Podcasts about alphafold

Today's guest is Annabel Romero, Specialist Leader focusing on AI for Drug Discovery at Deloitte and a structural biologist by training. Deloitte is a global consulting firm known for its work in digital transformation, data strategy, and AI adoption across regulated industries. Annabel joins Emerj Editorial Director Matthew DeMello to explore how AI systems are being designed to think more like scientists—particularly in protein modeling and life sciences research. She shares how tools like AlphaFold and large language models are accelerating drug targeting, predicting allergen cross-reactivity, and translating learnings from human biology to agricultural innovation. This episode is sponsored by Deloitte. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the ‘AI in Business' podcast!

”I really love the notion of contributing something to physics.” — Chemistry laureate John Jumper has always been passionate about science and understanding the world. With the AI tool AlphaFold, he and his co-laureate Demis Hassabis have provided a possibility to predict protein structures. In this podcast conversation, Jumper speaks about the excitement of seeing how AI can help us more in the future.Jumper also shares his scientific journey and how he ended up working with AlphaFold. He describes a special memory from the 2018 CASP conference where AlphaFold was presented for the first time. Another life-changing moment was the announcement of the Nobel Prize in Chemistry in October 2024 – Jumper tells us how his life has changed since then. Through their lives and work, failures and successes – get to know the individuals who have been awarded the Nobel Prize on the Nobel Prize Conversations podcast. Find it on Acast, or wherever you listen to pods. https://linktr.ee/NobelPrizeConversations© Nobel Prize Outreach. Hosted on Acast. See acast.com/privacy for more information.

Can AI help us model biology down to the molecular level? Neil deGrasse Tyson, Chuck Nice, and Gary O'Reilly learn about Nobel-prize-winning Alphafold, the protein folding problem, and how solving it could end disease with AI researcher, Max Jaderberg. NOTE: StarTalk+ Patrons can listen to this entire episode commercial-free here: https://startalkmedia.com/show/curing-all-disease-with-ai-with-max-jaderberg/Thanks to our Patrons Riley r, pesketti, Lindsay Vanlerberg, Andreas, Silvia Valentine, Brazen Rigsby, Marc, Lyda Swanston, Kevin Henry, Roberto Reyes, Cadexn, Cassandra Shanklin, Stan Adamson, Will Slade, Zach VanderGraaff, Tom Spalango, Laticia Edmonds, jason scott, Jigar Gada, Robert Jensen, Matt D., TOL, Thomas McDaniel, Sr., Ryan Ramsey, truthmind, Aaron TInker, George Assaf, Dante Ruzinok, Jonathan Ford, Just Ernst, David Eli Janes, Tamil, Sarah, Earnest Lee, Craig Hanson, Rob, Be Love, Brandon Wilson, TJ Kellysawyer, Bodhi Animations, Dave P., Christina Williams, Ivaylo Vartigorov, Roy Mitsuoka (@surflightroy), John Brendel, Moises Zorrilla, deborah shaw, Jim Muoio, Tahj Ward, Phil, Alex, Brian D. Smith, Nate Barmore, John J Lopez, Raphael Velazquez Cruz, Catboi Air, Jelly Mint, Audie Cruz for supporting us this week. Subscribe to SiriusXM Podcasts+ to listen to new episodes of StarTalk Radio ad-free and a whole week early.Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus.

Think AI is hitting a wall? Nope. This is just the start. Actually, we're at the first chapter. Here's what that means, and how you can move your company ahead. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the conversationUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Generative AI's current phaseMeta's in-house AI chips developmentOpenAI's new developer toolsDay zero of AI and future prospectsReinforcement learning advancementsEmergent reasoning capabilities in AIBusiness implications of AI advancementsAI in healthcare and scienceTimestamps:00:00 Day Zero of AI03:31 AI Tools Enhance Customization & Access09:02 Reinforcement Learning Enhances AI Reasoning11:27 Agentic AI: The Future of Tasks15:59 Tech Potential vs. Everyday Utilization18:48 AI Models Offer Broad Benefits23:15 "Generative AI: Optimism and Oversight"27:08 Generative AI vs. Domain-Specific AI29:24 Superhuman AI: Next FrontierKeywords:Generative AI, Fortune 100 leaders, chat GBT, Microsoft Copilot, enterprise companies, day zero of AI, livestream podcast, free daily newsletter, leveraging AI, capital expenditures, Meta AI chips, Nvidia, Taiwan's TSMC, AI infrastructure investments, Amazon, Google, Microsoft, OpenAI, responses API, agents SDK, legal research, customer support, deep research, agentic AI, supervised learning, reinforcement learning, language models, health care, computational biology, AlphaFold, protein folding prediction.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner

In Part 2 of Tom's wide-ranging conversation with Andy Weir, Andy explores how AI will transform material science, medicine, biotechnology, and possibly even human evolution itself. From AI-designed drugs and custom gene editing to the ethical dilemmas of “designer babies” and the future of cosmetic self-alteration, Andy contemplates what these advances could mean for human identity, equality, and society's deepest values. The episode then hurtles into the far future, weighing the prospects of artificial superintelligence, AI alignment, and the ultimate “tool or agent” debate. Tom and Andy touch on open versus closed source AI, existential risk, and what humanity's historical track record tells us about technology. SHOWNOTES 22:08 AI's leap in material science, biotech, and AlphaFold's revolution28:49 Hardware bottlenecks and the coming “AI card” revolution32:09 Efficiency breakthroughs, compression, and training paradigm shifts36:10 How new materials could propel us to low Earth orbit38:39 AI-designed proteins: The promise and danger within biology39:47 The ethics of designer babies: Health, intelligence, and consent46:38 The coming age of “cosmetic ethnicity” and identity fluidity47:29 Body hacking: Social and economic consequences, from eating to politics48:32 Why society will push—and resist—genetic modifications49:34 The looming “intelligence arms race” between humans and AI50:15 Why Andy doubts the need to compete with AI; the “bulldozer analogy”57:15 Caution and optimism: Why Andy expects a post-scarcity AI future58:10 Why “control” is likely to stay with humans—unless we hand it over1:01:04 Open source debate, narrative control, and algorithmic bias1:28:00 What excites Andy: Self-driving cars and societal revolution1:33:57 Andy on writing, his approach to AI, and what's next for his books1:35:29 Where to follow Andy Weir FOLLOW ANDY WEIR:Twitter/X: @andyweirauthorFacebook: Andy Weir CHECK OUT OUR SPONSORS ButcherBox: Ready to level up your meals? Go to ⁠https://ButcherBox.com/impact⁠ to get $20 off your first box and FREE bacon for life with the Bilyeu Box! Vital Proteins: Get 20% off by going to ⁠https://www.vitalproteins.com⁠ and entering promo code IMPACT at check out Netsuite: Download the CFO's Guide to AI and Machine Learning at ⁠https://NetSuite.com/THEORY⁠ iTrust Capital: Use code IMPACTGO when you sign up and fund your account to get a $100 bonus at ⁠https://www.itrustcapital.com/tombilyeu⁠  Mint Mobile: If you like your money, Mint Mobile is for you. Shop plans at ⁠https://mintmobile.com/impact.⁠  DISCLAIMER: Upfront payment of $45 for 3-month 5 gigabyte plan required (equivalent to $15/mo.). New customer offer for first 3 months only, then full-price plan options available. Taxes & fees extra. See MINT MOBILE for details. What's up, everybody? It's Tom Bilyeu here: If you want my help... STARTING a business:⁠ join me here at ZERO TO FOUNDER⁠ SCALING a business:⁠ see if you qualify here.⁠ Get my battle-tested strategies and insights delivered weekly to your inbox:⁠ sign up here.⁠ ********************************************************************** If you're serious about leveling up your life, I urge you to check out my new podcast,⁠ Tom Bilyeu's Mindset Playbook⁠ —a goldmine of my most impactful episodes on mindset, business, and health. Trust me, your future self will thank you. ********************************************************************** LISTEN TO IMPACT THEORY AD FREE + BONUS EPISODES on APPLE PODCASTS:⁠ apple.co/impacttheory⁠ ********************************************************************** FOLLOW TOM: Instagram:⁠ https://www.instagram.com/tombilyeu/⁠ Tik Tok:⁠ https://www.tiktok.com/@tombilyeu?lang=en⁠ Twitter:⁠ https://twitter.com/tombilyeu⁠ YouTube:⁠ https://www.youtube.com/@TomBilyeu⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Delve into the intersection of privacy, ethics, and technology with Dr. Rand Hindi, CEO of Zama. We discuss Dr. Hindi's early fascination with encryption, the future of homomorphic encryption, and its profound implications for various sectors including healthcare and advertising.Hindi explains advancements in bioinformatics, his vision of ubiquitous computing, and how individualized medicine can benefit from encrypted data.He also shares his personal experiments with diet and fitness, promising advancements in medtech, and the future outlook on transhumanism.00:00 Introduction to Privacy and Ethics00:07 Meet Dr. Rand Hindi: Deep Tech Entrepreneur00:53 The Personal Connection to Encryption03:26 Homomorphic Encryption Explained05:34 Advancements and Applications of Homomorphic Encryption07:22 The Future of Ubiquitous Computing09:36 AlphaFold and the Future of Biology12:12 Homomorphic Encryption in Healthcare14:47 Personal Experiments in Weight Loss & Fitness19:41 The Role of AI in Job Automation21:53 Ethereum and the Future of Blockchain24:17 The Potential of Precision Psychedelics26:55 Transhumanism and Extending Human Lifespan34:43 Geoengineering and Climate Change37:32 Final Thoughts and Farewell Get full access to Evolving with Nita Jain at www.nitajain.com/subscribe

The race to harness AI for scientific discovery may be the most consequential technological competition of this time—yet it's happening largely out of public view. While many AI headlines focus on chatbots writing essays and tech giants battling over billion-dollar models, a quiet revolution is brewing in America's laboratories.AI systems like AlphaFold (which recently won a Nobel Prize for protein structure prediction) are solving scientific problems that stumped humans for decades. A bipartisan coalition in Congress is now championing what they call the "American Science Acceleration Project" or ASAP—an audacious plan to make U.S. scientific research "ten times faster by 2030" through strategic deployment of AI. But as federal science funding faces pressure and international competition heats up, can America build the AI-powered scientific infrastructure we need? Will the benefits reach beyond elite coastal institutions to communities nationwide? And how do we ensure that as AI transforms scientific discovery, it creates opportunities instead of new divides?Joining us is Austin Carson, Founder and President of SeedAI, a nonprofit dedicated to expanding AI access and opportunity across America. Before launching SeedAI, Carson led government affairs at NVIDIA and served as Legislative Director for Rep. Michael McCaul. He's been deep in AI policy since 2016—ancient history in this rapidly evolving field—and recently organized the first-ever generative AI red-teaming event at DEF CON, collaborating with the White House to engage hundreds of college students in identifying AI vulnerabilities.

Super épisode avec Nicolas Granatino, investisseur deeptech au cœur de la scène IA européenne, bras armé d'Eric Schmidt en France, et pionnier des deals les plus emblématiques de l'IA open source.

