomorphic Labs 變革藥物研發，並與美國能源部合作「創世紀任務」以加速能源突破。 他最核心的觀點是 「激進豐饒」(Radical Abundance)：當 AI 讓醫療與能源成本趨近於零，人類將邁向「後稀缺」社會。為應對此轉變，他提出「全球豐饒紅利」等社會機制，並強調 AI 治理不能僅留給技術專家，需透過國際合作確保這項技術能造福全人類。 Powered by Firstory Hosting

OpenClaw's creator makes headlines by joining OpenAI after GitHub fame and a whirlwind of VC and big tech offers, redefining what's possible for independent developers in the AI arms race. Is this the year agentic AI goes mainstream, and are the big players ready for that disruption? OpenClaw, OpenAI and the future | Peter Steinberger OpenAI disbands mission alignment team Opinion | I Left My Job at OpenAI. Putting Ads on ChatGPT Was the Last Straw. - The New York Times Introducing GPT‑5.3‑Codex‑Spark Anthropic releases Sonnet 4.6 Exclusive: Pentagon threatens to cut off Anthropic in AI safeguards dispute Google's Pixel 10a Launches on March 5 for $499 Google's AI drug discovery spinoff Isomorphic Labs claims major leap beyond AlphaFold 3 Gemini 3 Deep Think: AI model update designed for science Radio host David Greene says Google's NotebookLM tool stole his voice A new way to express yourself: Gemini can now create music Why an A.I. Video of Tom Cruise Battling Brad Pitt Spooked Hollywood GPT-5 outperforms federal judges 100% to 52% in legal reasoning experiment An AI project is creating videos to go with Supreme Court justices' real words I used Claude to negotiate $163,000 off a hospital bill. In a complex healthcare system, AI is giving patients power. Sony Tech Can Identify Original Music in AI-Generated Songs AI Pioneer Fei-Fei Li's Startup World Labs Raises $1 Billion Yann v. Yoshua on directed systems Dr. Oz pushes AI avatars as a fix for rural health care. Not so fast, critics say An AI Agent Published a Hit Piece on Me An Ars Technica Reporter Blamed A.I. Tools for Fabricating Quotes in a Bizarre A.I. Story Plain Dealer using AI to write reporters' stories Mediahuis trials use of AI agents to carry out 'first-line' news reporting DJI's first robovac is an autonomous cleaning drone you can't trust Leaked Email Suggests Ring Plans to Expand 'Search Party' Surveillance Beyond Dogs ai;dr I hate my AI pet with every fiber of my being Thanks a lot, AI: Hard drives are sold out for the year, says WD Students Are Being Treated Like Guinea Pigs:' Inside an AI-Powered Private School peon-ping — Stop babysitting your terminal Hugo Barra makes a to-do agent Raspberry Pi soars 40% as CEO buys stock, AI chatter builds Hosts: Leo Laporte, Jeff Jarvis, and Emily Forlini Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: monarch.com with code IM bitwarden.com/twit preview.modulate.ai spaceship.com/twit

OpenClaw's creator makes headlines by joining OpenAI after GitHub fame and a whirlwind of VC and big tech offers, redefining what's possible for independent developers in the AI arms race. Is this the year agentic AI goes mainstream, and are the big players ready for that disruption? OpenClaw, OpenAI and the future | Peter Steinberger OpenAI disbands mission alignment team Opinion | I Left My Job at OpenAI. Putting Ads on ChatGPT Was the Last Straw. - The New York Times Introducing GPT‑5.3‑Codex‑Spark Anthropic releases Sonnet 4.6 Exclusive: Pentagon threatens to cut off Anthropic in AI safeguards dispute Google's Pixel 10a Launches on March 5 for $499 Google's AI drug discovery spinoff Isomorphic Labs claims major leap beyond AlphaFold 3 Gemini 3 Deep Think: AI model update designed for science Radio host David Greene says Google's NotebookLM tool stole his voice A new way to express yourself: Gemini can now create music Why an A.I. Video of Tom Cruise Battling Brad Pitt Spooked Hollywood GPT-5 outperforms federal judges 100% to 52% in legal reasoning experiment An AI project is creating videos to go with Supreme Court justices' real words I used Claude to negotiate $163,000 off a hospital bill. In a complex healthcare system, AI is giving patients power. Sony Tech Can Identify Original Music in AI-Generated Songs AI Pioneer Fei-Fei Li's Startup World Labs Raises $1 Billion Yann v. Yoshua on directed systems Dr. Oz pushes AI avatars as a fix for rural health care. Not so fast, critics say An AI Agent Published a Hit Piece on Me An Ars Technica Reporter Blamed A.I. Tools for Fabricating Quotes in a Bizarre A.I. Story Plain Dealer using AI to write reporters' stories Mediahuis trials use of AI agents to carry out 'first-line' news reporting DJI's first robovac is an autonomous cleaning drone you can't trust Leaked Email Suggests Ring Plans to Expand 'Search Party' Surveillance Beyond Dogs ai;dr I hate my AI pet with every fiber of my being Thanks a lot, AI: Hard drives are sold out for the year, says WD Students Are Being Treated Like Guinea Pigs:' Inside an AI-Powered Private School peon-ping — Stop babysitting your terminal Hugo Barra makes a to-do agent Raspberry Pi soars 40% as CEO buys stock, AI chatter builds Hosts: Leo Laporte, Jeff Jarvis, and Emily Forlini Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: monarch.com with code IM bitwarden.com/twit preview.modulate.ai spaceship.com/twit

OpenClaw's creator makes headlines by joining OpenAI after GitHub fame and a whirlwind of VC and big tech offers, redefining what's possible for independent developers in the AI arms race. Is this the year agentic AI goes mainstream, and are the big players ready for that disruption? OpenClaw, OpenAI and the future | Peter Steinberger OpenAI disbands mission alignment team Opinion | I Left My Job at OpenAI. Putting Ads on ChatGPT Was the Last Straw. - The New York Times Introducing GPT‑5.3‑Codex‑Spark Anthropic releases Sonnet 4.6 Exclusive: Pentagon threatens to cut off Anthropic in AI safeguards dispute Google's Pixel 10a Launches on March 5 for $499 Google's AI drug discovery spinoff Isomorphic Labs claims major leap beyond AlphaFold 3 Gemini 3 Deep Think: AI model update designed for science Radio host David Greene says Google's NotebookLM tool stole his voice A new way to express yourself: Gemini can now create music Why an A.I. Video of Tom Cruise Battling Brad Pitt Spooked Hollywood GPT-5 outperforms federal judges 100% to 52% in legal reasoning experiment An AI project is creating videos to go with Supreme Court justices' real words I used Claude to negotiate $163,000 off a hospital bill. In a complex healthcare system, AI is giving patients power. Sony Tech Can Identify Original Music in AI-Generated Songs AI Pioneer Fei-Fei Li's Startup World Labs Raises $1 Billion Yann v. Yoshua on directed systems Dr. Oz pushes AI avatars as a fix for rural health care. Not so fast, critics say An AI Agent Published a Hit Piece on Me An Ars Technica Reporter Blamed A.I. Tools for Fabricating Quotes in a Bizarre A.I. Story Plain Dealer using AI to write reporters' stories Mediahuis trials use of AI agents to carry out 'first-line' news reporting DJI's first robovac is an autonomous cleaning drone you can't trust Leaked Email Suggests Ring Plans to Expand 'Search Party' Surveillance Beyond Dogs ai;dr I hate my AI pet with every fiber of my being Thanks a lot, AI: Hard drives are sold out for the year, says WD Students Are Being Treated Like Guinea Pigs:' Inside an AI-Powered Private School peon-ping — Stop babysitting your terminal Hugo Barra makes a to-do agent Raspberry Pi soars 40% as CEO buys stock, AI chatter builds Hosts: Leo Laporte, Jeff Jarvis, and Emily Forlini Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: monarch.com with code IM bitwarden.com/twit preview.modulate.ai spaceship.com/twit

OpenClaw's creator makes headlines by joining OpenAI after GitHub fame and a whirlwind of VC and big tech offers, redefining what's possible for independent developers in the AI arms race. Is this the year agentic AI goes mainstream, and are the big players ready for that disruption? OpenClaw, OpenAI and the future | Peter Steinberger OpenAI disbands mission alignment team Opinion | I Left My Job at OpenAI. Putting Ads on ChatGPT Was the Last Straw. - The New York Times Introducing GPT‑5.3‑Codex‑Spark Anthropic releases Sonnet 4.6 Exclusive: Pentagon threatens to cut off Anthropic in AI safeguards dispute Google's Pixel 10a Launches on March 5 for $499 Google's AI drug discovery spinoff Isomorphic Labs claims major leap beyond AlphaFold 3 Gemini 3 Deep Think: AI model update designed for science Radio host David Greene says Google's NotebookLM tool stole his voice A new way to express yourself: Gemini can now create music Why an A.I. Video of Tom Cruise Battling Brad Pitt Spooked Hollywood GPT-5 outperforms federal judges 100% to 52% in legal reasoning experiment An AI project is creating videos to go with Supreme Court justices' real words I used Claude to negotiate $163,000 off a hospital bill. In a complex healthcare system, AI is giving patients power. Sony Tech Can Identify Original Music in AI-Generated Songs AI Pioneer Fei-Fei Li's Startup World Labs Raises $1 Billion Yann v. Yoshua on directed systems Dr. Oz pushes AI avatars as a fix for rural health care. Not so fast, critics say An AI Agent Published a Hit Piece on Me An Ars Technica Reporter Blamed A.I. Tools for Fabricating Quotes in a Bizarre A.I. Story Plain Dealer using AI to write reporters' stories Mediahuis trials use of AI agents to carry out 'first-line' news reporting DJI's first robovac is an autonomous cleaning drone you can't trust Leaked Email Suggests Ring Plans to Expand 'Search Party' Surveillance Beyond Dogs ai;dr I hate my AI pet with every fiber of my being Thanks a lot, AI: Hard drives are sold out for the year, says WD Students Are Being Treated Like Guinea Pigs:' Inside an AI-Powered Private School peon-ping — Stop babysitting your terminal Hugo Barra makes a to-do agent Raspberry Pi soars 40% as CEO buys stock, AI chatter builds Hosts: Leo Laporte, Jeff Jarvis, and Emily Forlini Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: monarch.com with code IM bitwarden.com/twit preview.modulate.ai spaceship.com/twit

