Podcasts about alphafold

Can an AI do the right thing for the wrong reason? Tim Scarfe speaks with Apollo Research's Alexander Meinke, Axel Højmark and Jérémy Scheurer about Measuring Reward-Seeking via Contrastive Belief Updates, their new research with OpenAI.The panel asks how models infer what graders reward, why good behaviour can come from the wrong reason, and whether that difference can be measured. The conversation moves through promise-breaking, grader awareness, reward hacking, scheming, opaque reasoning and corrigibility, then turns to a detailed walkthrough of the contrastive-belief method and what its results do and do not show. The o3 results discussed here concern an intermediate checkpoint without safety training.This episode was made in partnership with Apollo Research. MLST retained full editorial control.ReferenceApollo Research: https://www.apolloresearch.ai/---TIMESTAMPS:00:00:00 Cold Open00:02:12 Right Things, Wrong Reasons00:12:47 Grader Awareness00:26:22 Legibility00:32:35 What To Call It00:35:58 Intelligence, Agency, Anthropomorphism00:45:16 Apollo's Mission00:48:54 The End of the Exponential00:55:45 The Paper01:16:34 Closing Reflection---REFERENCES:tool:[00:00:08] Claude Fablehttps://www.anthropic.com/claude/fable[00:12:50] AlphaGo Zerohttps://deepmind.google/blog/alphago-zero-starting-from-scratch/[00:44:30] AlphaFold 3https://deepmind.google/science/alphafold/paper:[00:01:02] Measuring Reward-Seeking via Contrastive Belief Updateshttps://arxiv.org/abs/2607.18966[00:16:19] Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activationshttps://transformer-circuits.pub/2026/nla/[00:26:48] Stress Testing Deliberative Alignment for Anti-Scheming Traininghttps://arxiv.org/abs/2509.15541[00:35:33] Shortcut learning in deep neural networkshttps://arxiv.org/abs/2004.07780[00:53:49] Measuring AI Ability to Complete Long Software Taskshttps://arxiv.org/abs/2503.14499[00:59:52] Modifying LLM Beliefs with Synthetic Document Finetuninghttps://alignment.anthropic.com/2025/modifying-beliefs-via-sdf/[01:10:44] Alignment Faking in Large Language Modelshttps://arxiv.org/abs/2412.14093[01:13:55] Natural Emergent Misalignment from Reward Hackinghttps://www.anthropic.com/research/emergent-misalignment-reward-hackingother:[00:10:14] We Need a Science of Scheminghttps://www.apolloresearch.ai/science/science-of-scheming/[00:32:56] CoastRunners reward hacking examplehttps://deepmind.google/blog/specification-gaming-the-flip-side-of-ai-ingenuity/organization:[01:06:07] Redwood Researchhttps://www.redwoodresearch.org/---ReScript: https://app.rescript.info/share/718ab68e18cfa3b9b800da6b3290fd42

Business Insider founder Henry Blodgett unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Business Insider founder Henry Blodget unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Business Insider founder Henry Blodget unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Business Insider founder Henry Blodgett unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Business Insider founder Henry Blodget unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

It has already reassigned some members of the DeepMind project, while others have already left the company. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Recorded at the Battle of Ideas festival 2025 on Sunday 19 October at Church House, Westminster. ORIGINAL INTRODUCTION The past 20 years have seen astonishing advances in science, technology and medicine. Building upon the completion of the Human Genome Project at the turn of the century, scientists have developed ever more powerful ways to sequence and study DNA, enabling us to better understand genetic diseases and develop pioneering treatments. Now, AI tools such as the AlphaFold program developed by Google DeepMind are also providing unprecedented understanding of the human proteome – the complete set of proteins made from instructions in our DNA. As well as acquiring powerful ways to study DNA, we have also acquired powerful ways to change it. CRISPR genome editing, which enables us to make precise changes to the DNA of humans and other organisms, is now widely used in laboratories across the world and has been used in life-saving treatments for devastating diseases. Meanwhile, stem-cell research has advanced to the point where it is now possible to create structures resembling early human embryos entirely from stem cells, instead of having to begin by fertilising an egg cell with a sperm cell. There is even speculation about one day being able to bypass pregnancy altogether. Some are thrilled about the new