After pioneering reinforcement learning breakthroughs at DeepMind with Capture the Flag and AlphaStar, Max Jaderberg aims to revolutionize drug discovery with AI as Chief AI Officer of Isomorphic Labs, which was spun out of DeepMind. He discusses how AlphaFold 3's diffusion-based architecture enables unprecedented understanding of molecular interactions, and why we're approaching a "Move 37 moment" in AI-powered drug design where models will surpass human intuition. Max shares his vision for general AI models that can solve all diseases, and the importance of developing agents that can learn to search through the whole potential design space. Hosted by Stephanie Zhan, Sequoia capital Mentioned in this episode:  Playing Atari with Deep Reinforcement Learning: Seminal 2013 paper on Reinforcement Learning  Capture the Flag: 2019 DeepMind paper on the emergence of cooperative agents AlphaStar: 2019 DeepMind paper on attaining grandmaster level in StarCraft II using multi-agent RL AlphaFold Server: Web interface for AlphaFold 3 model for non-commercial academic use

This week on "Waking Up With AI," Katherine Forrest and Anna Gressel explore the intersection of AI and biology, focusing on how DeepMind's AlphaFold has revolutionized protein folding prediction by enabling scientists to better understand protein structures and interactions. ## Learn More About Paul, Weiss's Artificial Intelligence Practice: https://www.paulweiss.com/practices/litigation/artificial-intelligence

Demis Hassabis, CEO of Google DeepMind, sparked excitement with his 60 Minutes interview, outlining AI's potential to end all diseases within a decade. Drawing parallels to AlphaFold's revolutionary protein folding solution, Hassabis envisions AI drastically accelerating drug discovery, compressing timelines from years and billions to mere months by rapidly analyzing vast datasets. He highlights DeepMind's AI's astonishing discovery of millions of new materials, far surpassing traditional research, showcasing AI's power to "blaze through solutions." We delve into this ambitious vision, considering its feasibility and comparing it to futuristic scenarios, while also exploring AI's growing impact in cybersecurity, fraud prevention, and diagnostics.Beyond healthcare, we touch upon Will Manidis's intriguing observations on unexpected "miracle cures" linked to LLMs and a humorous take from Sam Altman on ChatGPT etiquette. We also spotlight a compelling custom ChatGPT prompt shared by @andrewchen (https://x.com/andrewchen/status/1914168705228882105). Join us for a thought-provoking discussion on the transformative power of AI and its potential to revolutionize our future.Mentioned: @GoogleDeepMind @demishassabis @WillManidis @andrewchen

How can AI help us understand and master deeply complex systems—from the game Go, which has 10 to the power 170 possible positions a player could pursue, or proteins, which, on average, can fold in 10 to the power 300 possible ways? This week, Reid and Aria are joined by Demis Hassabis. Demis is a British artificial intelligence researcher, co-founder, and CEO of the AI company, DeepMind. Under his leadership, DeepMind developed Alpha Go, the first AI to defeat a human world champion in Go and later created AlphaFold, which solved the 50-year-old protein folding problem. He's considered one of the most influential figures in AI. Demis, Reid, and Aria discuss game theory, medicine, multimodality, and the nature of innovation and creativity. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/  Listen to more from Possible here. Learn more about your ad choices. Visit podcastchoices.com/adchoices

How can AI help us understand and master deeply complex systems—from the game Go, which has 10 to the power 170 possible positions a player could pursue, or proteins, which, on average, can fold in 10 to the power 300 possible ways? This week, Reid and Aria are joined by Demis Hassabis. Demis is a British artificial intelligence researcher, co-founder, and CEO of the AI company, DeepMind. Under his leadership, DeepMind developed Alpha Go, the first AI to defeat a human world champion in Go and later created AlphaFold, which solved the 50-year-old protein folding problem. He's considered one of the most influential figures in AI. Demis, Reid, and Aria discuss game theory, medicine, multimodality, and the nature of innovation and creativity. For more info on the podcast and transcripts of all the episodes, visit https://www.possible.fm/podcast/  Select mentions:  Hitchhiker's Guide to the Galaxy by Douglas Adams AlphaGo documentary: https://www.youtube.com/watch?v=WXuK6gekU1Y Nash equilibrium & US mathematician John Forbes Nash Homo Ludens by Johan Huizinga Veo 2, an advanced, AI-powered video creation platform from Google DeepMind The Culture series by Iain Banks Hartmut Neven, German-American computer scientist Topics: 3:11 - Hellos and intros 5:20 - Brute force vs. self-learning systems 8:24 - How a learning approach helped develop new AI systems 11:29 - AlphaGo's Move 37 16:16 - What will the next Move 37 be? 19:42 - What makes an AI that can play the video game StarCraft impressive 22:32 - The importance of the act of play 26:24 - Data and synthetic data 28:33 - Midroll ad 28:39 - Is it important to have AI embedded in the world? 33:44 - The trade-off between thinking time and output quality 36:03 - Computer languages designed for AI 40:22 - The future of multimodality  43:27 - AI and geographic diversity  48:24 - AlphaFold and the future of medicine 51:18 - Rapid-fire Questions Possible is an award-winning podcast that sketches out the brightest version of the future—and what it will take to get there. Most of all, it asks: what if, in the future, everything breaks humanity's way? Tune in for grounded and speculative takes on how technology—and, in particular, AI—is inspiring change and transforming the future. Hosted by Reid Hoffman and Aria Finger, each episode features an interview with an ambitious builder or deep thinker on a topic, from art to geopolitics and from healthcare to education. These conversations also showcase another kind of guest: AI. Each episode seeks to enhance and advance our discussion about what humanity could possibly get right if we leverage technology—and our collective effort—effectively.

In this episode of Crazy Wisdom, Stewart Alsop speaks with German Jurado about the strange loop between computation and biology, the emergence of reasoning in AI models, and what it means to "stand on the shoulders" of evolutionary systems. They talk about CRISPR not just as a gene-editing tool, but as a memory architecture encoded in bacterial immunity; they question whether LLMs are reasoning or just mimicking it; and they explore how scientists navigate the unknown with a kind of embodied intuition. For more about German's work, you can connect with him through email at germanjurado7@gmail.com.Check out this GPT we trained on the conversation!Timestamps00:00 - Stewart introduces German Jurado and opens with a reflection on how biology intersects with multiple disciplines—physics, chemistry, computation.05:00 - They explore the nature of life's interaction with matter, touching on how biology is about the interface between organic systems and the material world.10:00 - German explains how bioinformatics emerged to handle the complexity of modern biology, especially in genomics, and how it spans structural biology, systems biology, and more.15:00 - Introduction of AI into the scientific process—how models are being used in drug discovery and to represent biological processes with increasing fidelity.20:00 - Stewart and German talk about using LLMs like GPT to read and interpret dense scientific literature, changing the pace and style of research.25:00 - The conversation turns to societal implications—how these tools might influence institutions, and the decentralization of expertise.30:00 - Competitive dynamics between AI labs, the scaling of context windows, and speculation on where the frontier is heading.35:00 - Stewart reflects on English as the dominant language of science and the implications for access and translation of knowledge.40:00 - Historical thread: they discuss the Republic of Letters, how the structure of knowledge-sharing has evolved, and what AI might do to that structure.45:00 - Wrap-up thoughts on reasoning, intuition, and the idea of scientists as co-evolving participants in both natural and artificial systems.50:00 - Final reflections and thank-yous, German shares where to find more of his thinking, and Stewart closes the loop on the conversation.Key InsightsCRISPR as a memory system – Rather than viewing CRISPR solely as a gene-editing tool, German Jurado frames it as a memory architecture—an evolved mechanism through which bacteria store fragments of viral DNA as a kind of immune memory. This perspective shifts CRISPR into a broader conceptual space, where memory is not just cognitive but deeply biological.AI models as pattern recognizers, not yet reasoners – While large language models can mimic reasoning impressively, Jurado suggests they primarily excel at statistical pattern matching. The distinction between reasoning and simulation becomes central, raising the question: are these systems truly thinking, or just very good at appearing to?The loop between computation and biology – One of the core themes is the strange feedback loop where biology inspires computational models (like neural networks), and those models in turn are used to probe and understand biological systems. It's a recursive relationship that's accelerating scientific insight but also complicating our definitions of intelligence and understanding.Scientific discovery as embodied and intuitive – Jurado highlights that real science often begins in the gut, in a kind of embodied intuition before it becomes formalized. This challenges the myth of science as purely rational or step-by-step and instead suggests that hunches, sensory experience, and emotional resonance play a crucial role.Proteins as computational objects – Proteins aren't just biochemical entities—they're shaped by information. Their structure, function, and folding dynamics can be seen as computations, and tools like AlphaFold are beginning to unpack that informational complexity in ways that blur the line between physics and code.Human alignment is messier than AI alignment – While AI alignment gets a lot of attention, Jurado points out that human alignment—between scientists, institutions, and across cultures—is historically chaotic. This reframes the AI alignment debate in a broader evolutionary and historical context, questioning whether we're holding machines to stricter standards than ourselves.Standing on the shoulders of evolutionary processes – Evolution is not just a backdrop but an active epistemic force. Jurado sees scientists as participants in a much older system of experimentation and iteration—evolution itself. In this view, we're not just designing models; we're being shaped by them, in a co-evolution of tools and understanding.

Today's guest is Dan Elton, a Staff Scientist at the National Human Genome Research Institute (NHGRI) at the National Institutes of Health (NIH). Dan returns to the program to explore how AI is advancing genetic research, from protein engineering to gene editing and risk prediction. One of the most significant breakthroughs in this space is AlphaFold, DeepMind's AI model that predicts protein structures with unprecedented accuracy. While it does not analyze genetic sequences directly, its ability to model protein folding is transforming drug development and protein engineering. Dan also discusses the potential for AI to improve polygenic risk prediction, where machine learning models are being applied to assess disease risk based on genetic markers. If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

Today's guest is Nishtha Jain, Head of Innovation and Digital Technology at Takeda Pharmaceuticals. Nistha returns to the program to discuss the transformative impact of AI across the pharmaceutical value chain. She and Emerj Editorial Director Matthew DeMello explore how AI is improving drug development efficiency, addressing data challenges, and overcoming regulatory hurdles. She highlights key breakthroughs like Google DeepMind's AlphaFold and AI-assisted clinical trial optimization, emphasizing the potential for faster, more cost-effective drug development. The discussion also covers the challenges of AI adoption, including data accessibility, regulatory compliance, and ethical considerations like bias in AI models. If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show!

Many in venture capital and biopharma are anointing artificial intelligence the savior of drug discovery—but what can AI actually do?In this eye-opening episode, Michael Marks sits down with Mike Nohaile, CEO of Prellis Biologics, to explore the hype versus reality in AI-enabled drug discovery. Mike details why, despite significant breakthroughs like AlphaFold and recent Nobel Prize win for computational protein design, fully AI-generated medicines still present challenges. He also discusses why we urgently need more effective medicines and details Prellis' unique system which combines laser printed human organoids and an externalized human immune system with AI, enabling the discovery of fully human antibodies. If you enjoy this episode, please subscribe and leave us a review on your favorite podcast platform. Sign up for our newsletter at techsurgepodcast.com for exclusive insights and updates on upcoming TechSurge Live Summits.Links:Explore Prellis Biologicshttps://prellisbio.com/Understand AlphaFold, DeepMind's AI model for predicting protein structureshttps://deepmind.google/alphafoldRead about the 2024 Nobel Prize in Chemistry https://www.nobelprize.org/prizes/chemistry/2024/press-release/ 

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It could be argued that biology has always boiled down to chemistry, and that chemistry has always boiled down to physics. However, not many would deny that the fields of biology and chemistry are overlapping more than ever, with both leveraging computing methods, also more than ever. This conversation with Dr. Ramesh Jha, Technical Staff Member at Los Alamos National Laboratory (LANL), crosses biology, chemistry, and computing methods. The work of his biome team at LANL uses computational tools to inform the design of enzymes that are produced via PCR-based cloning and then expressed in microbes. They use fluorescent gene circuits in these microbes, along with flow cytometry, to screen these large libraries for advantageous gain-of-function variants. When they find an interesting mutation, they isolate it, sequence it, and produce and evaluate those biocatalytic enzymes for bioremediation, biomanufacturing, and other important applications. Ramesh makes this complex and interdisciplinary science approachable and gives hope to how it could help address problems of “forever chemicals” and other environmental and manufacturing challenges. Join us for this interesting and inspiring conversation.  Subscribe to get future episodes as they drop and if you like what you're hearing we hope you'll share a review or recommend the series to a colleague.  Visit the Invitrogen School of Molecular Biology to access helpful molecular biology resources and educational content, and please share this resource with anyone you know working in molecular biology. For Research Use Only. Not for use in diagnostic procedures.