OpenClaw's creator makes headlines by joining OpenAI after GitHub fame and a whirlwind of VC and big tech offers, redefining what's possible for independent developers in the AI arms race. Is this the year agentic AI goes mainstream, and are the big players ready for that disruption? OpenClaw, OpenAI and the future | Peter Steinberger OpenAI disbands mission alignment team Opinion | I Left My Job at OpenAI. Putting Ads on ChatGPT Was the Last Straw. - The New York Times Introducing GPT‑5.3‑Codex‑Spark Anthropic releases Sonnet 4.6 Exclusive: Pentagon threatens to cut off Anthropic in AI safeguards dispute Google's Pixel 10a Launches on March 5 for $499 Google's AI drug discovery spinoff Isomorphic Labs claims major leap beyond AlphaFold 3 Gemini 3 Deep Think: AI model update designed for science Radio host David Greene says Google's NotebookLM tool stole his voice A new way to express yourself: Gemini can now create music Why an A.I. Video of Tom Cruise Battling Brad Pitt Spooked Hollywood GPT-5 outperforms federal judges 100% to 52% in legal reasoning experiment An AI project is creating videos to go with Supreme Court justices' real words I used Claude to negotiate $163,000 off a hospital bill. In a complex healthcare system, AI is giving patients power. Sony Tech Can Identify Original Music in AI-Generated Songs AI Pioneer Fei-Fei Li's Startup World Labs Raises $1 Billion Yann v. Yoshua on directed systems Dr. Oz pushes AI avatars as a fix for rural health care. Not so fast, critics say An AI Agent Published a Hit Piece on Me An Ars Technica Reporter Blamed A.I. Tools for Fabricating Quotes in a Bizarre A.I. Story Plain Dealer using AI to write reporters' stories Mediahuis trials use of AI agents to carry out 'first-line' news reporting DJI's first robovac is an autonomous cleaning drone you can't trust Leaked Email Suggests Ring Plans to Expand 'Search Party' Surveillance Beyond Dogs ai;dr I hate my AI pet with every fiber of my being Thanks a lot, AI: Hard drives are sold out for the year, says WD Students Are Being Treated Like Guinea Pigs:' Inside an AI-Powered Private School peon-ping — Stop babysitting your terminal Hugo Barra makes a to-do agent Raspberry Pi soars 40% as CEO buys stock, AI chatter builds Hosts: Leo Laporte, Jeff Jarvis, and Emily Forlini Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: monarch.com with code IM bitwarden.com/twit preview.modulate.ai spaceship.com/twit

This podcast features Gabriele Corso and Jeremy Wohlwend, co-founders of Boltz and authors of the Boltz Manifesto, discussing the rapid evolution of structural biology models from AlphaFold to their own open-source suite, Boltz-1 and Boltz-2. The central thesis is that while single-chain protein structure prediction is largely “solved” through evolutionary hints, the next frontier lies in modeling complex interactions (protein-ligand, protein-protein) and generative protein design, which Boltz aims to democratize via open-source foundations and scalable infrastructure.Full Video PodOn YouTube!Timestamps* 00:00 Introduction to Benchmarking and the “Solved” Protein Problem* 06:48 Evolutionary Hints and Co-evolution in Structure Prediction* 10:00 The Importance of Protein Function and Disease States* 15:31 Transitioning from AlphaFold 2 to AlphaFold 3 Capabilities* 19:48 Generative Modeling vs. Regression in Structural Biology* 25:00 The “Bitter Lesson” and Specialized AI Architectures* 29:14 Development Anecdotes: Training Boltz-1 on a Budget* 32:00 Validation Strategies and the Protein Data Bank (PDB)* 37:26 The Mission of Boltz: Democratizing Access and Open Source* 41:43 Building a Self-Sustaining Research Community* 44:40 Boltz-2 Advancements: Affinity Prediction and Design* 51:03 BoltzGen: Merging Structure and Sequence Prediction* 55:18 Large-Scale Wet Lab Validation Results* 01:02:44 Boltz Lab Product Launch: Agents and Infrastructure* 01:13:06 Future Directions: Developpability and the “Virtual Cell”* 01:17:35 Interacting with Skeptical Medicinal ChemistsKey SummaryEvolution of Structure Prediction & Evolutionary Hints* Co-evolutionary Landscapes: The speakers explain that breakthrough progress in single-chain protein prediction relied on decoding evolutionary correlations where mutations in one position necessitate mutations in another to conserve 3D structure.* Structure vs. Folding: They differentiate between structure prediction (getting the final answer) and folding (the kinetic process of reaching that state), noting that the field is still quite poor at modeling the latter.* Physics vs. Statistics: RJ posits that while models use evolutionary statistics to find the right “valley” in the energy landscape, they likely possess a “light understanding” of physics to refine the local minimum.The Shift to Generative Architectures* Generative Modeling: A key leap in AlphaFold 3 and Boltz-1 was moving from regression (predicting one static coordinate) to a generative diffusion approach that samples from a posterior distribution.* Handling Uncertainty: This shift allows models to represent multiple conformational states and avoid the “averaging” effect seen in regression models when the ground truth is ambiguous.* Specialized Architectures: Despite the “bitter lesson” of general-purpose transformers, the speakers argue that equivariant architectures remain vastly superior for biological data due to the inherent 3D geometric constraints of molecules.Boltz-2 and Generative Protein Design* Unified Encoding: Boltz-2 (and BoltzGen) treats structure and sequence prediction as a single task by encoding amino acid identities into the atomic composition of the predicted structure.* Design Specifics: Instead of a sequence, users feed the model blank tokens and a high-level “spec” (e.g., an antibody framework), and the model decodes both the 3D structure and the corresponding amino acids.* Affinity Prediction: While model confidence is a common metric, Boltz-2 focuses on affinity prediction—quantifying exactly how tightly a designed binder will stick to its target.Real-World Validation and Productization* Generalized Validation: To prove the model isn't just “regurgitating” known data, Boltz tested its designs on 9 targets with zero known interactions in the PDB, achieving nanomolar binders for two-thirds of them.* Boltz Lab Infrastructure: The newly launched Boltz Lab platform provides “agents” for protein and small molecule design, optimized to run 10x faster than open-source versions through proprietary GPU kernels.* Human-in-the-Loop: The platform is designed to convert skeptical medicinal chemists by allowing them to run parallel screens and use their intuition to filter model outputs.TranscriptRJ [00:05:35]: But the goal remains to, like, you know, really challenge the models, like, how well do these models generalize? And, you know, we've seen in some of the latest CASP competitions, like, while we've become really, really good at proteins, especially monomeric proteins, you know, other modalities still remain pretty difficult. So it's really essential, you know, in the field that there are, like, these efforts to gather, you know, benchmarks that are challenging. So it keeps us in line, you know, about what the models can do or not.Gabriel [00:06:26]: Yeah, it's interesting you say that, like, in some sense, CASP, you know, at CASP 14, a problem was solved and, like, pretty comprehensively, right? But at the same time, it was really only the beginning. So you can say, like, what was the specific problem you would argue was solved? And then, like, you know, what is remaining, which is probably quite open.RJ [00:06:48]: I think we'll steer away from the term solved, because we have many friends in the community who get pretty upset at that word. And I think, you know, fairly so. But the problem that was, you know, that a lot of progress was made on was the ability to predict the structure of single chain proteins. So proteins can, like, be composed of many chains. And single chain proteins are, you know, just a single sequence of amino acids. And one of the reasons that we've been able to make such progress is also because we take a lot of hints from evolution. So the way the models work is that, you know, they sort of decode a lot of hints. That comes from evolutionary landscapes. So if you have, like, you know, some protein in an animal, and you go find the similar protein across, like, you know, different organisms, you might find different mutations in them. And as it turns out, if you take a lot of the sequences together, and you analyze them, you see that some positions in the sequence tend to evolve at the same time as other positions in the sequence, sort of this, like, correlation between different positions. And it turns out that that is typically a hint that these two positions are close in three dimension. So part of the, you know, part of the breakthrough has been, like, our ability to also decode that very, very effectively. But what it implies also is that in absence of that co-evolutionary landscape, the models don't quite perform as well. And so, you know, I think when that information is available, maybe one could say, you know, the problem is, like, somewhat solved. From the perspective of structure prediction, when it isn't, it's much more challenging. And I think it's also worth also differentiating the, sometimes we confound a little bit, structure prediction and folding. Folding is the more complex process of actually understanding, like, how it goes from, like, this disordered state into, like, a structured, like, state. And that I don't think we've made that much progress on. But the idea of, like, yeah, going straight to the answer, we've become pretty good at.Brandon [00:08:49]: So there's this protein that is, like, just a long chain and it folds up. Yeah. And so we're good at getting from that long chain in whatever form it was originally to the thing. But we don't know how it necessarily gets to that state. And there might be intermediate states that it's in sometimes that we're not aware of.RJ [00:09:10]: That's right. And that relates also to, like, you know, our general ability to model, like, the different, you know, proteins are not static. They move, they take different shapes based on their energy states. And I think we are, also not that good at understanding the different states that the protein can be in and at what frequency, what probability. So I think the two problems are quite related in some ways. Still a lot to solve. But I think it was very surprising at the time, you know, that even with these evolutionary hints that we were able to, you know, to make such dramatic progress.Brandon [00:09:45]: So I want to ask, why does the intermediate states matter? But first, I kind of want to understand, why do we care? What proteins are shaped like?Gabriel [00:09:54]: Yeah, I mean, the proteins are kind of the machines of our body. You know, the way that all the processes that we have in our cells, you know, work is typically through proteins, sometimes other molecules, sort of intermediate interactions. And through that interactions, we have all sorts of cell functions. And so when we try to understand, you know, a lot of biology, how our body works, how disease work. So we often try