possibilities opened up by these developments, while others worry about human life being mechanised in ways that seem distasteful. Genetics, genomics, neuroscience and psychology can be (mis)used to seek to reduce human beings to brain circuits and physiological mechanisms, which can then be managed via modification, drugs or nudge-based policy. Who gets to decide what counts as progress, and who gets a say in how science is governed? What happens when the (in)famous Silicon Valley dictum ‘Move fast and break things' meets biology? What can be learned from incidents such as the He Jiankui scandal of 2018, in which a Chinese scientist who worked on human embryos in secret breached scientific and ethical standards, resulting in the birth of three children with edited genomes? How can oversight and rules be imposed, when the science is so complex and fast-moving, and when the world is made up of diverse (and in some cases warring) nation states? Over the past two decades, Sandy Starr of the Progress Educational Trust and Dr Stuart Derbyshire of the National University of Singapore have been involved in numerous national and international deliberations on science, ethics, policy and law in these areas. At this breakfast banter, they will compare their experiences, exchange insights and invite questions. DISCUSSANTS Dr Stuart Derbyshire associate professor, deputy head of psychology, National University of Singapore Sandy Starr deputy director, Progress Educational Trust; author, AI: Separating Man from Machine

Business Insider founder Henry Blodget unpacks why the era of news aggregation is finished and why journalists must adapt fast as AI redefines both reporting and analysis. If you care about the future of information, you'll want to hear this. Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident Sam Altman says we are in the singularity: 'This is the moment' AI arms race in line for a reckoning after OpenAI hacking incident Senior White House official claims China's K3 model stolen from Anthropic OpenAI makes ChatGPT Health available to all US users A.I. Companies Are Recruiting Electricians and Carpenters by the Thousands Trump administration to ban new Chinese robots and inverters, protecting U.S. AI Why AI Needs a "Genie Coefficient" Behind the Curtain: The AI titans' biggest private fear The FTC Would Like To Decide Which AI Answers Are Too Woke, And Is Calling That Consumer Protection Google shuts down its Nobel-prize winning AlphaFold project as it focuses on Gemini DeepMind paper says LLMs won't be good at scientific discovery: LLMs can't jump Amazon overhauls its AI strategy, winding down most flagship models An ESP32 based plane radar The Tick That Hunts Down Its Hosts—Including Us prompt-injection resumes The McLuhan Marshalling Machine Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Henry Blodget Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Ősi óriás maradványaira bukkantak az apadó Duna medrében Kitiltották a kínai humanoidokat az USA-ból Bérelhető iPhone és Mac – elindult az Apple lízingprogramja Az út szélén fehéren világító fák azt üzenik, baj van A nagy Ram-csapda: hány Gb memóriára van valóban szükség manapság az okostelefonjában? Így verik át AI-jal és deepfake-kel a digitális befektetőket A mobilod is kaphat hőgutát - így védd meg tőle! Ariana Grande kiadatlan dalai a dark weben kötöttek ki – bárki megvehette őket Leállította a Nobel-díjat nyert AlphaFold-projektet a Google Deepmind Nyilvánosságra került egy csomó Claude-beszélgetés, mert nem védte megfelelően azokat az Anthropic Irányíthatatlanná vált és több online szolgáltatásra is lecsapott az OpenAI tesztelés alatt álló ügynöke Új eszközcsaládot fejleszt az OpenAI, hogy a gépelést felválthassák a beszélgetések A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ősi óriás maradványaira bukkantak az apadó Duna medrében Kitiltották a kínai humanoidokat az USA-ból Bérelhető iPhone és Mac – elindult az Apple lízingprogramja Az út szélén fehéren világító fák azt üzenik, baj van A nagy Ram-csapda: hány Gb memóriára van valóban szükség manapság az okostelefonjában? Így verik át AI-jal és deepfake-kel a digitális befektetőket A mobilod is kaphat hőgutát - így védd meg tőle! Ariana Grande kiadatlan dalai a dark weben kötöttek ki – bárki megvehette őket Leállította a Nobel-díjat nyert AlphaFold-projektet a Google Deepmind Nyilvánosságra került egy csomó Claude-beszélgetés, mert nem védte megfelelően azokat az Anthropic Irányíthatatlanná vált és több online szolgáltatásra is lecsapott az OpenAI tesztelés alatt álló ügynöke Új eszközcsaládot fejleszt az OpenAI, hogy a gépelést felválthassák a beszélgetések A további adásainkat keresd a podcast.hirstart.hu oldalunkon. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Ukraine is shifting its long-range drone campaign to focus on critical Russian infrastructure, and Big Tech companies are facing an AI dilemma as they report quarterly earnings. Plus, Google DeepMind is leaving behind its Nobel-winning AlphaFold project for new ventures, and PwC published “thought leadership” reports containing AI-generated hallucinations.Mentioned in this podcast:Ukraine adapts strikes on Russian energy industry to hit critical componentsChip stocks tumble as AI sell-off deepensGoogle DeepMind dismantles Nobel-winning AlphaFold team in strategy shiftPwC published ‘thought leadership' reports marred by AI hallucinations Listen to Unhedged on Apple Podcasts, Pocket Casts or Spotify.Save 10% on tickets to the FT Weekend Festival with the code FTPodcast. Visit ft.com/festival to find out more.Want to get in touch? Email us at podcasts@ft.comNote: The FT does not use generative AI to voice its podcasts The FT News Briefing is produced by Victoria Craig, Sonja Hutson, Saffeya Ahmed, Katya Kumkova, and Fiona Symon. Our editor is Marc Filippino. Our show is mixed by Sam Giovinco and Alex Higgins. Additional help from Gavin Kallmann, Michael Lello, Peter Barber and David da Silva. Our intern is Cole van Miltenburg. Our executive producer is Topher Forhecz. Flo Phillips is the FT's global head of audio. The show's theme music is by Metaphor Music. Read a transcript of this episode on FT.com Hosted on Acast. See acast.com/privacy for more information.

- In the updated post, the company said it has been finding "a small number of cases where the models identified and used publicly exposed credentials at the account-level on other publicly-available services" during its ongoing review. -According to the Financial Times, the award-winning team behind Google DeepMind's AlphaFold program is no more. -SpaceXAI has filed a lawsuit against Minnesota Attorney General Keith Ellison to challenge the state's new law that would ban apps and websites that can generate nonconsensual intimate images. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Send us Fan MailIf AI is already being used across the drug development pipeline, why hasn't its impact matched the investment?AI can help researchers review scientific literature, predict protein structures, prioritize molecules, assess toxicity, support clinical trials, and monitor adverse events. But access to better tools doesn't automatically create better drugs.In this episode, I speak with Thibault Geoui, Science CDO and host of the Tech & Drugs Podcast, about where AI is making a practical difference in drug discovery and development—and where the results remain limited. We map AI across the full drug development funnel, from basic research and target identification to preclinical testing, clinical trials, regulatory documentation, commercialization, and pharmacovigilance.We also discuss why digital-native tech-bio companies may be better positioned to benefit from AI than traditional pharmaceutical organizations. The difference isn't simply the model. It's how data, people, laboratory experiments, and AI tools are connected inside the workflow.For digital pathology professionals, the conversation becomes especially relevant when we examine AI-powered biomarker development, the role of pathology in pharmaceutical research, and the Roche–PathAI case discussed in the episode.And, of course, we talk about the problem every AI user eventually faces: an answer can look polished, specific, and completely convincing—and still be wrong.Episode Highlights00:00 — When convincing AI output creates more work Why AI can accelerate information generation while increasing the time required for review and verification.02:15 — From structural biology to science and technology leadership Thibault shares his background in X-ray crystallography, structural biology, scientific data, and digital product development.15:36 — Understanding the drug discovery and development funnel How thousands of potential compounds are narrowed down through discovery, preclinical research, clinical trials, and approval.20:00 — AI for scientific literature review How alerts, filtering, summarization, and information extraction can help researchers manage a rapidly growing scientific literature base.22:32 — AlphaFold and protein structure prediction What faster access to predicted protein structures changes for researchers—and why structural prediction