Funding for the NIH and US biomedical research is imperiled at a momentous time of progress. Exemplifying this is the work of Dr. Anna Greka, a leading physician-scientist at the Broad Institute who is devoted to unlocking the mysteries of rare diseases— that cumulatively affect 30 million Americans— and finding cures, science supported by the NIH.A clip from our conversationThe audio is available on iTunes and Spotify. The full video is linked here, at the top, and also can be found on YouTube.Transcript with audio and external linksEric Topol (00:06):Well, hello. This is Eric Topol from Ground Truths, and I am really delighted to welcome today, Anna Greka. Anna is the president of the American Society for Clinical Investigation (ASCI) this year, a very prestigious organization, but she's also at Mass General Brigham, a nephrologist, a cell biologist, a physician-scientist, a Core Institute Member of the Broad Institute of MIT and Harvard, and serves as a member of the institute's Executive Leadership Team. So we got a lot to talk about of all these different things you do. You must be pretty darn unique, Anna, because I don't know any cell biologists, nephrologists, physician-scientist like you.Anna Greka (00:48):Oh, thank you. It's a great honor to be here and glad to chat with you, Eric.Eric Topol (00:54):Yeah. Well, I had the real pleasure to hear you speak at a November conference, the AI for Science Forum, which we'll link to your panel. Where I was in a different panel, but you spoke about your extraordinary work and it became clear that we need to get you on Ground Truths, so you can tell your story to everybody. So I thought rather than kind of going back from the past where you were in Greece and somehow migrated to Boston and all that. We're going to get to that, but you gave an amazing TED Talk and it really encapsulated one of the many phenomenal stories of your work as a molecular sleuth. So maybe if you could give us a synopsis, and of course we'll link to that so people could watch the whole talk. But I think that Mucin-1 or MUC1, as you call it, discovery is really important to kind of ground our discussion.A Mysterious Kidney Disease Unraveled Anna Greka (01:59):Oh, absolutely. Yeah, it's an interesting story. In some ways, in my TED Talk, I highlight one of the important families of this story, a family from Utah, but there's also other important families that are also part of the story. And this is also what I spoke about in London when we were together, and this is really sort of a medical mystery that initially started on the Mediterranean island of Cyprus, where it was found that there were many families in which in every generation, several members suffered and ultimately died from what at the time was a mysterious kidney disease. This was more than 30 years ago, and it was clear that there was something genetic going on, but it was impossible to identify the gene. And then even with the advent of Next-Gen sequencing, this is what's so interesting about this story, it was still hard to find the gene, which is a little surprising.Anna Greka (02:51):After we were able to sequence families and identify monogenic mutations pretty readily, this was still very resistant. And then it actually took the firepower of the Broad Institute, and it's actually from a scientific perspective, an interesting story because they had to dust off the old-fashioned Sanger sequencing in order to get this done. But they were ultimately able to identify this mutation in a VNTR region of the MUC1 gene. The Mucin-1 gene, which I call a dark corner of the human genome, it was really, it's highly repetitive, very GC-rich. So it becomes very difficult to sequence through there with Next-Gen sequencing. And so, ultimately the mutation of course was found and it's a single cytosine insertion in a stretch of cytosines that sort of causes this frameshift mutation and an early stop codon that essentially results in a neoprotein like a toxic, what I call a mangled protein that sort of accumulates inside the kidney cells.Anna Greka (03:55):And that's where my sort of adventure began. It was Eric Lander's group, who is the founding director of the Broad who discovered the mutation. And then through a conversation we had here in Boston, we sort of discovered that there was an opportunity to collaborate and so that's how I came to the Broad, and that's the beginnings of this story. I think what's fascinating about this story though, that starts in a remote Mediterranean island and then turns out to be a disease that you can find in every continent all over the world. There are probably millions of patients with kidney disease in whom we haven't recognized the existence of this mutation. What's really interesting about it though is that what we discovered is that the mangled protein that's a result of this misspelling of this mutation is ultimately captured by a family of cargo receptors, they're called the TMED cargo receptors and they end up sort of grabbing these misfolded proteins and holding onto them so tight that it's impossible for the cell to get rid of them.Anna Greka (04:55):And they become this growing heap of molecular trash, if you will, that becomes really hard to manage, and the cells ultimately die. So in the process of doing this molecular sleuthing, as I call it, we actually also identified a small molecule that actually disrupts these cargo receptors. And as I described in my TED Talk, it's a little bit like having these cargo trucks that ultimately need to go into the lysosome, the cells recycling facility. And this is exactly what this small molecule can do. And so, it was just like a remarkable story of discovery. And then I think the most exciting of all is that these cargo receptors turn out to be not only relevant to this one mangled misshapen protein, but they actually handle a completely different misshapen protein caused by a different genetic mutation in the eye, causing retinitis pigmentosa, a form of blindness, familial blindness. We're now studying familial Alzheimer's disease that's also involving these cargo receptors, and there are other mangled misshapen proteins in the liver, in the lung that we're now studying. So this becomes what I call a node, like a nodal mechanism that can be targeted for the benefit of many more patients than we had previously thought possible, which has been I think, the most satisfying part about this story of molecular sleuthing.Eric Topol (06:20):Yeah, and it's pretty extraordinary. We'll put the figure from your classic Cell paper in 2019, where you have a small molecule that targets the cargo receptor called TMED9.Anna Greka (06:34):Correct.Expanding the MissionEric Topol (06:34):And what's amazing about this, of course, is the potential to reverse this toxic protein disease. And as you say, it may have applicability well beyond this MUC1 kidney story, but rather eye disease with retinitis pigmentosa and the familial Alzheimer's and who knows what else. And what's also fascinating about this is how, as you said, there were these limited number of families with the kidney disease and then you found another one, uromodulin. So there's now, as you say, thousands of families, and that gets me to part of your sleuth work is not just hardcore science. You started an entity called the Ladders to Cures (L2C) Scientific Accelerator.Eric Topol (07:27):Maybe you can tell us about that because this is really pulling together all the forces, which includes the patient advocacy groups, and how are we going to move forward like this?Anna Greka (07:39):Absolutely. I think the goal of the Ladders to Cures Accelerator, which is a new initiative that we started at the Broad, but it really encompasses many colleagues across Boston. And now increasingly it's becoming sort of a national, we even have some international collaborations, and it's only two years that it's been in existence, so we're certainly in a growth mode. But the inspiration was really some of this molecular sleuthing work where I basically thought, well, for starters, it cannot be that there's only one molecular node, these TMED cargo receptors that we discovered there's got to be more, right? And so, there's a need to systematically go and find more nodes because obviously as anyone who works in rare genetic diseases will tell you, the problem for all of us is that we do what I call hand to hand combat. We start with the disease with one mutation, and we try to uncover the mechanism and then try to develop therapies, and that's wonderful.Anna Greka (08:33):But of course, it's slow, right? And if we consider the fact that there are 30 million patients in the United States in every state, everywhere in the country who suffer from a rare genetic disease, most of them, more than half of them are children, then we can appreciate the magnitude of the problem. Out of more than 8,000 genes that are involved in rare genetic diseases, we barely have something that looks like a therapy for maybe 500 of them. So there's a huge mismatch in the unmet need and magnitude of the problem. So the Ladders to Cures Accelerator is here to address this and to do this with the most modern tools available. And to your point, Eric, to bring patients along, not just as the recipients of whatever we discover, but also as partners in the research enterprise because it's really important to bring their perspectives and of course their partnerships in things like developing appropriate biomarkers, for example, for what we do down the road.Anna Greka (09:35):But from a fundamental scientific perspective, this is basically a project that aims to identify every opportunity for nodes, underlying all rare genetic diseases as quickly as possible. And this was one of the reasons I was there at the AI for Science Forum, because of course when one undertakes a project in which you're basically, this is what we're trying to do in the Ladders to Cures Accelerator, introduce dozens of thousands of missense and nonsense human mutations that cause genetic diseases, simultaneously introduce them into multiple human cells and then use modern scalable technology tools. Things like CRISPR screens, massively parallel CRISPR screens to try to interrogate all of these diseases in parallel, identify the nodes, and then develop of course therapeutic programs based on the discovery of these nodes. This is a massive data generation project that is much needed and in addition to the fact that it will help hopefully accelerate our approach to all rare diseases, genetic diseases. It is also a highly controlled cell perturbation dataset that will require the most modern tools in AI, not only to extract the data and understand the data of this dataset, but also because this, again, an extremely controlled, well controlled cell perturbation dataset can be used to train models, train AI models, so that in the future, and I hope this doesn't sound too futuristic, but I think that we're all aiming for that cell biologists for sure dream of this moment, I think when we can actually have in silico the opportunity to make predictions about what cell behaviors are going to look like based on a new perturbation that was not in the training set. So an experiment that hasn't yet been done on a cell, a perturbation that has not been made on a human cell, what if like a new drug, for example, or a new kind of perturbation, a new chemical perturbation, how would it affect the behavior of the cell? Can we make a predictive model for that? This doesn't exist today, but I think this is something, the cell prediction model is a big question for biology for the future. And so, I'm very energized by the opportunity to both address this problem of rare monogenic diseases that remains an unmet need and help as many patients as possible while at the same time advancing biology as much as we possibly can. So it's kind of like a win-win lifting all boats type of enterprise, hopefully.Eric Topol (12:11):Yeah. Well, there's many things to get to unpack what you've just been reviewing. So one thing for sure is that of these 8,000 monogenic diseases, they have relevance to the polygenic common diseases, of course. And then also the fact that the patient family advocates, they are great at scouring the world internet, finding more people, bringing together communities for each of these, as you point out aptly, these rare diseases cumulatively are high, very high proportion, 10% of Americans or more. So they're not so rare when you think about the overall.Anna Greka (12:52):Collectively.Help From the Virtual Cell?Eric Topol (12:53):Yeah. Now, and of course is this toxic proteinopathies, there's at least 50 of these and the point that people have been thinking until now that, oh, we found a mangled protein, but what you've zeroed in on is that, hey, you know what, it's not just a mangled protein, it's how it gets stuck in the cell and that it can't get to the lysosome to get rid of it, there's no waste system. And so, this is such fundamental work. Now that gets me to the virtual cell story, kind of what you're getting into. I just had a conversation with Charlotte Bunne and Steve Quake who published a paper in December on the virtual cell, and of course that's many years off, but of course it's a big, bold, ambitious project to be able to say, as you just summarized, if you had cells in silico and you could do perturbations in silico, and of course they were validated by actual experiments or bidirectionally the experiments, the real ones helped to validate the virtual cell, but then you could get a true acceleration of your understanding of cell biology, your field of course.Anna Greka (14:09):Exactly.Eric Topol (14:12):So what you described, is it the same as a virtual cell? Is it kind of a precursor to it? How do you conceive this because this is such a complex, I mean it's a fundamental unit of life, but it's also so much more complex than a protein or an RNA because not only all the things inside the cell, inside all these organelles and nucleus, but then there's all the outside interactions. So this is a bold challenge, right?Anna Greka (14:41):Oh my god, it's absolutely from a biologist perspective, it's the challenge of a generation for sure. We think taking humans to Mars, I mean that's an aspirational sort of big ambitious goal. I think this is the, if you will, the Mars shot for biology, being able to, whether the terminology, whether you call it a virtual cell. I like the idea of saying that to state it as a problem, the way that people who think about it from a mathematics perspective for example, would think about it. I think stating it as the cell prediction problem appeals to me because it actually forces us biologists to think about setting up the way that we would do these cell perturbation data sets, the way we would generate them to set them up to serve predictions. So for example, the way that I would think about this would be can I in the future have so much information about how cell perturbations work that I can train a model so that it can predict when I show it a picture of another cell under different conditions that it hasn't seen before, that it can still tell me, ah, this is a neuron in which you perturbed the mitochondria, for example, and now this is sort of the outcome that you would expect to see.Anna Greka (16:08):And so, to be able to have this ability to have a model that can have the ability to predict in silico what cells would look like after perturbation, I think that's sort of the way that I think about this problem. It is very far away from anything that exists today. But I think that the beginning starts, and this is one of the unique things about my institute, if I can say, we have a place where cell biologists, geneticists, mathematicians, machine learning experts, we all come together in the same place to really think and grapple with these problems. And of course we're very outward facing, interacting with scientists all across the world as well. But there's this sort of idea of bringing people into one institute where we can just think creatively about these big aspirational problems that we want to solve. I think this is one of the unique things about the ecosystem at the Broad Institute, which I'm proud to be a part of, and it is this kind of out of the box thinking that will hopefully get us to generate the kinds of data sets that will serve the needs of building these kinds of models with predictive capabilities down the road.Anna Greka (17:19):But as you astutely said, AlphaFold of course was based on the protein database existing, right? And that was a wealth of available information in which one could train models that would ultimately be predictive, as we have seen this miracle that Demi Hassabis and John Jumper have given to humanity, if you will.Anna Greka (17:42):But as Demis and John would also say, I believe is as I have discussed with them, in fact, the cell prediction problem is really a bigger problem because we do not have a protein data bank to go to right now, but we need to create it to generate these data. And so, my Ladders to Cures Accelerator is here to basically provide some part of the answer to that problem, create this kind of well-controlled database that we need for cell perturbations, while at the same time maximizing our learnings about these fully penetrant coding mutations and what their downstream sequelae would be in many different human cells. And so, in this way, I think we can both advance our knowledge about these monogenic diseases, build models, hopefully with predictive capabilities. And to your point, a lot of what we will learn about this biology, if we think that it involves 8,000 or more out of the 20,000 genes in our genome, it will of course serve our understanding of polygenic diseases ultimately as well as we go deeper into this biology and we look at the combinatorial aspects of what different mutations do to human cells. And so, it's a huge aspirational problem for a whole generation, but it's a good one to work on, I would say.Learning the Language of Life with A.I. Eric Topol (19:01):Oh, absolutely. Now I think you already mentioned something that's quite, well, two things from what you just touched on. One of course, how vital it is to have this inner or transdisciplinary capability because you do need expertise across these vital areas. But the convergence, I mean, I love your term nodal biology and the fact that there's all these diseases like you were talking about, they do converge and nodal is a good term to highlight that, but it's not. Of course, as you mentioned, we have genome editing which allows to look at lots of different genome perturbations, like the single letter change that you found in MUC1 pathogenic critical mutation. There's also the AI world which is blossoming like I've never seen. In fact, I had in Science this week about learning the language of life with AI and how there's been like 15 new foundation models, DNA, proteins, RNA, ligands, all their interactions and the beginning of the cell story too with the human cell.Eric Topol (20:14):So this is exploding. As you said, the expertise in computer science and then this whole idea that you could take these powerful tools and do as you said, which is the need to accelerate, we just can't sit around here when there's so much discovery work to be done with the scalability, even though it might take years to get to this artificial intelligence virtual cell, which I have to agree, everyone in biology would say that's the holy grail. And as you remember at our conference in London, Demi Hassabis said that's what we'd like to do now. So it has the attention of leaders in AI around the world, obviously in the science and the biomedical community like you and many others. So it is an extraordinary time where we just can't sit still with these tools that we have, right?Anna Greka (21:15):Absolutely. And I think this is going to be, you mentioned the ASCI presidency in the beginning of our call. This is going to be the president gets to give an address at the annual meeting in Chicago. This is going to be one of the points I make, no matter what field in biomedicine we're in, we live in, I believe, a golden era and we have so many tools available to us that we can really accelerate our ability to help more patients. And of course, this is our mandate, the most important stakeholders for everything that we do as physician-scientists are our patients ultimately. So I feel very hopeful for the future and our ability to use these tools and to really make good on the promise of research is a public good. And I really hope that we can advance our knowledge for the benefit of all. And this is really an exciting time, I think, to be in this field and hopefully for the younger colleagues a time to really get excited about getting in there and getting involved and asking the big questions.Career ReflectionsEric Topol (22:21):Well, you are the prototype for this and an inspiration to everyone really, I'm sure to your lab group, which you highlighted in the TED Talk and many other things that you do. Now I want to spend a little bit of time about your career. I think it's fascinating that you grew up in Greece and your father's a nephrologist and your mother's a pathologist. So you had two physicians to model, but I guess you decided to go after nephrology, which is an area in medicine that I kind of liken it to Rodney Dangerfield, he doesn't get any respect. You don't see many people that go into nephrology. But before we get to your decision to do that somehow or other you came from Greece to Harvard for your undergrad. How did you make that connect to start your college education? And then subsequently you of course you stayed in Boston, you've never left Boston, I think.Anna Greka (23:24):I never left. Yeah, this is coming into 31 years now in Boston.Anna Greka (23:29):Yeah, I started as a Harvard undergraduate and I'm now a full professor. It's kind of a long, but wonderful road. Well, actually I would credit my parents. You mentioned that my father, they're both physician-scientists. My father is now both retired, but my father is a nephrologist, and my mother is a pathologist, actually, they were both academics. And so, when we were very young, we lived in England when my parents were doing postdoctoral work. That was actually a wonderful gift that they gave me because I became bilingual. It was a very young age, and so that allowed me to have this advantage of being fluent in English. And then when we moved back to Greece where I grew up, I went to an American school. And from that time, this is actually an interesting story in itself. I'm very proud of this school.Anna Greka (24:22):It's called Anatolia, and it was founded by American missionaries from Williams College a long time ago, 150 and more years ago. But it is in Thessaloniki, Greece, which is my hometown, and it's a wonderful institution, which gave me a lot of gifts as well, preparing me for coming to college in the United States. And of course, I was a good student in high school, but what really was catalytic was that I was lucky enough to get a scholarship to go to Harvard. And that was really, you could say the catalyst that propelled me from a teenager who was dreaming about a career as a physician-scientist because I certainly was for as far back as I remember in fact. But then to make that a reality, I found myself on the Harvard campus initially for college, and then I was in the combined Harvard-MIT program for my MD PhD. And then I trained in Boston at Mass General in Brigham, and then sort of started my academic career. And that sort of brings us to today, but it is an unlikely story and one that I feel still very lucky and blessed to have had these opportunities. So for sure, it's been wonderful.Eric Topol (25:35):We're the ones lucky that you came here and set up shop and you did your productivity and discovery work and sleuthing has been incredible. But I do think it's interesting too, because when you did your PhD, it was in neuroscience.Anna Greka (25:52):Ah, yes. That's another.Eric Topol (25:54):And then you switch gears. So tell us about that?Anna Greka (25:57):This is interesting, and actually I encourage more colleagues to think about it this way. So I have always been driven by the science, and I think that it seems a little backward to some people, but I did my PhD in neuroscience because I was interested in understanding something about these ion channels that were newly discovered at the time, and they were most highly expressed in the brain. So here I was doing work in the brain in the neuroscience program at Harvard, but then once I completed my PhD and I was in the middle of my residency training actually at Mass General, I distinctly remember that there was a paper that came out that implicated the same family of ion channels that I had spent my time understanding in the brain. It turned out to be a channelopathy that causes kidney disease.Anna Greka (26:43):So that was the light bulb, and it made me realize that maybe what I really wanted to do is just follow this thread. And my scientific curiosity basically led me into studying the kidney and then it seemed practical therefore to get done with my clinical training as efficiently as possible. So I finished residency, I did nephrology training, and then there I was in the lab trying to understand the biology around this channelopathy. And that sort of led us into the early projects in my young lab. And in fact, it's interesting we didn't talk about that work, but that work in itself actually has made it all the way to phase II trials in patients. This was a paper we published in Science in 2017 and follow onto that work, there was an opportunity to build this into a real drug targeting one of these ion channels that has made it into phase II trials. And we'll see what happens next. But it's this idea of following your scientific curiosity, which I also talked about in my TED Talk, because you don't know to what wonderful places it will lead you. And quite interestingly now my lab is back into studying familial Alzheimer's and retinitis pigmentosa in the eye in brain. So I tell people, do not limit yourself to whatever someone says your field is or should be. Just follow your scientific curiosity and usually that takes you to a lot more interesting places. And so, that's certainly been a theme from my career, I would say.Eric Topol (28:14):No, I think that's perfect. Curiosity driven science is not the term. You often hear hypothesis driven or now with AI you hear more AI exploratory science. But no, that's great. Now I want to get a little back to the AI story because it's so fascinating. You use lots of different types of AI such as cellular imaging would be fusion models and drug discovery. I mean, you've had drug discovery for different pathways. You mentioned of course the ion channel and then also as we touched on with your Cell paper, the whole idea of targeting the cargo receptor with a small molecule and then things in between. You discussed this of course at the London panel, but maybe you just give us the skinny on the different ways that you incorporate AI in the state-of-the-art science that you're doing?Anna Greka (29:17):Sure, yeah, thank you. I think there are many ways in which even for quite a long time before AI became such a well-known kind of household term, if you will, the concept of machine learning in terms of image processing is something that has been around for some time. And so, this is actually a form of AI that we use in order to process millions of images. My lab has by produced probably more than 20 million images over the last few years, maybe five to six years. And so, if you can imagine it's impossible for any human to process this many images and make sense of them. So of course, we've been using machine learning that is becoming increasingly more and more sophisticated and advanced in terms of being able to do analysis of images, which is a lot of what we cell biologists do, of course.Anna Greka (30:06):And so, there's multiple different kinds of perturbations that we do to cells, whether we're using CRISPR or base editing to make, for example, genome wide or genome scale perturbations or small molecules as we have done as well in the past. These are all ways in which we are then using machine learning to read out the effects in images of cells that we're looking at. So that's one way in which machine learning is used in our daily work, of course, because we study misshape and mangled proteins and how they are recognized by these cargo receptors. We also use AlphaFold pretty much every day in my lab. And this has been catalytic for us as a tool because we really are able to accelerate our discoveries in ways that were even just three or four years ago, completely impossible. So it's been incredible to see how the young people in my lab are just so excited to use these tools and they're becoming extremely savvy in using these tools.Anna Greka (31:06):Of course, this is a new generation of scientists, and so we use AlphaFold all the time. And this also has a lot of implications of course for some of the interventions that we might think about. So where in this cargo receptor complex that we study for example, might we be able to fit a drug that would disrupt the complex and lead the cargo tracks into the lysosome for degradation, for example. So there's many ways in which AI can be used for all of these functions. So I would say that if we were to organize our thinking around it, one way to think about the use of machine learning AI is around what I would call understanding biology in cells and what in sort of more kind of drug discovery terms you would call target identification, trying to understand the things that we might want to intervene on in order to have a benefit for disease.Anna Greka (31:59):So target ID is one area in which I think machine learning and AI will have a catalytic effect as they already are. The other of course, is in the actual development of the appropriate drugs in a rational way. So rational drug design is incredibly enabled by AlphaFold and all these advances in terms of understanding protein structures and how to fit drugs into them of all different modalities and kinds. And I think an area that we are not yet harnessing in my group, but I think the Ladders to Cures Accelerator hopes to build on is really patient data. I think that there's a lot of opportunity for AI to be used to make sense of medical records for example and how we extract information that would tell us that this cohort of patients is a better cohort to enroll in your trial versus another. There are many ways in which we can make use of these tools. Not all of them are there yet, but I think it's an exciting time for being involved in this kind of work.Eric Topol (32:58):Oh, no question. Now it must be tough when you know the mechanism of these families disease and you even have a drug candidate, but that it takes so long to go from that to helping these families. And what are your thoughts about that, I mean, are you thinking also about genome editing for some of these diseases or are you thinking to go through the route of here's a small molecule, here's the tox data in animal models and here's phase I and on and on. Where do you think because when you know so much and then these people are suffering, how do you bridge that gap?Anna Greka (33:39):Yeah, I think that's an excellent question. Of course, having patients as our partners in our research is incredible as a way for us to understand the disease, to build biomarkers, but it is also exactly creating this kind of emotional conflict, if you will, because of course, to me, honesty is the best policy, if you will. And so, I'm always very honest with patients and their families. I welcome them to the lab so they can see just how long it takes to get some of these things done. Even today with all the tools that we have, of course there are certain things that are still quite slow to do. And even if you have a perfect drug that looks like it fits into the right pocket, there may still be some toxicity, there may be other setbacks. And so, I try to be very honest with patients about the road that we're on. The small molecule path for the toxic proteinopathies is on its way now.Anna Greka (34:34):It's partnered with a pharmaceutical company, so it's on its way hopefully to patients. Of course, again, this is an unpredictable road. Things can happen as you very well know, but I'm at least glad that it's sort of making its way there. But to your point, and I'm in an institute where CRISPR was discovered, and base editing and prime editing were discovered by my colleagues here. So we are in fact looking at every other modality that could help with these diseases. We have several hurdles to overcome because in contrast to the liver and the brain, the kidney for example, is not an organ in which you can easily deliver nucleic acid therapies, but we're making progress. I have a whole subgroup within the bigger group who's focusing on this. It's actually organized in a way where they're running kind of independently from the cell biology group that I run.Anna Greka (35:31):And it's headed by a person who came from industry so that she has the opportunity to really drive the project the way that it would be run milestone driven, if you will, in a way that it would be run as a therapeutics program. And we're really trying to go after all kinds of different nucleic acid therapies that would target the mutations themselves rather than the cargo receptors. And so, there's ASO and siRNA technologies and then also actual gene editing technologies that we are investigating. But I would say that some of them are closer than others. And again, to your question about patients, I tell them honestly when a project looks to be more promising, and I also tell them when a project looks to have hurdles and that it will take long and that sometimes I just don't know how long it will take before we can get there. The only thing that I can promise patients in any of our projects, whether it's Alzheimer's, blindness, kidney disease, all I can promise is that we're working the hardest we possibly can on the problem.Anna Greka (36:34):And I think that is often reassuring I have found to patients, and it's best to be honest about the fact that these things take a long time, but I do think that they find it reassuring that someone is on it essentially, and that there will be some progress as we move forward. And we've made progress in the very first discovery that came out of my lab. As I mentioned to you, we've made it all the way to phase II trials. So I have seen the trajectory be realized, and I'm eager to make it happen again and again as many times as I can within my career to help as many people as possible.The Paucity of Physician-ScientistsEric Topol (37:13):I have no doubts that you'll be doing this many times in your career. No, there's no question about it. It's extraordinary actually. There's a couple of things there I want to pick up on. Physician-scientists, as you know, are a rarefied species. And you have actually so nicely told the story about when you have a physician-scientist, you're caring for the patients that you're researching, which is, most of the time we have scientists. Nothing wrong with them of course, but you have this hinge point, which is really important because you're really hearing the stories and experiencing the patients and as you say, communicating about the likelihood of being able to come up with a treatment or the progress. What are we going to do to get more physician-scientists? Because this is a huge problem, it has been for decades, but the numbers just keep going lower and lower.Anna Greka (38:15):I think you're absolutely right. And this is again, something that in my leadership of the ASCI I have made sort of a cornerstone of our efforts. I think that it has been well-documented as a problem. I think that the pressures of modern clinical care are really antithetical to the needs of research, protected time to really be able to think and be creative and even have the funding available to be able to pursue one's program. I think those pressures are becoming so heavy for investigators that many of them kind of choose one or the other route most often the clinical route because that tends to be, of course where they can support their families better. And so, this has been kind of the conundrum in some ways that we take our best and brightest medical students who are interested in investigation, we train them and invest in them in becoming physician-scientists, but then we sort of drop them at the most vulnerable time, which is usually after one completes their clinical and scientific training.Anna Greka (39:24):And they're embarking on early phases of one's careers. It has been found to be a very vulnerable point when a lot of people are now in their mid-thirties or even late thirties perhaps with some family to take care of other burdens of adulthood, if you will. And I think what it becomes very difficult to sustain a career where one salary is very limited due to the research component. And so, I think we have to invest in our youngest people, and it is a real issue that there's no good mechanism to do that at the present time. So I was actually really hoping that there would be an opportunity with leadership at the NIH to really think about this. It's also been discussed at the level of the National Academy of Medicine where I had some role in discussing the recent report that they put out on the biomedical enterprise in the United States. And it's kind of interesting to see that there is a note made there about this issue and the fact that there needs to be, I think, more generous investment in the careers of a few select physician-scientists that we can support. So if you look at the numbers, currently out of the entire physician workforce, a physician-scientist comprised of less than 1%.Anna Greka (40:45):It's probably closer to 0.8% at this point.Eric Topol (40:46):No, it's incredible.Anna Greka (40:48):So that's really not enough, I think, to maintain the enterprise and if you will, this incredible innovation economy that the United States has had this miracle engine, if you will, in biomedicine that has been fueled in large part by physician investigators. Of course, our colleagues who are non-physician investigators are equally important partners in this journey. But we do need a few of the physician-scientists investigators I think as well, if you really think about the fact that I think 70% of people who run R&D programs in all the big pharmaceutical companies are physician-scientists. And so, we need people like us to be able to work on these big problems. And so, more investment, I think that the government, the NIH has a role to play there of course. And this is important from both an economic perspective, a competition perspective with other nations around the world who are actually heavily investing in the physician-scientist workforce.Anna Greka (41:51):And I think it's also important to do so through our smaller scale efforts at the ASCI. So one of the things that I have been involved in as a council member and now as president is the creation of an awards program for those early career investigators. So we call them the Emerging-Generation Awards, and we also have the Young Physician-Scientist Awards. And these are really to recognize people who are making that transition from being kind of a trainee and a postdoc and have finished their clinical training into becoming an independent assistant professor. And so, those are small awards, but they're kind of a symbolic tap on the shoulder, if you will, that the ASCI sees you, you're talented, stay the course. We want you to become a future member. Don't give up and please keep on fighting. I think that can take us only so far.Anna Greka (42:45):I mean, unless there's a real investment, of course still it will be hard to maintain people in the pipeline. But this is just one way in which we have tried to, these programs that the ASCI offers have been very successful over the last few years. We create a cohort of investigators who are clearly recognized by members of the ASCI is being promising young colleagues. And we give them longitudinal training as part of a cohort where they learn about how to write a grant, how to write a paper, leadership skills, how to run a lab. And they're sort of like a buddy system as well. So they know that they're in it together rather than feeling isolated and struggling to get their careers going. And so, we've seen a lot of success. One way that we measure that is conversion into an ASCI membership. And so, we're encouraged by that, and we hope that the program can continue. And of course, as president, I'm going to be fundraising for that as well, it's part of the role. But it is a really worthy cause because to your point, we have to somehow make sure that our younger colleagues stay the course that we can at least maintain, if not bolster our numbers within the scientific workforce.Eric Topol (43:57):Well, you outlined some really nice strategies and plans. It's a formidable challenge, of course. And we'd like to see billions of dollars to support this. And maybe someday we will because as you say, if we could relieve the financial concerns of people who have curiosity driven ideas.Anna Greka (44:18):Exactly.Eric Topol (44:19):We could do a lot to replenish and build a big physician-scientist workforce. Now, the last thing I want to get to, is you have great communication skills. Obviously, anybody who is listening or watching this.Eric Topol (44:36):Which is another really important part of being a scientist, no less a physician or the hybrid of the two. But I wanted to just go to the backstory because your TED Talk, which has been watched by hundreds of thousands of people, and I'm sure there's hundreds of thousands more that will watch it, but the TED organization is famous for making people come to the place a week ahead. This is Vancouver used to be in LA or Los Angeles area and making them rehearse the talk, rehearse, rehearse, rehearse, which seems crazy. You could train the people there, how to give a talk. Did you have to go through that?Anna Greka (45:21):Not really. I did rehearse once on stage before I actually delivered the talk live. And I was very encouraged by the fact that the TED folks who are of course very well calibrated, said just like that. It's great, just like that.Eric Topol (45:37):That says a lot because a lot of people that do these talks, they have to do it 10 times. So that kind of was another metric. But what I don't like about that is it just because these people almost have to memorize their talks from giving it so much and all this coaching, it comes across kind of stilted and unnatural, and you're just a natural great communicator added to all your other things.Anna Greka (46:03):I think it's interesting. Actually, I would say, if I may, that I credit, of course, I actually think that it's important, for us physician-scientists, again, science and research is a public good, and being able to communicate to the public what it is that we do, I think is kind of an obligation for the fact that we are funded by the public to do this kind of work. And so, I think that's important. And I always wanted to cultivate those communication skills for the benefit of communicating simply and clearly what it is that we do in our labs. But also, I would say as part of my story, I mentioned that I had the opportunity to attend a special school growing up in Greece, Anatolia, which was an American school. One of the interesting things about that is that there was an oratory competition.Anna Greka (46:50):I got very early exposure entering that competition. And if you won the first prize, it was in the kind of ancient Rome way, first among equals, right? And so, that was the prize. And I was lucky to have this early exposure. This is when I was 14, 15, 16 years old, that I was training to give these oratory speeches in front of an audience and sort of compete with other kids who were doing the same. I think these are just wonderful gifts that a school can give a student that have stayed with me for life. And I think that that's a wonderful, yeah, I credit that experience for a lot of my subsequent capabilities in this area.Eric Topol (47:40):Oh, that's fantastic. Well, this has been such an enjoyable conversation, Anna. Did I miss anything that we need to bring up, or do you think we have it covered?Anna Greka (47:50):Not at all. No, this was wonderful, and I thoroughly enjoyed it as well. I'm very honored seeing how many other incredible colleagues you've had on the show. It's just a great honor to be a part of this. So thank you for having me.Eric Topol (48:05):Well, you really are such a great inspiration to all of us in the biomedical community, and we'll be cheering for your continued success and thanks so much for joining today, and I look forward to the next time we get a chance to visit.Anna Greka (48:20):Absolutely. Thank you, Eric.**************************************Thanks for listening, watching or reading Ground Truths. Your subscription is greatly appreciated.If you found this podcast interesting please share it!That makes the work involved in putting these together especially worthwhile.All content on Ground Truths—newsletters, analyses, and podcasts—is free, open-access.Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. And such support is becoming more vital In light of current changes of funding and support for biomedical research at NIH and other US governmental agencies.Thanks to my producer Jessica Nguyen and to Sinjun Balabanoff for audio and video support at Scripps Research. Get full access to Ground Truths at erictopol.substack.com/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.