to boil it down to, okay, what is going right in case of, you know, our normal biological function and what is going wrong in case of the disease state. And we boil it down to kind of, you know, proteins and kind of other molecules and their interaction. And so when we try predicting the structure of proteins, it's critical to, you know, have an understanding of kind of those interactions. It's a bit like seeing the difference between... Having kind of a list of parts that you would put it in a car and seeing kind of the car in its final form, you know, seeing the car really helps you understand what it does. On the other hand, kind of going to your question of, you know, why do we care about, you know, how the protein falls or, you know, how the car is made to some extent is that, you know, sometimes when something goes wrong, you know, there are, you know, cases of, you know, proteins misfolding. In some diseases and so on, if we don't understand this folding process, we don't really know how to intervene.RJ [00:11:30]: There's this nice line in the, I think it's in the Alpha Fold 2 manuscript, where they sort of discuss also like why we even hopeful that we can target the problem in the first place. And then there's this notion that like, well, four proteins that fold. The folding process is almost instantaneous, which is a strong, like, you know, signal that like, yeah, like we should, we might be... able to predict that this very like constrained thing that, that the protein does so quickly. And of course that's not the case for, you know, for, for all proteins. And there's a lot of like really interesting mechanisms in the cells, but yeah, I remember reading that and thought, yeah, that's somewhat of an insightful point.Gabriel [00:12:10]: I think one of the interesting things about the protein folding problem is that it used to be actually studied. And part of the reason why people thought it was impossible, it used to be studied as kind of like a classical example. Of like an MP problem. Uh, like there are so many different, you know, type of, you know, shapes that, you know, this amino acid could take. And so, this grows combinatorially with the size of the sequence. And so there used to be kind of a lot of actually kind of more theoretical computer science thinking about and studying protein folding as an MP problem. And so it was very surprising also from that perspective, kind of seeing. Machine learning so clear, there is some, you know, signal in those sequences, through evolution, but also through kind of other things that, you know, us as humans, we're probably not really able to, uh, to understand, but that is, models I've, I've learned.Brandon [00:13:07]: And so Andrew White, we were talking to him a few weeks ago and he said that he was following the development of this and that there were actually ASICs that were developed just to solve this problem. So, again, that there were. There were many, many, many millions of computational hours spent trying to solve this problem before AlphaFold. And just to be clear, one thing that you mentioned was that there's this kind of co-evolution of mutations and that you see this again and again in different species. So explain why does that give us a good hint that they're close by to each other? Yeah.RJ [00:13:41]: Um, like think of it this way that, you know, if I have, you know, some amino acid that mutates, it's going to impact everything around it. Right. In three dimensions. And so it's almost like the protein through several, probably random mutations and evolution, like, you know, ends up sort of figuring out that this other amino acid needs to change as well for the structure to be conserved. Uh, so this whole principle is that the structure is probably largely conserved, you know, because there's this function associated with it. And so it's really sort of like different positions compensating for, for each other. I see.Brandon [00:14:17]: Those hints in aggregate give us a lot. Yeah. So you can start to look at what kinds of information about what is close to each other, and then you can start to look at what kinds of folds are possible given the structure and then what is the end state.RJ [00:14:30]: And therefore you can make a lot of inferences about what the actual total shape is. Yeah, that's right. It's almost like, you know, you have this big, like three dimensional Valley, you know, where you're sort of trying to find like these like low energy states and there's so much to search through. That's almost overwhelming. But these hints, they sort of maybe put you in. An area of the space that's already like, kind of close to the solution, maybe not quite there yet. And, and there's always this question of like, how much physics are these models learning, you know, versus like, just pure like statistics. And like, I think one of the thing, at least I believe is that once you're in that sort of approximate area of the solution space, then the models have like some understanding, you know, of how to get you to like, you know, the lower energy, uh, low energy state. And so maybe you have some, some light understanding. Of physics, but maybe not quite enough, you know, to know how to like navigate the whole space. Right. Okay.Brandon [00:15:25]: So we need to give it these hints to kind of get into the right Valley and then it finds the, the minimum or something. Yeah.Gabriel [00:15:31]: One interesting explanation about our awful free works that I think it's quite insightful, of course, doesn't cover kind of the entirety of, of what awful does that is, um, they're going to borrow from, uh, Sergio Chinico for MIT. So he sees kind of awful. Then the interesting thing about awful is God. This very peculiar architecture that we have seen, you know, used, and this architecture operates on this, you know, pairwise context between amino acids. And so the idea is that probably the MSA gives you this first hint about what potential amino acids are close to each other. MSA is most multiple sequence alignment. Exactly. Yeah. Exactly. This evolutionary information. Yeah. And, you know, from this evolutionary information about potential contacts, then is almost as if the model is. of running some kind of, you know, diastro algorithm where it's sort of decoding, okay, these have to be closed. Okay. Then if these are closed and this is connected to this, then this has to be somewhat closed. And so you decode this, that becomes basically a pairwise kind of distance matrix. And then from this rough pairwise distance matrix, you decode kind of theBrandon [00:16:42]: actual potential structure. Interesting. So there's kind of two different things going on in the kind of coarse grain and then the fine grain optimizations. Interesting. Yeah. Very cool.Gabriel [00:16:53]: Yeah. You mentioned AlphaFold3. So maybe we have a good time to move on to that. So yeah, AlphaFold2 came out and it was like, I think fairly groundbreaking for this field. Everyone got very excited. A few years later, AlphaFold3 came out and maybe for some more history, like what were the advancements in AlphaFold3? And then I think maybe we'll, after that, we'll talk a bit about the sort of how it connects to Bolt. But anyway. Yeah. So after AlphaFold2 came out, you know, Jeremy and I got into the field and with many others, you know, the clear problem that, you know, was, you know, obvious after that was, okay, now we can do individual chains. Can we do interactions, interaction, different proteins, proteins with small molecules, proteins with other molecules. And so. So why are interactions important? Interactions are important because to some extent that's kind of the way that, you know, these machines, you know, these proteins have a function, you know, the function comes by the way that they interact with other proteins and other molecules. Actually, in the first place, you know, the individual machines are often, as Jeremy was mentioning, not made of a single chain, but they're made of the multiple chains. And then these multiple chains interact with other molecules to give the function to those. And on the other hand, you know, when we try to intervene of these interactions, think about like a disease, think about like a, a biosensor or many other ways we are trying to design the molecules or proteins that interact in a particular way with what we would call a target protein or target. You know, this problem after AlphaVol2, you know, became clear, kind of one of the biggest problems in the field to, to solve many groups, including kind of ours and others, you know, started making some kind of contributions to this problem of trying to model these interactions. And AlphaVol3 was, you know, was a significant advancement on the problem of modeling interactions. And one of the interesting thing that they were able to do while, you know, some of the rest of the field that really tried to try to model different interactions separately, you know, how protein interacts with small molecules, how protein interacts with other proteins, how RNA or DNA have their structure, they put everything together and, you know, train very large models with a lot of advances, including kind of changing kind of systems. Some of the key architectural choices and managed to get a single model that was able to set this new state-of-the-art performance across all of these different kind of modalities, whether that was protein, small molecules is critical to developing kind of new drugs, protein, protein, understanding, you know, interactions of, you know, proteins with RNA and DNAs and so on.Brandon [00:19:39]: Just to satisfy the AI engineers in the audience, what were some of the key architectural and data, data changes that made that possible?Gabriel [00:19:48]: Yeah, so one critical one that was not necessarily just unique to AlphaFold3, but there were actually a few other teams, including ours in the field that proposed this, was moving from, you know, modeling structure prediction as a regression problem. So where there is a single answer and you're trying to shoot for that answer to a generative modeling problem where you have a posterior distribution of possible structures and you're trying to sample this distribution. And this achieves two things. One is it starts to allow us to try to model more dynamic systems. As we said, you know, some of these structures can actually take multiple structures. And so, you know, you can now model that, you know, through kind of modeling the entire distribution. But on the second hand, from more kind of core modeling