alone doesn't solve drug discovery.24:13 — Searching an enormous chemical space How AI can help design and prioritize potential molecules for synthesis and experimental testing.25:50 — Predicting efficacy and toxicity Where AI supports preclinical research, why the models remain imperfect, and why experimental validation still matters.29:38 — Has AI changed drug development outcomes yet? A practical discussion about drug approval rates, AI investment, uneven returns, and the difference between deploying a tool and integrating it into a process.34:33 — Why traditional pharma struggles to scale AI Siloed data, legacy systems, organizational complexity, and the need to build reusable data workflows.37:57 — The “lab in the loop” model How tech-bio companies connect AI predictions with wet-lab experiments and feed the new data back into their models.44:37 — Can tech-bio companies shorten development timelines? How digital-native organizations are changing parts of the discovery and preclinical process.58:00 — AI, pharma, and digital pathology What the Roche–PathAI case discussed in the episode may indicate about the role of pathology data, biomarker discovery, and pharmaceutical workflows.01:06:17 — AI errors in regulated environments Why responsibility remains with the person or company submitting AI-assisted work, regardless of which tool produced it.01:17:37 — The growing cost of AI tools Subscriptions, token limits, model selection, AI orchestrators, and the need to use expensive tools more intentionally.01:27:50 — What successful AI adoption requires Starting with focused pilots, training scientists and technologists together, and treating implementation as organizational change.01:30:26 — The AI quirks that still frustrate users Hallucinated information, ignored writing instructions, stylistic habits, and poor awareness of time and context.The episode's timestamped themes and examples are documented in the supplied summary. The broader discussion covers AI from literature mining and molecular design through clinical development and post-market monitoring. Resources Mentioned Thibault Geoui's LinkedIn profile Tech & Drugs PodcastMIT NANDA study on generative AI implementation and return on investment Insilico Medicine as an example of a digital-native tech-bio company AI is already changing how scientific work gets done. The bigger question is whether organizations can redesign their workflows, train their teams, and maintain the human oversight needed to use it well.Listen to the full episode for a practical look at AI in drug discovery, drug development, and digital pathology.Support the showGet the "Digital Pathology 101" FREE E-book and join us!

Este podcast está patrocinado por Qonto.Si tienes una empresa, sabes que uno de los principales retos es poder mantener el control y ver claras tus finanzas. Pagos por un lado, cobros por otro, facturas en otra plataforma… Con Qonto, centralizas todas las finanzas en un solo lugar: cuenta de empresa remunerada, tarjetas para ti y para tu equipo, gestión de gastos y facturación integradas. Crear tu cuenta, aprobar un gasto, emitir una factura… todo rápido, en una misma solución. Obtén la claridad y el control financiero que necesitas. Abre tu cuenta hoy y empieza gratis.https://qonto.com/es¿Puede la inteligencia artificial reducir a la mitad el tiempo necesario para desarrollar un medicamento? Pedro Coelho, fundador de Biorce, explica cómo su compañía está construyendo un sistema operativo de IA capaz de acelerar el diseño y la gestión de ensayos clínicos.En este episodio hablamos de un sector en el que llevar un medicamento desde el laboratorio hasta la farmacia puede tardar 12 años y costar miles de millones. Pedro cuenta cómo Biorce puede convertir procesos que antes requerían documentos de más de 100 páginas, equipos de 20 personas y meses de trabajo en tareas realizadas en minutos. También profundizamos en los modelos especializados de IA, AlphaFold, la simulación de moléculas, la seguridad de los datos farmacéuticos y el futuro de una industria en la que las máquinas podrían comunicarse directamente con los reguladores. Pero la historia de Biorce también es personal. Tras perder a su padre por un melanoma y comprobar cómo los retrasos podían decidir quién accedía a tiempo a un tratamiento, Pedro dejó su anterior empresa para intentar reconstruir desde cero el sistema de ensayos clínicos. Desde entonces, Biorce ha levantado una ronda de 52,5 millones, ha firmado contratos multimillonarios y aspira a convertirse en la infraestructura tecnológica de las principales farmacéuticas.