Google's Impact on Health Report 2025 reveals the company's extensive influence on global digital health, including their Nobel Prize-winning AlphaFold 2 AI system. The value of watches that detect falls and raises the alarm just got a significant upgrade with Google receiving FDA clearance for "loss of pulse detection" technology for wearables that can identify sudden cardiac arrest signs remotely. New Australian study finds social media influencers driving demand for unnecessary health tests with limited clinical evidence. Commercialisation, misinformation and the rise in health equity gaps. Reporting by Pulse+IT. Tina Purnat's BMJ Opinion Piece is a must read for those interested in the proliferation of misinformationWas Chris Longhurst right years earlier than he predicted – the US has proposed legislation that would classify AI as a "practitioner licensed by law" with prescription capabilities, raising significant ethical and regulatory questions. The UK is exploring implementation of a single digital patient record system across health and social care, promising better continuity but facing logistical challenges. Will they pull this off?Check out the Global Digital Health Partnership's digital health repository LinkOur guest of Pulse is Dr Karen DeSalvo, Chief Health Officer at Google. Part 1 of our chat covers Karen's impressive career trajectory and personal motivations, AI, and how Google is focused on getting correct health information into the hands of everybody. Follow Karen on LinkedIn LinkVisit Pulse+IT.news to subscribe to breaking digital news, weekly newsletters and a rich treasure trove of archival material. People in the know, get their news from Pulse+IT – Your leading voice in digital health news.Follow us on LinkedIn Louise | George | Pulse+ITFollow us on BlueSky Louise | George | Pulse+ITSend us your questions pulsepod@pulseit.newsProduction by Octopod Productions | Ivan Juric