questions, when you move from a regression problem to a generative modeling problem, you are really tackling the way that you think about uncertainty in the model in a different way. So if you think about, you know, I'm undecided between different answers, what's going to happen in a regression model is that, you know, I'm going to try to make an average of those different kind of answers that I had in mind. When you have a generative model, what you're going to do is, you know, sample all these different answers and then maybe use separate models to analyze those different answers and pick out the best. So that was kind of one of the critical improvement. The other improvement is that they significantly simplified, to some extent, the architecture, especially of the final model that takes kind of those pairwise representations and turns them into an actual structure. And that now looks a lot more like a more traditional transformer than, you know, like a very specialized equivariant architecture that it was in AlphaFold3.Brandon [00:21:41]: So this is a bitter lesson, a little bit.Gabriel [00:21:45]: There is some aspect of a bitter lesson, but the interesting thing is that it's very far from, you know, being like a simple transformer. This field is one of the, I argue, very few fields in applied machine learning where we still have kind of architecture that are very specialized. And, you know, there are many people that have tried to replace these architectures with, you know, simple transformers. And, you know, there is a lot of debate in the field, but I think kind of that most of the consensus is that, you know, the performance... that we get from the specialized architecture is vastly superior than what we get through a single transformer. Another interesting thing that I think on the staying on the modeling machine learning side, which I think it's somewhat counterintuitive seeing some of the other kind of fields and applications is that scaling hasn't really worked kind of the same in this field. Now, you know, models like AlphaFold2 and AlphaFold3 are, you know, still very large models.RJ [00:29:14]: in a place, I think, where we had, you know, some experience working in, you know, with the data and working with this type of models. And I think that put us already in like a good place to, you know, to produce it quickly. And, you know, and I would even say, like, I think we could have done it quicker. The problem was like, for a while, we didn't really have the compute. And so we couldn't really train the model. And actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so like, while the model was training, we were like, finding bugs left and right. A lot of them that I wrote. And like, I remember like, I was like, sort of like, you know, doing like, surgery in the middle, like stopping the run, making the fix, like relaunching. And yeah, we never actually went back to the start. We just like kept training it with like the bug fixes along the way, which was impossible to reproduce now. Yeah, yeah, no, that model is like, has gone through such a curriculum that, you know, learned some weird stuff. But yeah, somehow by miracle, it worked out.Gabriel [00:30:13]: The other funny thing is that the way that we were training, most of that model was through a cluster from the Department of Energy. But that's sort of like a shared cluster that many groups use. And so we were basically training the model for two days, and then it would go back to the queue and stay a week in the queue. Oh, yeah. And so it was pretty painful. And so we actually kind of towards the end with Evan, the CEO of Genesis, and basically, you know, I was telling him a bit about the project and, you know, kind of telling him about this frustration with the compute. And so luckily, you know, he offered to kind of help. And so we, we got the help from Genesis to, you know, finish up the model. Otherwise, it probably would have taken a couple of extra weeks.Brandon [00:30:57]: Yeah, yeah.Brandon [00:31:02]: And then, and then there's some progression from there.Gabriel [00:31:06]: Yeah, so I would say kind of that, both one, but also kind of these other kind of set of models that came around the same time, were kind of approaching were a big leap from, you know, kind of the previous kind of open source models, and, you know, kind of really kind of approaching the level of AlphaVault 3. But I would still say that, you know, even to this day, there are, you know, some... specific instances where AlphaVault 3 works better. I think one common example is antibody antigen prediction, where, you know, AlphaVault 3 still seems to have an edge in many situations. Obviously, these are somewhat different models. They are, you know, you run them, you obtain different results. So it's, it's not always the case that one model is better than the other, but kind of in aggregate, we still, especially at the time.Brandon [00:32:00]: So AlphaVault 3 is, you know, still having a bit of an edge. We should talk about this more when we talk about Boltzgen, but like, how do you know one is, one model is better than the other? Like you, so you, I make a prediction, you make a prediction, like, how do you know?Gabriel [00:32:11]: Yeah, so easily, you know, the, the great thing about kind of structural prediction and, you know, once we're going to go into the design space of designing new small molecule, new proteins, this becomes a lot more complex. But a great thing about structural prediction is that a bit like, you know, CASP was doing, basically the way that you can evaluate them is that, you know, you train... You know, you train a model on a structure that was, you know, released across the field up until a certain time. And, you know, one of the things that we didn't talk about that was really critical in all this development is the PDB, which is the Protein Data Bank. It's this common resources, basically common database where every biologist publishes their structures. And so we can, you know, train on, you know, all the structures that were put in the PDB until a certain date. And then... And then we basically look for recent structures, okay, which structures look pretty different from anything that was published before, because we really want to try to understand generalization.Brandon [00:33:13]: And then on this new structure, we evaluate all these different models. And so you just know when AlphaFold3 was trained, you know, when you're, you intentionally trained to the same date or something like that. Exactly. Right. Yeah.Gabriel [00:33:24]: And so this is kind of the way that you can somewhat easily kind of compare these models, obviously, that assumes that, you know, the training. You've always been very passionate about validation. I remember like DiffDoc, and then there was like DiffDocL and DocGen. You've thought very carefully about this in the past. Like, actually, I think DocGen is like a really funny story that I think, I don't know if you want to talk about that. It's an interesting like... Yeah, I think one of the amazing things about putting things open source is that we get a ton of feedback from the field. And, you know, sometimes we get kind of great feedback of people. Really like... But honestly, most of the times, you know, to be honest, that's also maybe the most useful feedback is, you know, people sharing about where it doesn't work. And so, you know, at the end of the day, it's critical. And this is also something, you know, across other fields of machine learning. It's always critical to set, to do progress in machine learning, set clear benchmarks. And as, you know, you start doing progress of certain benchmarks, then, you know, you need to improve the benchmarks and make them harder and harder. And this is kind of the progression of, you know, how the field operates. And so, you know, the example of DocGen was, you know, we published this initial model called DiffDoc in my first year of PhD, which was sort of like, you know, one of the early models to try to predict kind of interactions between proteins, small molecules, that we bought a year after AlphaFold2 was published. And now, on the one hand, you know, on these benchmarks that we were using at the time, DiffDoc was doing really well, kind of, you know, outperforming kind of some of the traditional physics-based methods. But on the other hand, you know, when we started, you know, kind of giving these tools to kind of many biologists, and one example was that we collaborated with was the group of Nick Polizzi at Harvard. We noticed, started noticing that there was this clear, pattern where four proteins that were very different from the ones that we're trained on, the models was, was struggling. And so, you know, that seemed clear that, you know, this is probably kind of where we should, you know, put our focus on. And so we first developed, you know, with Nick and his group, a new benchmark, and then, you know, went after and said, okay, what can we change? And kind of about the current architecture to improve this pattern and generalization. And this is the same that, you know, we're still doing today, you know, kind of, where does the model not work, you know, and then, you know, once we have that benchmark, you know, let's try to, through everything we, any ideas that we have of the problem.RJ [00:36:15]: And there's a lot of like healthy skepticism in the field, which I think, you know, is, is, is great. And I think, you know, it's very clear that there's a ton of things, the models don't really work well on, but I think one thing that's probably, you know, undeniable is just like the pace of, pace of progress, you know, and how, how much better we're getting, you know, every year. And so I think if you, you know, if you assume, you know, any constant, you know, rate of progress moving forward, I think things are going to look pretty cool at some point in the future.Gabriel [00:36:42]: ChatGPT was only three years ago. Yeah, I mean, it's wild, right?RJ [00:36:45]: Like, yeah, yeah, yeah, it's one of those things. Like, you've been doing this. Being in the field, you don't see it coming, you know? And like, I think, yeah, hopefully we'll, you know, we'll, we'll continue to have as much progress we've had the past few years.Brandon [00:36:55]: So this is maybe an aside, but I'm really curious, you get this great feedback from the, from the community, right? By being open source. My question is partly like, okay, yeah, if you open source and everyone can copy what you did, but it's also maybe balancing