Welcome to episode #1044 of Thinking With Mitch Joel (formerly Six Pixels of Separation). Steve Brown has spent more than twenty-five years helping organizations prepare for the future of technology. A former Senior Director and in-house futurist at Google DeepMind during the launch of AlphaFold, former Chief Evangelist and Futurist at Intel, entrepreneur and advisor, Steve has worked with global brands including Nike, JPMorgan Chase, Samsung, Disney and more to navigate digital transformation and the rapidly evolving AI landscape. His latest book, The AI Ultimatum - Preparing For A World Of Intelligent Machines And Radical Transformation, argues that AI is not another technology deployment but a fundamental organizational transformation that will reshape business over the next decade. In this episode, Steve explains why simply licensing AI tools is only the beginning, outlining the three stages organizations must navigate as they evolve toward becoming truly AI-first. We discuss the growing role of AI agents and digital employees, why leaders must rethink workflows instead of simply automating existing processes, and how organizations can prepare employees for a future where humans and intelligent machines work side by side. Steve also explores the importance of data strategy, change management, and responsible AI governance, while tackling difficult questions about surveillance, workforce anxiety, human creativity, and whether AI represents another technology cycle or something fundamentally different. Throughout the conversation, he makes the case that the real challenge is not adopting AI for efficiency alone, but reimagining how organizations create value in a world where intelligence itself has become programmable. Enjoy the conversation… Running time: 1:01:14. Hello from beautiful Montreal. Listen and subscribe over at Apple Podcasts. Listen and subscribe over at Spotify. Please visit and leave comments on the blog - Thinking With Mitch Joel. Feel free to connect to me directly on LinkedIn. Check out ThinkersOne. Here is my conversation with Steve Brown. The AI Ultimatum - Preparing For A World Of Intelligent Machines And Radical Transformation. Steve's Blog. Follow Steve on LinkedIn. Chapters: (00:00) - Navigating the AI Transformation. (06:06) - The Three Steps of AI Adoption. (08:47) - AI in Journalism: The Human-Machine Collaboration. (12:02) - Consumer Perception of AI-Generated Content. (18:03) - The AI Ultimatum: Embrace or Be Left Behind. (21:10) - The Role of Responsibility in AI Adoption. (24:00) - The Future of Digital Employees. (29:42) - The Future of Remote Work and AI Avatars. (30:05) - Managing Digital Employees: A New Paradigm. (32:35) - Types of AI Agents and Their Impact on Work. (34:34) - Evolving Roles: From Accountants to Financial Advisors. (37:09) - The Burnout Dilemma: Technology vs. Human Capacity. (39:11) - Involving Employees in AI Design Processes. (41:03) - Resistance to AI: Fear of Replacement and Identity. (44:42) - Leadership Perspectives on AI Integration. (46:36) - Creating Super Teams: The New Managerial Role. (48:04) - Case Study: Nvidia's AI Workforce Strategy. (50:10) - Early Successes in AI Deployment. (52:19) - Surveillance vs. Performance Measurement in AI. (56:25) - The Need for a Healthier Work Ecosystem. (58:24) - A Shift in Thinking: The Rise of OpenClaw.

In this episode, Professor Paola Cinnella - Professor of Fluid Mechanics at Sorbonne University and Director of the Sorbonne Cluster for Artificial Intelligence (SCAI) - joins Neil to discuss her path from classical fluid mechanics and high-order numerical methods into uncertainty quantification, Bayesian methods, data-driven turbulence modeling and AI for Science.Paola has built a career at the intersection of CFD, compressible and turbulent flows, dense gas dynamics, uncertainty quantification, robust optimization and machine learning. We discuss academic careers, dense gases, RANS uncertainty, AirfRANS, surrogate modeling, scientific publishing, education in the age of AI, and the idea of the "centaur scientist".Key topicsFluid mechanics, CFD and high-order schemesDense gases, real-gas effects and expansion shockwavesUncertainty quantification and Bayesian methodsRANS turbulence-model uncertaintyAirfRANS and CFD datasets for machine learningTurbulence modeling vs surrogate modelingScientific publishing and ML-for-CFD standardsSCAI and AI for