Hugo Penedones é licenciado em Engenharia Informática e Computação pela Universidade do Porto e é cofundador e atualmente CTO da Inductiva.AI, uma empresa de Inteligência Artificial para a ciência e engenharia. Anteriormente, passou pela Google DeepMind, onde foi membro fundador do projeto AlphaFold, um algoritmo de previsão de estruturas de proteínas que viria a revolucionar a ciência nesta área a levar a atribuição do Prémio Nobel de Química de 2024 a Demis Hassabis e John M. Jumper (David Baker foi o 3º laureado com o Nobel). Ao longo da sua carreira, trabalhou em diversas áreas, incluindo visão por computador, pesquisa web, bioinformática e aprendizagem por reforço em instituições de investigação como o Idiap e a EPFL na Suíça. _______________ Índice: (0:00) Início (3:30) PUB (3:54) IA aplicada à Ciência | Projecto Alphafold (Google Deepmind) | Paper em que o convidado foi co-autor (14:01) Alphafold vs LLMs (ex: ChatGPT) | AlphaGo (22:20) Como num hackathon com o Hugo e dois colegas começou o Alphafold | Demis Hassabis (CEO da Deepmind) (28:31) Outras aplicações de AI na ciência: fusão nuclear, previsão do tempo (41:14) IA na engenharia de materiais: descoberta de novos materiais e o potencial dos supercondutores (46:35) IA cientista: Poderá a IA formular hipóteses científicas no futuro? | Matemática | P vs NP (57:10 ) Modelos de machine learning são caixas negras? (1:03:12) Inductiva, a startup do convidado dedicada a simulações numéricas com machine learning (1:13:47) A promessa da computação quântica Cortar de 1:14:44 a 1:16:38 (assegura pf que fica silêncio no final, antes de eu fazer a pergunta seguinte, que muda de tema) (1:16:03) Desafios da qualidade dos dados na ciência com IA | Será possível simularmos uma célula? (1:24:44) Que progressos podemos esperar da IA na ciência nos próximos 10 anos? | Alphacell ______________ Esta conversa foi editada por: João RibeiroSee omnystudio.com/listener for privacy information.