priorities, right? Where you, like all my customers are saying. I want this, there's all these problems with the model. Yeah, yeah. But my customers don't care, right? So like, how do you, how do you think about that? Yeah.Gabriel [00:37:26]: So I would say a couple of things. One is, you know, part of our goal with Bolts and, you know, this is also kind of established as kind of the mission of the public benefit company that we started is to democratize the access to these tools. But one of the reasons why we realized that Bolts needed to be a company, it couldn't just be an academic project is that putting a model on GitHub is definitely not enough to get, you know, chemists and biologists, you know, across, you know, both academia, biotech and pharma to use your model to, in their therapeutic programs. And so a lot of what we think about, you know, at Bolts beyond kind of the, just the models is thinking about all the layers. The layers that come on top of the models to get, you know, from, you know, those models to something that can really enable scientists in the industry. And so that goes, you know, into building kind of the right kind of workflows that take in kind of, for example, the data and try to answer kind of directly that those problems that, you know, the chemists and the biologists are asking, and then also kind of building the infrastructure. And so this to say that, you know, even with models fully open. You know, we see a ton of potential for, you know, products in the space and the critical part about a product is that even, you know, for example, with an open source model, you know, running the model is not free, you know, as we were saying, these are pretty expensive model and especially, and maybe we'll get into this, you know, these days we're seeing kind of pretty dramatic inference time scaling of these models where, you know, the more you run them, the better the results are. But there, you know, you see. You start getting into a point that compute and compute costs becomes a critical factor. And so putting a lot of work into building the right kind of infrastructure, building the optimizations and so on really allows us to provide, you know, a much better service potentially to the open source models. That to say, you know, even though, you know, with a product, we can provide a much better service. I do still think, and we will continue to put a lot of our models open source because the critical kind of role. I think of open source. Models is, you know, helping kind of the community progress on the research and, you know, from which we, we all benefit. And so, you know, we'll continue to on the one hand, you know, put some of our kind of base models open source so that the field can, can be on top of it. And, you know, as we discussed earlier, we learn a ton from, you know, the way that the field uses and builds on top of our models, but then, you know, try to build a product that gives the best experience possible to scientists. So that, you know, like a chemist or a biologist doesn't need to, you know, spin off a GPU and, you know, set up, you know, our open source model in a particular way, but can just, you know, a bit like, you know, I, even though I am a computer scientist, machine learning scientist, I don't necessarily, you know, take a open source LLM and try to kind of spin it off. But, you know, I just maybe open a GPT app or a cloud code and just use it as an amazing product. We kind of want to give the same experience. So this front world.Brandon [00:40:40]: I heard a good analogy yesterday that a surgeon doesn't want the hospital to design a scalpel, right?Brandon [00:40:48]: So just buy the scalpel.RJ [00:40:50]: You wouldn't believe like the number of people, even like in my short time, you know, between AlphaFold3 coming out and the end of the PhD, like the number of people that would like reach out just for like us to like run AlphaFold3 for them, you know, or things like that. Just because like, you know, bolts in our case, you know, just because it's like. It's like not that easy, you know, to do that, you know, if you're not a computational person. And I think like part of the goal here is also that, you know, we continue to obviously build the interface with computational folks, but that, you know, the models are also accessible to like a larger, broader audience. And then that comes from like, you know, good interfaces and stuff like that.Gabriel [00:41:27]: I think one like really interesting thing about bolts is that with the release of it, you didn't just release a model, but you created a community. Yeah. Did that community, it grew very quickly. Did that surprise you? And like, what is the evolution of that community and how is that fed into bolts?RJ [00:41:43]: If you look at its growth, it's like very much like when we release a new model, it's like, there's a big, big jump, but yeah, it's, I mean, it's been great. You know, we have a Slack community that has like thousands of people on it. And it's actually like self-sustaining now, which is like the really nice part because, you know, it's, it's almost overwhelming, I think, you know, to be able to like answer everyone's questions and help. It's really difficult, you know. The, the few people that we were, but it ended up that like, you know, people would answer each other's questions and like, sort of like, you know, help one another. And so the Slack, you know, has been like kind of, yeah, self, self-sustaining and that's been, it's been really cool to see.RJ [00:42:21]: And, you know, that's, that's for like the Slack part, but then also obviously on GitHub as well. We've had like a nice, nice community. You know, I think we also aspire to be even more active on it, you know, than we've been in the past six months, which has been like a bit challenging, you know, for us. But. Yeah, the community has been, has been really great and, you know, there's a lot of papers also that have come out with like new evolutions on top of bolts and it's surprised us to some degree because like there's a lot of models out there. And I think like, you know, sort of people converging on that was, was really cool. And, you know, I think it speaks also, I think, to the importance of like, you know, when, when you put code out, like to try to put a lot of emphasis and like making it like as easy to use as possible and something we thought a lot about when we released the code base. You know, it's far from perfect, but, you know.Brandon [00:43:07]: Do you think that that was one of the factors that caused your community to grow is just the focus on easy to use, make it accessible? I think so.RJ [00:43:14]: Yeah. And we've, we've heard it from a few people over the, over the, over the years now. And, you know, and some people still think it should be a lot nicer and they're, and they're right. And they're right. But yeah, I think it was, you know, at the time, maybe a little bit easier than, than other things.Gabriel [00:43:29]: The other thing part, I think led to, to the community and to some extent, I think, you know, like the somewhat the trust in the community. Kind of what we, what we put out is the fact that, you know, it's not really been kind of, you know, one model, but, and maybe we'll talk about it, you know, after Boltz 1, you know, there were maybe another couple of models kind of released, you know, or open source kind of soon after. We kind of continued kind of that open source journey or at least Boltz 2, where we are not only improving kind of structure prediction, but also starting to do affinity predictions, understanding kind of the strength of the interactions between these different models, which is this critical component. critical property that you often want to optimize in discovery programs. And then, you know, more recently also kind of protein design model. And so we've sort of been building this suite of, of models that come together, interact with one another, where, you know, kind of, there is almost an expectation that, you know, we, we take very at heart of, you know, always having kind of, you know, across kind of the entire suite of different tasks, the best or across the best. model out there so that it's sort of like our open source tool can be kind of the go-to model for everybody in the, in the industry. I really want to talk about Boltz 2, but before that, one last question in this direction, was there anything about the community which surprised you? Were there any, like, someone was doing something and you're like, why would you do that? That's crazy. Or that's actually genius. And I never would have thought about that.RJ [00:45:01]: I mean, we've had many contributions. I think like some of the. Interesting ones, like, I mean, we had, you know, this one individual who like wrote like a complex GPU kernel, you know, for part of the architecture on a piece of, the funny thing is like that piece of the architecture had been there since AlphaFold 2, and I don't know why it took Boltz for this, you know, for this person to, you know, to decide to do it, but that was like a really great contribution. We've had a bunch of others, like, you know, people figuring out like ways to, you know, hack the model to do something. They click peptides, like, you know, there's, I don't know if there's any other interesting ones come to mind.Gabriel [00:45:41]: One cool one, and this was, you know, something that initially was proposed as, you know, as a message in the Slack channel by Tim O'Donnell was basically, he was, you know, there are some cases, especially, for example, we discussed, you know, antibody-antigen interactions where the models don't necessarily kind of get the right answer. What he noticed is that, you know, the models were somewhat stuck into predicting kind of the antibodies. And so he basically ran the experiments in this model, you can condition, basically, you can give hints. And so he basically gave, you know, random hints to the model, basically, okay, you should bind to this residue, you should bind to the first residue, or you should bind to the 11th residue, or you should bind to the 21st residue, you know, basically every 10 residues scanning the entire antigen.Brandon [00:46:33]: Residues are the...Gabriel [00:46:34]: The amino acids. The amino acids, yeah. So the first amino acids. The 11 amino acids, and so on. So it's sort of like doing a scan, and then, you know, conditioning the model to predict all of them, and then looking at the confidence of the model in each of those cases and taking the top. And