ScienceEducation, ChatGPT and centaur scientistsPapersQuantification of model uncertainty in RANS simulations: A review - Heng Xiao, Paola Cinnellahttps://doi.org/10.1016/j.paerosci.2018.10.001Discovery of Algebraic Reynolds-Stress Models Using Sparse Symbolic Regression - Martin Schmelzer, Richard P. Dwight, Paola Cinnellahttps://doi.org/10.1007/s10494-019-00089-xBayesian estimates of parameter variability in the k-epsilon turbulence model - W.N. Edeling, P. Cinnella, R.P. Dwight, H. Bijlhttps://doi.org/10.1016/j.jcp.2013.10.027AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutionshttps://arxiv.org/abs/2212.07564Data-driven turbulence modeling - Paola Cinnellahttps://arxiv.org/abs/2404.09074Direct numerical simulations of supersonic turbulent channel flows of dense gases - Luca Sciacovelli, Paola Cinnella, Xavier Gloerfelthttps://doi.org/10.1017/jfm.2017.237LinksPaola Cinnella named Director of SCAIhttps://scai.sorbonne-universite.fr/news/paola-cinnella-new-directorSCAIhttps://scai.sorbonne-universite.fr/Paola Cinnella - HAL publicationshttps://cv.hal.science/paola-cinnellaPaola Cinnella - Google Scholarhttps://scholar.google.com/citations?hl=fr&user=wBRA0JAAAAAJERCOFTAC SIG 54 - Machine Learning for Fluid Dynamicshttps://www.ercoftac.org/special_interest_groups/54-machine-learning-for-fluid-dynamics/master-of-science-internships/Chapters00:00 Podcast intro00:39 Introducing Prof. Paola Cinnella03:28 Conversation begins03:56 How Paola found fluid mechanics07:09 Moving from Italy to France08:37 High-order schemes and compressible flows09:30 Building an academic career12:06 Dense gases and uncertainty quantification15:16 Expansion shockwaves and real-gas effects19:17 Returning to Paris and academic mobility24:52 Academia, passion and persistence27:51 Bayesian methods and turbulence uncertainty30:47 Learning statistics across disciplines33:07 LearnFluidS, AirfRANS and CFD datasets36:33 Skepticism and physics in ML turbulence modeling40:41 Could ML lead to a universal turbulence model?42:59 Turbulence models, surrogate models and RANS45:03 Why LES alone cannot solve optimization47:15 Multi-fidelity modeling49:08 What Computers & Fluids looks for in ML-for-CFD papers54:05 CFD metrics vs machine-learning metrics57:13 Overselling, publication pressure and quality62:22 SCAI and AI for Science66:07 Cross-disciplinary AI for Science69:26 Education in the AI era72:44 Critical thinking and AI outputs78:15 AI as a companion, not a replacement81:42 AlphaFold and the future of discovery83:43 Training centaur scientists85:11 Closing thoughts

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This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at https://notion.com/mlstProtein folding stalled biology for fifty years. A sequence of amino acids dictates a three-dimensional shape, but reading that shape meant a year and roughly $100,000 of crystallography per structure. Then AlphaFold 2 won CASP14 so decisively the organizers called the problem essentially solved.In this documentary cut, John Jumper, who shared the 2024 Nobel Prize in Chemistry and has since left DeepMind for Anthropic, walks Tim Scarfe through what the system did and, more interestingly, what it did not. The architecture gets a proper dissection: MSAs, the Evoformer, invariant point attention, the FAPE loss, and Jumper's correction of the equivariance story, which ablations valued at roughly 2.5 of 30 GDT points rather than the whole win. He is blunt about the limits. AlphaFold predicts one experiment extraordinarily well; it is not a model of the cell, it does not capture dynamics, and on a given drug target it is "wrong nine times out of ten."From there: the AlphaFold Database of 200M+ predicted structures, AlphaFold 3 and ligands, Isomorphic Labs, and Jumper's quarrel with the bitter lesson, where finite data and human hypotheses still matter. Emmanuel Nji of BioStruct Africa closes the film on what changes when work that took years now takes months, and on training the next thousand structural biologists across Africa.