On this week's episode of No Priors, Sarah Guo is joined by leading members of the teams at Vevo Therapeutics and the Arc Institute – Nima Alidoust, CEO/Co-Founder at Vevo Therapeutics; Johnny Yu, CSO/Co-Founder at Vevo Therapeutics; Patrick Hsu, CEO/Co-Founder at Arc Institute; Dave Burke, CTO at Arc Institute; and Hani Goodarzi, Core Investigator at Arc Institute. Predicting protein structure (AlphaFold 3, Chai-1, Evo 2) was a big AI/biology breakthrough. The next big leap is modeling entire human cells—how they behave in disease, or how they respond to new therapeutics. The same way LLMs needed enormous text corpora to become truly powerful, Virtual Cell Models need massive, high-quality cellular datasets to train on. In this episode, the teams discuss the groundbreaking release of the Tahoe-100M single cell dataset, Arc Atlas, and how these advancements could transform drug discovery. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @Nalidoust | @IAmJohnnyYu | @PDHsh | @Davey_Burke | @Genophoria Download the Tahoe Dataset Show Notes: 0:00 Introduction 1:40 Significance of Tahoe-100M dataset 4:22 Where we are with virtual cell models and protein language models 10:26 Significance of perturbational data 17:39 Challenges and innovations in data collection 24:42 Open sourcing and community collaboration 33:51 Predictive ability and importance of virtual cell models 35:27 Drug discovery and virtual cell models 44:27 Platform vs. single hypothesis companies 46:05 Rise of Chinese biotechs 51:36 AI in drug discovery

La tertulia semanal en la que repasamos las últimas noticias de la actualidad científica. En el episodio de hoy: Cara A: -Regalo: Arranca “El Café de Ganimedes”, nuestro nuevo pódcast (5:00) -Nuevo récord en fusión, WEST logra 1337 segundos (EAST logró 1066, ref. ep497) (21:00) -La astronomía multimensajero definitiva (GW + FRB + neutrino) (26:00) -Dudas sobre el origen de la peste negra (ref. ep377) (31:00) -AlphaFold falla en proteínas metamórficas (ref. ep438) (36:00) -Escribas del Antiguo Egipto y marcadores óseos específicos asociados al riesgo ocupacional (46:00) Este episodio continúa en la Cara B. Contertulios: María Ribes, Sara Robisco, Juan Carlos Gil, 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

V epizodi 170 je bil gost dr. Boštjan Kobe, znanstvenik, ki je diplomiral iz kemije na Univerzi v Ljubljani, doktoriral v Teksasu in trenutno deluje v Avstraliji. Njegovo raziskovalno področje je strukturna biologija, s poudarkom na proteinih in imunskem odzivu. V epizodi se dotakneva naslednjih tematik: Razvoj kristalografije Uporaba strukturne biologije Izvor življenja in evolucija Etična vprašanja pri genskem manipuliranju CRISPR tehnologija in njene posledice AlphaFold in prihodnost napovedovanja strukture proteinov ============= Obvladovanje matematike je ključ do odklepanja neštetih priložnosti v življenju in karieri. Zato je Klemen Selakovič soustvaril aplikacijo Astra AI. Ta projekt uteleša vizijo sveta, kjer se noben otrok ne počuti neumnega ali nesposobnega. Kjer je znanje človeštva dostopno vsakomur. Pridruži se pri revoluciji izobraževanja s pomočjo umetne inteligence. https://astra.si/ai/  

The 2024 Nobel Prize in Chemistry was shared by three researchers who used artificial intelligence to predict the three-dimensional shapes of proteins based on their amino acid sequences. In this episode, we hear from one of them, Demis Hassabis, CEO and co-founder of Google DeepMind. His AI program, AlphaFold, curates the 3D-structures of more than 200 million naturally-occurring proteins (https://alphafold.ebi.ac.uk/). This database is available to the public for free! After a brief introduction to the topic, we hear Dr. Hassabis's Nobel lecture on how he got involved in this groundbreaking research, and how he sees AI impacting biology in the future. Here is the link to the full public-domain lecture (with slides and charts): https://www.nobelprize.org/prizes/chemistry/2024/hassabis/lecture/ ‘Bench Talk: The Week in Science' is a weekly radio program that airs on WFMP Louisville FORward Radio 106.5 FM (forwardradio.org) every Monday at 7:30 pm, Tuesday at 11:30 am, and Wednesday at 7:30 am. Visit our Facebook page for links to the articles discussed in this episode: https://www.facebook.com/pg/BenchTalkRadio/posts/?ref=page_internal

durée : 00:02:18 - Le grand format - En permettant de prédire la structure d'une protéine à partir d'une séquence d'ADN, Alphafold, le système mis au point par Google DeepMind, a ouvert en grand le champ des possibles pour les chercheurs. Rencontre avec Aude Bernheim, microbiologiste à l'Institut Pasteur.

We're experimenting and would love to hear from you!In this episode of 'Discover Daily', we delve into the groundbreaking advancements in AI-driven drug discovery, highlighting DeepMind's AlphaFold 3 and significant partnerships between Isomorphic Labs and pharmaceutical giants Eli Lilly and Novartis. The episode explores how these collaborations, backed by substantial investments reaching into billions, are revolutionizing the development of small molecule therapeutics and accelerating the traditionally lengthy drug development process.We then shift to Finland's historic entry into the Artemis Accords as the 53rd nation and first signatory of 2025, marking a significant milestone in international space cooperation. This strategic move not only demonstrates Finland's commitment to peaceful space exploration but also positions the country to benefit from potential investment returns and enhanced polar region monitoring capabilities in response to increasing climate change impacts.The episode concludes with a detailed examination of a new executive order mandating the declassification of remaining files related to the assassinations of JFK, RFK, and MLK Jr. This unprecedented move towards transparency sets specific timelines for intelligence agencies to develop comprehensive release plans, potentially unveiling new insights into these pivotal moments in American history while addressing decades of public interest in these historical events.From Perplexity's Discover Feed: https://www.perplexity.ai/page/ai-developed-drugs-coming-soon-KafDx1.USaWRvWfDBgYk.g https://www.perplexity.ai/page/finland-signs-artemis-accords-SJdroKJERvqYlwwVm9z_pQhttps://www.perplexity.ai/page/trump-signs-executive-order-to-rUYlBy8tR1yBhoZc8YhyCg Perplexity is the fastest and most powerful way to search the web. Perplexity crawls the web and curates the most relevant and up-to-date sources (from academic papers to Reddit threads) to create the perfect response to any question or topic you're interested in. Take the world's knowledge with you anywhere. Available on iOS and Android Join our growing Discord community for the latest updates and exclusive content. Follow us on: Instagram Threads X (Twitter) YouTube Linkedin

Can artificial intelligence redefine the future of drug development and clinical trials?  In this episode of Tech Talks Daily, I sit down with Dave Latshaw, Ph.D., the internationally recognized AI and machine learning expert who serves as CEO of BioPhy. Founded in 2019, BioPhy focuses on using AI to revolutionize the later stages of drug development, a critical yet often overlooked segment of the pharmaceutical pipeline. Dave shares insights into BioPhy's innovative platform, which combines scientific, clinical, and regulatory insights to predict clinical trial success and steer capital allocation. At the heart of BioPhy's approach is its patent-pending AI engine, BioLogic, and generative AI solution, BioPhyRx, designed to enhance clinical trial outcomes, reduce failure rates, and accelerate the time to market for life-saving drugs. Dave also explores how BioPhy's operational assessment model prioritizes immediate ROI by addressing challenges downstream from drug discovery. In our conversation, Dave delves into the complexities of AI adoption in pharma, including the challenges of scaling AI solutions, managing high computational costs, and overcoming stakeholder fears about job displacement. Drawing from his experience at Johnson & Johnson, where his AI innovations contributed to the global rollout of the COVID-19 vaccine, Dave reflects on lessons learned and the transformative potential of AI in healthcare. As we look ahead, Dave discusses the future of AI in reducing administrative burdens on clinicians, automating regulatory compliance, and enabling groundbreaking advancements like DeepMind's AlphaFold.  How can AI transform not just how we develop drugs but also the healthcare outcomes for millions of people worldwide? Tune in to find out, and share your thoughts on the role of AI in the future of medicine.