so it's sort of like a very somewhat crude way of doing kind of inference time search. But surprisingly, you know, for antibody-antigen prediction, it actually kind of helped quite a bit. And so there's some, you know, interesting ideas that, you know, obviously, as kind of developing the model, you say kind of, you know, wow. This is why would the model, you know, be so dumb. But, you know, it's very interesting. And that, you know, leads you to also kind of, you know, start thinking about, okay, how do I, can I do this, you know, not with this brute force, but, you know, in a smarter way.RJ [00:47:22]: And so we've also done a lot of work on that direction. And that speaks to, like, the, you know, the power of scoring. We're seeing that a lot. I'm sure we'll talk about it more when we talk about BullsGen. But, you know, our ability to, like, take a structure and determine that that structure is, like... Good. You know, like, somewhat accurate. Whether that's a single chain or, like, an interaction is a really powerful way of improving, you know, the models. Like, sort of like, you know, if you can sample a ton and you assume that, like, you know, if you sample enough, you're likely to have, like, you know, the good structure. Then it really just becomes a ranking problem. And, you know, now we're, you know, part of the inference time scaling that Gabby was talking about is very much that. It's like, you know, the more we sample, the more we, like, you know, the ranking model. The ranking model ends up finding something it really likes. And so I think our ability to get better at ranking, I think, is also what's going to enable sort of the next, you know, next big, big breakthroughs. Interesting.Brandon [00:48:17]: But I guess there's a, my understanding, there's a diffusion model and you generate some stuff and then you, I guess, it's just what you said, right? Then you rank it using a score and then you finally... And so, like, can you talk about those different parts? Yeah.Gabriel [00:48:34]: So, first of all, like, the... One of the critical kind of, you know, beliefs that we had, you know, also when we started working on Boltz 1 was sort of like the structure prediction models are somewhat, you know, our field version of some foundation models, you know, learning about kind of how proteins and other molecules interact. And then we can leverage that learning to do all sorts of other things. And so with Boltz 2, we leverage that learning to do affinity predictions. So understanding kind of, you know, if I give you this protein, this molecule. How tightly is that interaction? For Boltz 1, what we did was taking kind of that kind of foundation models and then fine tune it to predict kind of entire new proteins. And so the way basically that that works is sort of like instead of for the protein that you're designing, instead of fitting in an actual sequence, you fit in a set of blank tokens. And you train the models to, you know, predict both the structure of kind of that protein. The structure also, what the different amino acids of that proteins are. And so basically the way that Boltz 1 operates is that you feed a target protein that you may want to kind of bind to or, you know, another DNA, RNA. And then you feed the high level kind of design specification of, you know, what you want your new protein to be. For example, it could be like an antibody with a particular framework. It could be a peptide. It could be many other things. And that's with natural language or? And that's, you know, basically, you know, prompting. And we have kind of this sort of like spec that you specify. And, you know, you feed kind of this spec to the model. And then the model translates this into, you know, a set of, you know, tokens, a set of conditioning to the model, a set of, you know, blank tokens. And then, you know, basically the codes as part of the diffusion models, the codes. It's a new structure and a new sequence for your protein. And, you know, basically, then we take that. And as Jeremy was saying, we are trying to score it and, you know, how good of a binder it is to that original target.Brandon [00:50:51]: You're using basically Boltz to predict the folding and the affinity to that molecule. So and then that kind of gives you a score? Exactly.Gabriel [00:51:03]: So you use this model to predict the folding. And then you do two things. One is that you predict the structure and with something like Boltz2, and then you basically compare that structure with what the model predicted, what Boltz2 predicted. And this is sort of like in the field called consistency. It's basically you want to make sure that, you know, the structure that you're predicting is actually what you're trying to design. And that gives you a much better confidence that, you know, that's a good design. And so that's the first filtering. And the second filtering that we did as part of kind of the Boltz2 pipeline that was released is that we look at the confidence that the model has in the structure. Now, unfortunately, kind of going to your question of, you know, predicting affinity, unfortunately, confidence is not a very good predictor of affinity. And so one of the things that we've actually done a ton of progress, you know, since we released Boltz2.Brandon [00:52:03]: And kind of we have some new results that we are going to kind of announce soon is kind of, you know, the ability to get much better hit rates when instead of, you know, trying to rely on confidence of the model, we are actually directly trying to predict the affinity of that interaction. Okay. Just backing up a minute. So your diffusion model actually predicts not only the protein sequence, but also the folding of it. Exactly.Gabriel [00:52:32]: And actually, you can... One of the big different things that we did compared to other models in the space, and, you know, there were some papers that had already kind of done this before, but we really scaled it up was, you know, basically somewhat merging kind of the structure prediction and the sequence prediction into almost the same task. And so the way that Boltz2 works is that you are basically the only thing that you're doing is predicting the structure. So the only sort of... Supervision is we give you a supervision on the structure, but because the structure is atomic and, you know, the different amino acids have a different atomic composition, basically from the way that you place the atoms, we also understand not only kind of the structure that you wanted, but also the identity of the amino acid that, you know, the models believed was there. And so we've basically, instead of, you know, having these two supervision signals, you know, one discrete, one continuous. That somewhat, you know, don't interact well together. We sort of like build kind of like an encoding of, you know, sequences in structures that allows us to basically use exactly the same supervision signal that we were using to Boltz2 that, you know, you know, largely similar to what AlphaVol3 proposed, which is very scalable. And we can use that to design new proteins. Oh, interesting.RJ [00:53:58]: Maybe a quick shout out to Hannes Stark on our team who like did all this work. Yeah.Gabriel [00:54:04]: Yeah, that was a really cool idea. I mean, like looking at the paper and there's this is like encoding or you just add a bunch of, I guess, kind of atoms, which can be anything, and then they get sort of rearranged and then basically plopped on top of each other so that and then that encodes what the amino acid is. And there's sort of like a unique way of doing this. It was that was like such a really such a cool, fun idea.RJ [00:54:29]: I think that idea was had existed before. Yeah, there were a couple of papers.Gabriel [00:54:33]: Yeah, I had proposed this and and Hannes really took it to the large scale.Brandon [00:54:39]: In the paper, a lot of the paper for Boltz2Gen is dedicated to actually the validation of the model. In my opinion, all the people we basically talk about feel that this sort of like in the wet lab or whatever the appropriate, you know, sort of like in real world validation is the whole problem or not the whole problem, but a big giant part of the problem. So can you talk a little bit about the highlights? From there, that really because to me, the results are impressive, both from the perspective of the, you know, the model and also just the effort that went into the validation by a large team.Gabriel [00:55:18]: First of all, I think I should start saying is that both when we were at MIT and Thomas Yacolas and Regina Barzillai's lab, as well as at Boltz, you know, we are not a we're not a biolab and, you know, we are not a therapeutic company. And so to some extent, you know, we were first forced to, you know, look outside of, you know, our group, our team to do the experimental validation. One of the things that really, Hannes, in the team pioneer was the idea, OK, can we go not only to, you know, maybe a specific group and, you know, trying to find a specific system and, you know, maybe overfit a bit to that system and trying to validate. But how can we test this model? So. Across a very wide variety of different settings so that, you know, anyone in the field and, you know, printing design is, you know, such a kind of wide task with all sorts of different applications from therapeutic to, you know, biosensors and many others that, you know, so can we get a validation that is kind of goes across many different tasks? And so he basically put together, you know, I think it was something like, you know, 25 different. You know, academic and industry labs that committed to, you know, testing some of the designs from the model and some of this testing is still ongoing and, you know, giving results kind of back to us in exchange for, you know, hopefully getting some, you know, new great sequences for their task. And he was able to, you know, coordinate this, you know, very wide set of, you know, scientists and already in the paper, I think we. Shared results from, I think, eight to 10 different labs kind of showing results from, you know, designing peptides, designing to target, you know, ordered proteins, peptides targeting disordered proteins, which are results, you know, of designing proteins that bind to small molecules, which are results of, you know, designing nanobodies and across a wide variety of different targets. And so that's sort of like. That gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of wide.Brandon [00:57:39]: And so those would be therapeutics for those animals or are they relevant to humans as well? They're relevant to humans as well.Gabriel [00:57:45]: Obviously, you need to do some work into, quote unquote, humanizing them, making sure that, you know, they have the right characteristics to so they're not toxic to humans and so on.RJ [00:57:57]: There are some approved medicine in the market that are nanobodies. There's a general. General pattern, I think, in like in trying to design things that are smaller, you know, like it's easier to manufacture at the same time, like that comes with like potentially other challenges, like maybe a little bit less selectivity than like if you have something that has like more hands, you know, but the yeah, there's this big desire to, you know, try to design many proteins, nanobodies, small peptides, you know, that just are just great drug modalities.Brandon [00:58:27]: Okay. I think we were left off. We were talking about validation. Validation in the lab. And I was very excited about seeing like all the diverse validations that you've done. Can you go into some more detail about them? Yeah. Specific ones. Yeah.RJ [00:58:43]: The nanobody one. I think we did. What was it? 15 targets. Is that correct? 