Episode 142Happy holidays! This is one of my favorite episodes of the year — for the third time, Nathan Benaich and I did our yearly roundup of all the AI news and advancements you need to know. This includes selections from this year's State of AI Report, some early takes on o3, a few minutes LARPing as China Guys……… If you've stuck around and continue to listen, I'm really thankful you're here. I love hearing from you. You can find Nathan and Air Street Press here on Substack and on Twitter, LinkedIn, and his personal site. Check out his writing at press.airstreet.com. Find me on Twitter (or LinkedIn if you want…) for updates on new episodes, and reach me at editor@thegradient.pub for feedback, ideas, guest suggestions. Outline* (00:00) Intro* (01:00) o3 and model capabilities + “reasoning” capabilities* (05:30) Economics of frontier models* (09:24) Air Street's year and industry shifts: product-market fit in AI, major developments in science/biology, "vibe shifts" in defense and robotics* (16:00) Investment strategies in generative AI, how to evaluate and invest in AI companies* (19:00) Future of BioML and scientific progress: on AlphaFold 3, evaluation challenges, and the need for cross-disciplinary collaboration* (32:00) The “AGI” question and technology diffusion: Nathan's take on “AGI” and timelines, technology adoption, the gap between capabilities and real-world impact* (39:00) Differential economic impacts from AI, tech diffusion* (43:00) Market dynamics and competition* (50:00) DeepSeek and global AI innovation* (59:50) A robotics renaissance? robotics coming back into focus + advances in vision-language models and real-world applications* (1:05:00) Compute Infrastructure: NVIDIA's dominance, GPU availability, the competitive landscape in AI compute* (1:12:00) Industry consolidation: partnerships, acquisitions, regulatory concerns in AI* (1:27:00) Global AI politics and regulation: international AI governance and varying approaches* (1:35:00) The regulatory landscape* (1:43:00) 2025 predictions * (1:48:00) ClosingLinks and ResourcesFrom Air Street Press:* The State of AI Report* The State of Chinese AI* Open-endedness is all we'll need* There is no scaling wall: in discussion with Eiso Kant (Poolside)* Alchemy doesn't scale: the economics of general intelligence* Chips all the way down* The AI energy wars will get worse before they get betterOther highlights/resources:* Deepseek: The Quiet Giant Leading China's AI Race — an interview with DeepSeek CEO Liang Wenfeng via ChinaTalk, translated by Jordan Schneider, Angela Shen, Irene Zhang and others* A great position paper on open-endedness by Minqi Jiang, Tim Rocktäschel, and Ed Grefenstette — Minqi also wrote a blog post on this for us!* for China Guys only: China's AI Regulations and How They Get Made by Matt Sheehan (+ an interview I did with Matt in 2022!)* The Simple Macroeconomics of AI by Daron Acemoglu + a critique by Maxwell Tabarrok (more links in the Report)* AI Nationalism by Ian Hogarth (from 2018)* Some analysis on the EU AI Act + regulation from Lawfare Get full access to The Gradient at thegradientpub.substack.com/subscribe

This and all episodes at: https://aiandyou.net/ . Here to give us insights into some of the really cool stuff Google DeepMind is doing is Alexandra Belias, Head of product policy & partnerships. She serves as a bridge between DeepMind's product policy organization and the policy community. She previously led their international public policy work. She has an MPA in Economic Policy from LSE and is currently a tech fellow at the Harvard Carr Center for Human Rights. We talk about Google DeepMind's science policy, the emerging network of national AI safety institutes, the tension between regulation and innovation, AlphaFold and its successors, AlphaMissense and AlphaProteo, their SynthID watermarking detection tool, reducing contrail pollution through AI, and safety frameworks for frontier AI. All this plus our usual look at today's AI headlines. Transcript and URLs referenced at HumanCusp Blog.          

Nathan welcomes back computational biochemist Amelie Schreiber for a fascinating update on AI's revolutionary impact in biology. In this episode of The Cognitive Revolution, we explore recent breakthroughs including AlphaFold3, ESM3, and new diffusion models transforming protein engineering and drug discovery. Join us for an insightful discussion about how AI is reshaping our understanding of molecular biology and making complex protein engineering tasks more accessible than ever before. Help shape our show by taking our quick listener survey at https://bit.ly/TurpentinePulse SPONSORS: Shopify: Shopify is the world's leading e-commerce platform, offering a market-leading checkout system and exclusive AI apps like Quikly. Nobody does selling better than Shopify. Get a $1 per month trial at https://shopify.com/cognitive SelectQuote: Finding the right life insurance shouldn't be another task you put off. SelectQuote compares top-rated policies to get you the best coverage at the right price. Even in our AI-driven world, protecting your family's future remains essential. Get your personalized quote at https://selectquote.com/cognitive Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers13. OCI powers industry leaders with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before December 31, 2024 at https://oracle.com/cognitive Weights & Biases RAG++: Advanced training for building production-ready RAG applications. Learn from experts to overcome LLM challenges, evaluate systematically, and integrate advanced features. Includes free Cohere credits. Visit https://wandb.me/cr to start the RAG++ course today. CHAPTERS: (00:00:00) Teaser (00:00:46) About the Episode (00:04:30) AI for Biology (00:07:14) David Baker's Impact (00:11:49) AlphaFold 3 & ESM3 (00:16:40) Protein Interaction Prediction (Part 1) (00:16:44) Sponsors: Shopify | SelectQuote (00:19:18) Protein Interaction Prediction (Part 2) (00:31:12) MSAs & Embeddings (Part 1) (00:32:32) Sponsors: Oracle Cloud Infrastructure (OCI) | Weights & Biases RAG++ (00:34:49) MSAs & Embeddings (Part 2) (00:35:57) Beyond Structure Prediction (00:51:13) Dynamics vs. Statics (00:57:24) In-Painting & Use Cases (00:59:48) Workflow & Platforms (01:06:45) Design Process & Success Rates (01:13:23) Ambition & Task Definition (01:19:25) New Models: PepFlow & GeoAB (01:28:23) Flow Matching vs. Diffusion (01:30:42) ESM3 & Multimodality (01:37:10) Summary & Future Directions (01:45:34) Outro SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://www.linkedin.com/in/nathanlabenz/ Youtube: https://www.youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431

In this Three Breakthroughs episode of the Alumni Ventures Tech Optimist Podcast, host Mike Collins, Founder and CEO of Alumni Ventures, is joined by Senior Principal Naren Ramaswamy to explore three transformative advancements in artificial intelligence. The conversation covers the debate over AI scaling approaches, the revolutionary impact of AlphaFold 3 in molecular biology and drug discovery, and the rapid development of AI agents that promise to redefine productivity and trust. This episode offers an inspiring look at how these innovations are shaping the future of science, technology, and human collaboration.To Learn More:Alumni Ventures (AV)AV LinkedInAV Deep Tech FundTech OptimistLegal Disclosure:https://av-funds.com/tech-optimist-disclosuresCreators & Guests Mike Collins - Host Naren Ramaswamy - Guest

Can AI compress the yearslong 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.

Our 190th episode with a summary and discussion of last week's big AI news! Hosted by Andrey Kurenkov and Jeremie Harris. Note from Andrey: this one is coming out a bit later than planned, apologies! Next one will be coming out sooner. Feel free to email us your questions and feedback at contact@lastweekinai.com and/or hello@gladstone.ai Read out our text newsletter and comment on the podcast at https://lastweekin.ai/. Sponsors: The Generator - An interdisciplinary AI lab empowering innovators from all fields to bring visionary ideas to life by harnessing the capabilities of artificial intelligence In this episode: * OpenAI's pitch for a $100 billion data center and AI strategy plan outlines infrastructure and regulatory needs, emphasizing AI's foundational role akin to electricity.  * Google's Gemini model challenges OpenAI's dominance, showing strong performance in chatbot arenas alongside generative AI advancements.  * DeepMind's AlphaFold3 gets open-sourced for academic use, while new chips from NVIDIA and Google show significant performance boosts.  * Anthropic and TSMC updates highlight strategic funding, regulation influences, and the complex dynamics of AI hardware and international policy. If you would like to become a sponsor for the newsletter, podcast, or both, please fill out this form. Timestamps + Links: (00:00:00) Intro / Banter (00:02:44) News Preview (00:03:34) Sponsor Break Tools & Apps (00:04:36) OpenAI, Google and Anthropic Are Struggling to Build More Advanced AI (00:16:22) OpenAI Nears Launch of AI Agent Tool to Automate Tasks for Users (00:19:14) Google drops new Gemini model and it goes straight to the top of the LLM leaderboard (00:19:14)  Chinese AI startup takes aim at OpenAI's Sora with image-to-video tool launch (00:20:04) Introducing the Forge Reasoning API Beta and Nous Chat: An Evolution in LLM Inference Applications & Business (00:23:47) OpenAI Discusses AI Data Center That Could Cost $100 Billion (00:26:48) Elon Musk's massive AI data center gets unlocked — xAI gets approved for 150MW of power, enabling all 100,000 GPUs to run concurrently (00:29:34) Newest Google and Nvidia Chips Speed AI Training (00:34:45) Ex-OpenAI CTO Murati's New Team Takes Shape (00:34:45) Amazon Discussing New Multibillion-Dollar Investment in Anthropic Projects & Open Source (00:37:52) Google DeepMind open-sources AlphaFold 3, ushering in a new era for drug discovery and molecular biology (00:41:29) Near plans to build world's largest 1.4T parameter open-source AI model Research & Advancements (00:45:38) The Super Weight in Large Language Models (00:55:42) Compositional Abilities Emerge Multiplicatively: Exploring Diffusion Models on a Synthetic Task (01:03:47) Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models (01:08:14) Contextualized Evaluations: Taking the Guesswork Out of Language Model Evaluations Policy & Safety (01:11:14) The Code of Practice for general-purpose AI offers a unique opportunity for the EU (01:15:38) Three Sketches of ASL-4 Safety Case Components (01:23:05) U.S Department of Commerce finalizes $6.6 billion CHIPS Act funding for TSMC Fab 21 Arizona site , TSMC cannot make 2nm chips abroad now: MOEA (01:26:21) OpenAI to present plans for U.S. AI strategy and an alliance to compete with China (01:30:42) OpenAI loses another lead safety researcher, Lilian Weng (01:33:00) Outro

Can AI compress the yearslong 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 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.

Can AI compress the yearslong 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 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.

Episode 34: How will the 2024 election impact AI advancements by 2025? Matt Wolfe (https://x.com/mreflow) and Nathan Lands (https://x.com/NathanLands) dive into the implications of the upcoming Trump term. In this episode, Matt and Nathan discuss the potential AI developments over the next few years, how different political outcomes could shape AI progress, and the shifting landscape in Silicon Valley. They explore the latest in AI models like OpenAI 01, the debate over AGI timelines, and how regulatory approaches might impact America's competitive edge in tech. Check out The Next Wave YouTube Channel if you want to see Matt and Nathan on screen: https://lnk.to/thenextwavepd — Show Notes: (00:00) This won't become political; mood is changing. (04:55) Reduce government size, invest severance in tech. (08:28) AI can reveal corruption in government spending. (10:58) AI regulation may favor big companies, hinder startups. (14:50) Sam Altman expects AGI by 2025, despite skepticism. (17:59) AGI expected around 2025-2027, training slowing. (19:56) AI models don't learn in real-time conversations. (22:48) Humans struggle to foresee technological advancements' impact. (27:53) AGI leads to ASI due to intelligence. (29:39) Optimistic about AI and future advancements. (32:20) Predicts accurately, but often wrong on timing. — Mentions: Sam Altman: https://blog.samaltman.com/ OpenAI: https://openai.com/ Dario Amodei: https://www.linkedin.com/in/dario-amodei-3934934/ Anthropic: https://www.anthropic.com/ AlphaFold: https://alphafold.ebi.ac.uk/ Dogecoin: https://dogecoin.com/ — Check Out Matt's Stuff: • Future Tools - https://futuretools.beehiiv.com/ • Blog - https://www.mattwolfe.com/ • YouTube- https://www.youtube.com/@mreflow — Check Out Nathan's Stuff: Newsletter: https://news.lore.com/ Blog - https://lore.com/ The Next Wave is a HubSpot Original Podcast // Brought to you by The HubSpot Podcast Network // Production by Darren Clarke // Editing by Ezra Bakker Trupiano

Join Andy Harrison, a pioneer in computational biology, as he delves into the remarkable ways AI is revolutionizing drug discovery. Learn how tools like AlphaFold are accelerating the development of life-saving medicines by predicting protein structures with unprecedented accuracy. Discover the immense potential of AI in streamlining the pharmaceutical industry and improving patient outcomes. Learn more about your ad choices. Visit megaphone.fm/adchoices

Terminamos nuestro repaso a los premios Nobel de ciencias, como siempre, con el galardón de Química, que este año ha sido todo lo contrario de una sorpresa. Se lo han llevado tres de los candidatos más firmes: David Baker, "por diseñar nuevas proteínas mediante ordenador", y Demis Hassabis y John Jumper, "por sus métodos para predecir la estructura tridimensional de las proteínas". Jumper y Hassabis son los responsables de que exista AlphaFold, una inteligencia artificial de la que hemos hablado más de una vez en La Brújula, y que fue la primera en predecir la forma tridimensional de una proteína a partir de su secuencia de aminoácidos. Esto ha supuesto una revolución para la bioquímica, porque la secuencia de aminoácidos de las proteínas podemos "leerlas" en el ADN, y gracias a programas como éste ahora podemos pasar de "la letra" a "el objeto". Baker, por su parte, es uno de los padres de las técnicas informáticas para el estudio de proteínas, y es responsable de RoseTTAFold, el "competidor" de AlphaFold, que aunque llegó un poco más tarde también está siendo parte de esta revolución. En el programa de hoy repasamos muy rápido la relevancia de estas investigaciones, pero si queréis aprender más sobre ellas podéis volver a escuchar los capítulos s08e16 y s10e17 de este pódcast. También podéis buscar el episodio s05e10 de nuestro pódcast hermano, Aparici en Órbita. En todos ellos os hablamos de estas inteligencias artificiales en mucho más detalle. Este programa se emitió originalmente el 9 de octubre de 2024. Podéis escuchar el resto de audios de La Brújula en la app de Onda Cero y en su web, ondacero.es

What would you like to see more of? Let us know!In this episode of Discover Daily, we explore two major technological breakthroughs shaping our future. First, we discuss DeepMind's release of AlphaFold 3's source code, a significant advancement in protein structure prediction that promises to accelerate drug development by up to three years. With 76% accuracy in protein-ligand interactions and 65% accuracy in protein-DNA interactions, this open-source release marks a new era in computational biology and pharmaceutical research.We then delve into China's ambitious Solar Great Wall project, a massive renewable energy installation that stretches 400 kilometers across Inner Mongolia's desert landscape. This unprecedented initiative combines solar power generation with ecological restoration, featuring 196,000 solar panels in the Dalad Banner section alone. By 2030, the project aims to generate 180 billion kilowatt-hours annually, exceeding Beijing's current annual power consumption while simultaneously combating desertification.The dual-purpose design of the Solar Great Wall showcases how renewable energy projects can address multiple challenges simultaneously. Beyond power generation, the installation creates 50,000 new jobs, enables desert farming under the panels, and serves as a protective barrier for the Yellow River ecosystem. The project's innovative bifacial panels and AI-driven tracking systems demonstrate China's commitment to leading the global transition to sustainable energy.From Perplexity's Discover Feed:https://www.perplexity.ai/page/deepmind-releases-alphafold-co-jvNh2oy5TLyXE0SSrjYSCghttps://www.perplexity.ai/page/china-s-solar-great-wall-opa_xYm3RdO7j31AfESZuwPerplexity is the fastest and most powerful way to search the web. Perplexity crawls the web and curates the most relevant and up-to-date sources (from academic papers to Reddit threads) to create the perfect response to any question or topic you're interested in. Take the world's knowledge with you anywhere. Available on iOS and Android Join our growing Discord community for the latest updates and exclusive content. Follow us on: Instagram Threads X (Twitter) YouTube Linkedin

Send Everyday AI and Jordan a text messageYa know how in big cities there's a mix of cars and buses? It's a symbiotic symphony of private vehicles and public transportation sharing the road. Can AI be like that? Nik Marda, the Technical Lead, AI Governance at Mozilla joins us to help us build that path. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Nik questions on AI governanceUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:1. Public AI Explained2. Public AI Implementations and Limitations3. Trustworthiness and Concerns in AI Models.4. AI Integration in Daily Life5. Funding and Investment Needs for Public AITimestamps:00:00 Public AI promises a safer, smarter future.04:00 AI needs balance: commercial and public infrastructure.07:41 Public-private AI ecosystems can symbiotically coexist.12:34 Prioritize AI safety with smaller, quality datasets.15:38 Public AI models, data, and infrastructure needed.19:36 Public AI prioritizes trust, safety, accessibility, accountability.23:51 Public AI approaches offer control and cost benefits.27:56 Diversify AI sources for improved organizational resiliency.30:12 Public AI offers open-source, mission-focused alternatives.Keywords:Public AI, Private AI, AI application development, Llama, OpenAI, Google, investment in AI, public AI initiatives, Olmo, Falcon 40b, AlphaFold 2, Mozilla's Common Voice, National AI Research Resource, FAST, India AI, AI Factories, AI vendors, AI systems, AI Trust, AI Accountability, AI Governance, Mozilla, commercial AI infrastructure, sensitive data, public-private structures, closed proprietary AI models, open-source AI models, AI integration, incentive structure, proprietary AI systems. Get more out of ChatGPT by learning our PPP method in this live, interactive and free training! Sign up now: https://youreverydayai.com/ppp-registration/

AI's Influence on Creativity, Writing, and Learning: A Deep Dive with Naomi S. Baron   Join us in this insightful episode as we explore the profound impact of artificial intelligence on writing, creativity, and education with renowned linguist and author Naomi S. Baron. Delve into key discussions from her book, 'Who Wrote This: How AI and the Lure of Efficiency Threaten Human Writing,' highlighting both the potential benefits and ethical dilemmas of AI-generated content. Discover the complexity of copyright issues in the AI era, the importance of maintaining manual skills and personal touch in professional fields, and the significance of mental challenges in fostering authentic creativity. Learn about AI breakthroughs, such as AlphaFold in medicine, and real-world experiments like Google's Notebook LLM. This episode is a must-watch for anyone interested in the evolving role of AI in our lives, the protection of human authorship, and the vital interplay between technology and the human mind.   00:00 Introduction to AI Writing Tools 00:52 Meet the Expert: Naomi S. Baron 01:28 AI's Impact on Authorship and Creativity 03:08 The Deep Dive Experiment 06:05 Legal and Ethical Concerns 14:24 The Value of Human Creativity 28:46 The Struggle and Reward of Creativity 31:48 The Creative Struggle: Is It Necessary? 32:45 Artistic Mastery: From Bach to Picasso 35:44 Innovation and Discipline: Insights from Peter Compo 36:38 The Impact of AI on Education and Skills 42:13 The Importance of Personal Voice in Writing 44:35 The Physicality of Reading and Writing 54:35 The Future of Jobs in the Age of AI 01:01:51 Concluding Thoughts and Reflections

(0:00) Bestie intros! (3:18) The science behind Hurricanes Helene and Milton (14:59) The economics of intensifying natural disasters (29:03) AlphaFold creators win Nobel Prize in Chemistry (35:17) The Jayter's Ball (38:53) Google antitrust update: DOJ is going for a breakup (53:32) VC giveback: CRV will return ~$275M of a $500M fund to LPs (1:03:44) New TikTok survey shows increased usage as a news source (1:15:26) Election update: Are polling problems causing a strategy shift for Kamala Harris? Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@theallinpod Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg Intro Video Credit: https://x.com/TheZachEffect Referenced in the show: https://allin.com/meetups https://youtube.com/@allin https://allin.com/tequila https://allin.com https://x.com/Ry_Bass/status/1844367980249178396 https://www.newsweek.com/hurricane-helene-update-economic-losses-damage-could-total-160-billion-1961240 https://www.climate.gov/news-features/blogs/enso/september-2024-enso-update-binge-watch https://www.nature.com/articles/s43247-024-01442-3 https://x.com/vkhosla/status/1844166857655533811 https://www.aoml.noaa.gov/hrd/hrd_sub/sfury.html https://www.nature.com/articles/d41586-024-03214-7 https://www.bloomberg.com/news/articles/2024-10-09/us-says-it-s-weighing-google-breakup-as-remedy-in-monopoly-case https://assets.bwbx.io/documents/users/iqjWHBFdfxIU/rtKjE02hAh_k/v0 https://x.com/AOC/status/1844034727935988155 https://www.nytimes.com/2024/10/02/technology/crv-vc-fund-returning-money.html https://www.axios.com/2023/03/03/founders-fund-slashes-vc-peter-thiel https://www.pewresearch.org/short-reads/2024/09/17/more-americans-regularly-get-news-on-tiktok-especially-young-adults https://www.pewresearch.org/data-labs/2024/10/08/who-u-s-adults-follow-on-tiktok https://www.wsj.com/world/europe/russia-pays-criminals-to-sow-mayhem-in-europe-warns-u-k-spy-chief-21ab960c https://x.com/iapolls2022/status/1844418916107341948 https://x.com/2waytvapp/status/1844803367740096811 https://x.com/DavidSacks/status/1829383729284067659

This week, Geoffrey Hinton and Dennis Hassabis won Nobel Prizes in Physics and Chemistry for their groundbreaking AI work, sparking conversations about AI's influence across scientific disciplines. The episode explores Hinton's AI safety concerns and Hassabis' work on AlphaFold, transforming protein structure prediction. An unexpected set of honors, these awards spotlight AI's growing role in advancing research far beyond traditional computer science. Concerned about being spied on? Tired of censored responses? AI Daily Brief listeners receive a 20% discount on Venice Pro. Visit ⁠⁠⁠https://venice.ai/nlw⁠⁠⁠ and enter the discount code NLWDAILYBRIEF. The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614 Subscribe to the newsletter: https://aidailybrief.beehiiv.com/ Join our Discord: https://bit.ly/aibreakdown

The Nobel Prize in chemistry went to three scientists for groundbreaking work using artificial intelligence to advance biomedical and protein research. AlphaFold uses databases of protein structures and sequences to predict and even design protein structures. It speeds up a months or years-long process to mere hours or minutes. Amna Nawaz discussed more with one of the winners, Demis Hassabis. PBS News is supported by - https://www.pbs.org/newshour/about/funders

In this episode, Professor Hannah Fry sits down with Pushmeet Kohli, VP of Research at Google DeepMind to discuss AI's impact on scientific discovery. They go on a whirlwind tour of scientific projects, touching on recent breakthroughs in AlphaFold, material science, weather forecasting, and mathematics to better understand how AI can enhance our scientific understanding of the world.Further reading:Millions of new materials discovered with deep learningGraphCast: AI model for faster and more accurate global weather forecastingAlphaFold: A breakthrough unfolds (S2,E1)AlphaGeometry: An Olympiad-level AI system for geometryAI achieves silver-medal standard solving International Mathematical Olympiad problemsPresenter: Professor Hannah FrySeries Producer: Dan HardoonEditor: Rami Tzabar, TellTale Studios Commissioner & Producer: Emma YousifMusic composition: Eleni Shaw Camera Director and Video Editor: Tommy BruceAudio Engineer: Perry RogantinVideo Studio Production: Nicholas DukeVideo Editor: Bilal MerhiVideo Production Design: James BartonVisual Identity and Design: Eleanor TomlinsonProduction support: Mo Dawoud Commissioned by Google DeepMind Want to share feedback? Why not leave a review on your favorite streaming platform? Have a suggestion for a guest that we should have on next? Leave us a comment on YouTube and stay tuned for future episodes.

We may be on the cusp of a revolution in medicine, thanks to tools like AlphaFold, the technology for Google DeepMind, which helps scientists predict and see the shapes of thousands of proteins. How does AlphaFold work, what difference is it actually making in science, and what kinds of mysteries could it unlock? Today's guest is Pushmeet Kohli. He is the head of AI for science at DeepMind. We talk about proteins, why they matter, why they're challenging, how AlphaFold could accelerate and expand the hunt for miracle drugs, and what tools like AlphaFold tell us about the mystery of the cosmos and our efforts to understand it. If you have questions, observations, or ideas for future episodes, email us at PlainEnglish@Spotify.com. Host: Derek Thompson Guest: Pushmeet Kohli Producer: Devon Baroldi Learn more about your ad choices. Visit podcastchoices.com/adchoices