14. 14 targets. Testing. So we typically the way this works is like we make a lot of designs. All right. On the order of like tens of thousands. And then we like rank them and we pick like the top. And in this case, and was 15 right for each target and then we like measure sort of like the success rates, both like how many targets we were able to get a binder for and then also like more generally, like out of all of the binders that we designed, how many actually proved to be good binders. Some of the other ones I think involved like, yeah, like we had a cool one where there was a small molecule or design a protein that binds to it. That has a lot of like interesting applications, you know, for example. Like Gabri mentioned, like biosensing and things like that, which is pretty cool. We had a disordered protein, I think you mentioned also. And yeah, I think some of those were some of the highlights. Yeah.Gabriel [00:59:44]: So I would say that the way that we structure kind of some of those validations was on the one end, we have validations across a whole set of different problems that, you know, the biologists that we were working with came to us with. So we were trying to. For example, in some of the experiments, design peptides that would target the RACC, which is a target that is involved in metabolism. And we had, you know, a number of other applications where we were trying to design, you know, peptides or other modalities against some other therapeutic relevant targets. We designed some proteins to bind small molecules. And then some of the other testing that we did was really trying to get like a more broader sense. So how does the model work, especially when tested, you know, on somewhat generalization? So one of the things that, you know, we found with the field was that a lot of the validation, especially outside of the validation that was on specific problems, was done on targets that have a lot of, you know, known interactions in the training data. And so it's always a bit hard to understand, you know, how much are these models really just regurgitating kind of what they've seen or trying to imitate. What they've seen in the training data versus, you know, really be able to design new proteins. And so one of the experiments that we did was to take nine targets from the PDB, filtering to things where there is no known interaction in the PDB. So basically the model has never seen kind of this particular protein bound or a similar protein bound to another protein. So there is no way that. The model from its training set can sort of like say, okay, I'm just going to kind of tweak something and just imitate this particular kind of interaction. And so we took those nine proteins. We worked with adaptive CRO and basically tested, you know, 15 mini proteins and 15 nanobodies against each one of them. And the very cool thing that we saw was that on two thirds of those targets, we were able to, from this 15 design, get nanomolar binders, nanomolar, roughly speaking, just a measure of, you know, how strongly kind of the interaction is, roughly speaking, kind of like a nanomolar binder is approximately the kind of binding strength or binding that you need for a therapeutic. Yeah. So maybe switching directions a bit. Bolt's lab was just announced this week or was it last week? Yeah. This is like your. First, I guess, product, if that's if you want to call it that. Can you talk about what Bolt's lab is and yeah, you know, what you hope that people take away from this? Yeah.RJ [01:02:44]: You know, as we mentioned, like I think at the very beginning is the goal with the product has been to, you know, address what the models don't on their own. And there's largely sort of two categories there. I'll split it in three. The first one. It's one thing to predict, you know, a single interaction, for example, like a single structure. It's another to like, you know, very effectively search a space, a design space to produce something of value. What we found, like sort of building on this product is that there's a lot of steps involved, you know, in that there's certainly need to like, you know, accompany the user through, you know, one of those steps, for example, is like, you know, the creation of the target itself. You know, how do we make sure that the model has like a good enough understanding of the target? So we can like design something and there's all sorts of tricks, you know, that you can do to improve like a particular, you know, structure prediction. And so that's sort of like, you know, the first stage. And then there's like this stage of like, you know, designing and searching the space efficiently. You know, for something like BullsGen, for example, like you, you know, you design many things and then you rank them, for example, for small molecule process, a little bit more complicated. We actually need to also make sure that the molecules are synthesizable. And so the way we do that is that, you know, we have a generative model that learns. To use like appropriate building blocks such that, you know, it can design within a space that we know is like synthesizable. And so there's like, you know, this whole pipeline really of different models involved in being able to design a molecule. And so that's been sort of like the first thing we call them agents. We have a protein agent and we have a small molecule design agents. And that's really like at the core of like what powers, you know, the BullsLab platform.Brandon [01:04:22]: So these agents, are they like a language model wrapper or they're just like your models and you're just calling them agents? A lot. Yeah. Because they, they, they sort of perform a function on behalf of.RJ [01:04:33]: They're more of like a, you know, a recipe, if you wish. And I think we use that term sort of because of, you know, sort of the complex pipelining and automation, you know, that goes into like all this plumbing. So that's the first part of the product. The second part is the infrastructure. You know, we need to be able to do this at very large scale for any one, you know, group that's doing a design campaign. Let's say you're designing, you know, I'd say a hundred thousand possible candidates. Right. To find the good one that is, you know, a very large amount of compute, you know, for small molecules, it's on the order of like a few seconds per designs for proteins can be a bit longer. And so, you know, ideally you want to do that in parallel, otherwise it's going to take you weeks. And so, you know, we've put a lot of effort into like, you know, our ability to have a GPU fleet that allows any one user, you know, to be able to do this kind of like large parallel search.Brandon [01:05:23]: So you're amortizing the cost over your users. Exactly. Exactly.RJ [01:05:27]: And, you know, to some degree, like it's whether you. Use 10,000 GPUs for like, you know, a minute is the same cost as using, you know, one GPUs for God knows how long. Right. So you might as well try to parallelize if you can. So, you know, a lot of work has gone, has gone into that, making it very robust, you know, so that we can have like a lot of people on the platform doing that at the same time. And the third one is, is the interface and the interface comes in, in two shapes. One is in form of an API and that's, you know, really suited for companies that want to integrate, you know, these pipelines, these agents.RJ [01:06:01]: So we're already partnering with, you know, a few distributors, you know, that are gonna integrate our API. And then the second part is the user interface. And, you know, we, we've put a lot of thoughts also into that. And this is when I, I mentioned earlier, you know, this idea of like broadening the audience. That's kind of what the, the user interface is about. And we've built a lot of interesting features in it, you know, for example, for collaboration, you know, when you have like potentially multiple medicinal chemists or. We're going through the results and trying to pick out, okay, like what are the molecules that we're going to go and test in the lab? It's powerful for them to be able to, you know, for example, each provide their own ranking and then do consensus building. And so there's a lot of features around launching these large jobs, but also around like collaborating on analyzing the results that we try to solve, you know, with that part of the platform. So Bolt's lab is sort of a combination of these three objectives into like one, you know, sort of cohesive platform. Who is this accessible to? Everyone. You do need to request access today. We're still like, you know, sort of ramping up the usage, but anyone can request access. If you are an academic in particular, we, you know, we provide a fair amount of free credit so you can play with the platform. If you are a startup or biotech, you may also, you know, reach out and we'll typically like actually hop on a call just to like understand what you're trying to do and also provide a lot of free credit to get started. And of course, also with larger companies, we can deploy this platform in a more like secure environment. And so that's like more like customizing. You know, deals that we make, you know, with the partners, you know, and that's sort of the ethos of Bolt. I think this idea of like servicing everyone and not necessarily like going after just, you know, the really large enterprises. And that starts from the open source, but it's also, you know, a key design principle of the product itself.Gabriel [01:07:48]: One thing I was thinking about with regards to infrastructure, like in the LLM space, you know, the cost of a token has gone down by I think a factor of a thousand or so over the last three years, right? Yeah. And is it possible that like essentially you can exploit economies of scale and infrastructure that you can make it cheaper to run these things yourself than for any person to roll their own system? A hundred percent. Yeah.RJ [01:08:08]: I mean, we're already there, you know, like running Bolts on our platform, especially on a large screen is like considerably cheaper than it would probably take anyone to put the open source model out there and run it. And on top of the infrastructure, like one of the things that we've been working on is accelerating the models. So, you know. Our small molecule screening pipeline is 10x faster on Bolts Lab than it is in the open source, you know, and that's also part of like, you know, building a product, you know, of something that scales really well. And we really wanted to get to a point where like, you know, we could keep prices very low in a way that it would be a no-brainer, you know, to use Bolts through our platform.Gabriel [01:08:52]: How do you think about validation of your like agentic systems? Because, you know, as you were saying earlier. Like we're AlphaFold style models are really good at, let's say, monomeric, you know, proteins where you have, you know, co-evolution data. But now suddenly the whole point of this is to design something which doesn't have, you know, co-evolution data, something which is really novel. So now you're basically leaving the domain that you thought was, you know, that you know you are good at. So like, how do you validate that?RJ [01:09:22]: Yeah, I like every complete, but there's obviously, you know, a ton of computational metrics. That we rely on, but those are only take you so far. You really got to go to the lab, you know, and test, you know, okay, with this method A and this method B, how much better are we? You know, how much better is my, my hit rate? How stronger are my binders? Also, it's not just about hit rate. It's also about how good the binders are. And there's really like no way, nowhere around that. I think we're, you know, we've really ramped up the amount of experimental validation that we do so that we like really track progress, you know, as scientifically sound, you know. Yeah. As, as possible out of this, I think.Gabriel [01:10:00]: Yeah, no, I think, you know, one thing that is unique about us and maybe companies like us is that because we're not working on like maybe a couple of therapeutic pipelines where, you know, our validation would be focused on those. We, when we do an experimental validation, we try to test it across tens of targets. And so that on the one end, we can get a much more statistically significant result and, and really allows us to make progress. From the methodological side without being, you know, steered by, you know, overfitting on any one particular system. And of course we choose, you know, w

Editor's note: Welcome to our new AI for Science pod, with your new hosts RJ and Brandon! See the writeup on Latent.Space (https://Latent.Space) for more details on why we're launching 2 new pods this year. RJ Honicky is a co-founder and CTO at MiraOmics (https://miraomics.bio/), building AI models and services for single cell, spatial transcriptomics and pathology slide analysis. Brandon Anderson builds AI systems for RNA drug discovery at Atomic AI (https://atomic.ai). Anything said on this podcast is his personal take — not Atomic's.—From building molecular dynamics simulations at the University of Washington to red-teaming GPT-4 for chemistry applications and co-founding Future House (a focused research organization) and Edison Scientific (a venture-backed startup automating science at scale)—Andrew White has spent the last five years living through the full arc of AI's transformation of scientific discovery, from ChemCrow (the first Chemistry LLM agent) triggering White House briefings and three-letter agency meetings, to shipping Kosmos, an end-to-end autonomous research system that generates hypotheses, runs experiments, analyzes data, and updates its world model to accelerate the scientific method itself.* The ChemCrow story: GPT-4 + React + cloud lab automation, released March 2023, set off a storm of anxiety about AI-accelerated bioweapons/chemical weapons, led to a White House briefing (Jake Sullivan presented the paper to the president in a 30-minute block), and meetings with three-letter agencies asking “how does this change breakout time for nuclear weapons research?”* Why scientific taste is the frontier: RLHF on hypotheses didn't work (humans pay attention to tone, actionability, and specific facts, not “if this hypothesis is true/false, how does it change the world?”), so they shifted to end-to-end feedback loops where humans click/download discoveries and that signal rolls up to hypothesis quality* Cosmos: the full scientific agent with a world model (distilled memory system, like a Git repo for scientific knowledge) that iterates on hypotheses via literature search, data analysis, and experiment design—built by Ludo after weeks of failed attempts, the breakthrough was putting data analysis in the loop (literature alone didn't work)* Why molecular dynamics and DFT are overrated: “MD and DFT have consumed an enormous number of PhDs at the altar of beautiful simulation, but they don't model the world correctly—you simulate water at 330 Kelvin to get room temperature, you overfit to validation data with GGA/B3LYP functionals, and real catalysts (grain boundaries, dopants) are too complicated for DFT”* The AlphaFold vs. DE Shaw Research counterfactual: DE Shaw built custom silicon, taped out chips with MD algorithms burned in, ran MD at massive scale in a special room in Times Square, and David Shaw flew in by helicopter to present—Andrew thought protein folding would require special machines to fold one protein per day, then AlphaFold solved it in Google Colab on a desktop GPU* The E3 Zero reward hacking saga: trained a model to generate molecules with specific atom counts (verifiable reward), but it kept exploiting loopholes, then a Nature paper came out that year proving six-nitrogen compounds are possible under extreme conditions, then it started adding nitrogen gas (purchasable, doesn't participate in reactions), then acid-base chemistry to move one atom, and Andrew ended up “building a ridiculous catalog of purchasable compounds in a Bloom filter” to close the loopAndrew White* FutureHouse: http://futurehouse.org/* Edison Scientific: http://edisonscientific.com/* X: https://x.com/andrewwhite01* Cosmos paper: https://futurediscovery.org/cosmosFull Video EpisodeTimestamps00:00:00 Introduction: Andrew White on Automating Science with Future House and Edison Scientific00:02:22 The Academic to Startup Journey: Red Teaming GPT-4 and the ChemCrow Paper00:11:35 Future House Origins: The FRO Model and Mission to Automate Science00:12:32 Resigning Tenure: Why Leave Academia for AI Science00:15:54 What Does ‘Automating Science' Actually Mean?00:17:30 The Lab-in-the-Loop Bottleneck: Why Intelligence Isn't Enough00:18:39 Scientific Taste and Human Preferences: The 52% Agreement Problem00:20:05 Paper QA, Robin, and the Road to Cosmos00:21:57 World Models as Scientific Memory: The GitHub Analogy00:40:20 The Bitter Lesson for Biology: Why Molecular Dynamics and DFT Are Overrated00:43:22 AlphaFold's Shock: When First Principles Lost to Machine Learning00:46:25 Enumeration and Filtration: How AI Scientists Generate Hypotheses00:48:15 CBRN Safety and Dual-Use AI: Lessons from Red Teaming01:00:40 The Future of Chemistry is Language: Multimodal Debate01:08:15 Ether Zero: The Hilarious Reward Hacking Adventures01:10:12 Will Scientists Be Displaced? Jevons Paradox and Infinite Discovery01:13:46 Cosmos in Practice: Open Access and Enterprise Partnerships Get full access to Latent.Space at www.latent.space/subscribe

Can AI compress the years long research time of a PhD into seconds? Research scientist Max Jaderberg explores how “AI analogs” simulate real-world lab work with staggering speed and scale, unlocking new insights on protein folding and drug discovery. Drawing on his experience working on Isomorphic Labs' and Google DeepMind's AlphaFold 3 — an AI model for predicting the structure of molecules — Jaderberg explains how this new technology frees up researchers' time and resources to better understand the real, messy world and tackle the next frontiers of science, medicine and more. Hosted on Acast. See acast.com/privacy for more information.

成為這個頻道的會員並獲得福利：https://www.youtube.com/channel/UCJIPFjZSCWR15_jxBaK2fQQ/join前陣子我在旅行途中看了一部剛出的紀錄片《The Thinking Game》，看完之後只能用「驚為天人」來形容。這部片記錄了 DeepMind 創辦人 Demis Hassabis 追尋通用人工智慧（AGI）的過程，看完當下我就決定：一定要做一集影片好好跟大家聊聊這個人，以及這家改變世界的公司。你很難想像，現在我們熟悉的 AlphaGo、AlphaFold 甚至是 Gemini，其實都源自於一個 13 歲西洋棋神童的頓悟。當年 Demis 在一場長達 10 小時的對弈後，意識到人類大腦如果只用來玩零和遊戲太過浪費。於是他從遊戲開發轉向神經科學，最後創立 DeepMind，並向 Peter Thiel 和 Elon Musk 提出了一個瘋狂的計畫：「我們要打造一個 AI 界的阿波羅計畫，第一步解開智慧，第二步用它解決所有問題。」這集影片不只是紀錄片的補充說明，我整理了 Demis 過去 20 年的長征故事，包括 Google 與 Facebook 當年的搶人大戰內幕、AlphaFold 如何破解困擾科學界 50 年的難題，以及現在 Google DeepMind 如何在逆境中反擊。這不只是一個關於開發軟體或遊戲的故事，更是一段人類試圖解開智慧謎團、破解生命密碼的旅程。希望能透過這集，帶大家看懂這場人類史上最宏大的科學實驗。本集精彩亮點：♟️ 西洋棋神童的頓悟： 為什麼一場 10 小時的平局，讓他決定放棄下棋轉做 AI？

Proteins are crucial for life. They're made of amino acids that “fold” into millions of different shapes. And depending on their structure, they do radically different things in our cells. For a long time, predicting those shapes for research was considered a grand biological challenge.But in 2020, Google's AI lab DeepMind released Alphafold, a tool that was able to accurately predict many of the structures necessary for understanding biological mechanisms in a matter of minutes. In 2024, the Alphafold team was awarded a Nobel Prize in chemistry for the advance.Five years later after its release, Host Ira Flatow checks in on the state of that tech and how it's being used in health research with John Jumper, one of the lead scientists responsible for developing Alphafold.Guest: John Jumper, scientist at Google Deepmind and co-recipient of the 2024 Nobel Prize in chemistry.Transcripts for each episode are available within 1-3 days at sciencefriday.com. Subscribe to this podcast. Plus, to stay updated on all things science, sign up for Science Friday's newsletters.