---TIMESTAMPS:00:00:00 Cold open: predicting nature with a button press00:01:03 The protein folding bottleneck and CASP00:04:39 The Nobel, the database, and the move to Anthropic00:05:50 Sponsor (Notion) and framing: what AlphaFold does not claim00:07:39 Proteins as self-assembling nanomachines00:12:24 From structures to biology: drug discovery and Midnolin00:17:37 The humility of AlphaFold: a narrow predictor00:22:18 Inside the architecture: Evoformer, IPA and FAPE00:30:20 Ruthless empiricism: ablations and 100x in data00:35:20 Predict, control, understand00:40:00 Against the bitter lesson; AlphaFold 3 as diffusion00:45:07 Intelligence, representations and AGI00:49:23 Epilogue: AlphaFold in Africa00:52:16 Closing: the case for hybrid science models---REFERENCES:organization:[00:01:55] Critical Assessment of Structure Prediction (CASP)https://predictioncenter.org/[00:04:39] The Nobel Prize in Chemistry 2024https://www.nobelprize.org/prizes/chemistry/2024/summary/[00:05:18] BioStruct Africahttps://www.biostructafrica.org/[00:18:03] Isomorphic Labshttps://www.isomorphiclabs.com/paper:[00:03:09] AlphaFold Protein Structure Databasehttps://doi.org/10.1093/nar/gkab1061[00:17:25] Accurate structure prediction of biomolecular interactions with AlphaFold 3https://www.nature.com/articles/s41586-024-07487-w[00:22:18] Highly accurate protein structure prediction with AlphaFoldhttps://www.nature.com/articles/s41586-021-03819-2[00:23:10] Midnolin promotes degradation of substrates independent of ubiquitinationhttps://doi.org/10.1126/science.adh5021[00:27:00] Improved protein structure prediction using potentials from deep learninghttps://www.nature.com/articles/s41586-019-1923-7tool:[00:03:09] AlphaFold Protein Structure Database (EBI)https://alphafold.ebi.ac.uk/[00:45:55] AlphaEvolve: a coding agent for designing advanced algorithmshttps://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/other:[00:39:40] The Bitter Lessonhttp://www.incompleteideas.net/IncIdeas/BitterLesson.html---ReScript: https://app.rescript.info/share/d8cde5c221fb71e2c0f5aafe94f90dfaDisclaimer - not sponsored, editorial with us - we filmed it at GDM, London

When Demis Hassabis pitched DeepMind to a few venture capitalists back in 2010, the business plan was almost comically audacious. “Step one: Solve intelligence. Step two: Use it to solve everything else,” he recalls in a conversation at Stanford Graduate School of Business with Stanford University President Jonathan Levin. “And people were quite confused. But we really meant it.”Sixteen years later, the “broad arcs” of that plan have gone “unbelievably well,” says Hassabis, a chess prodigy turned video game developer turned neuroscientist turned Nobel Prize-winning AI pioneer. Today he's on a mission to create “the ultimate tool for science,” building on his decision to give away AlphaFold, the groundbreaking AI system that predicts the structures of proteins. The future, Hassabis says, is just around the corner: “Ten years from now, I think we'll realize that we were standing in the foothills of the singularity now.”AI@GSB, the Dean's Applied AI initiative at the Stanford Graduate School of Business (GSB), and Stanford Medical School hosted a conversation with Demis Hassabis, Co-founder and CEO of Google DeepMind, on the frontier of artificial intelligence and what it means for how we live, work, and flourish.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

On the Science pod, we've been covering a lot of the ground on how AI is revolutionizing STEM, but one of our favorite off the record topics since our launch is which field is harder to accelerate: math, bio, or physics? Today we're back in Materials Science land with Radical — Unlike biological molecules that can be represented (and predicted!) by token strings, the success of materials involve many more macro complex variables like supply chains, microstructures, and manufacturing processes. If you recall the LK99 drama of 2023, while the basic ingredients were known, part of the confusion came from the lack of disclosure around manufacturing, and therefore defeated reproducibility. There is probably no "one-shot" model capable of designing a material that works perfectly at scale.How Radical is accelerating materials discovery >10x the pace of DARPA/GE MACHJoseph Krause is a materials scientist through and through. And after spending his career watching industries stall out waiting for better materials, he founded Radical AI to do something about it.We recently sat down with Joseph to talk about Radical AI, materials discovery, self-driving labs, and the future of AI science. Joseph did not sugar coat anything: accelerating the materials discovery pipeline is a hard problem. But it's one that he strongly believes we need to invest in, for the future of consumer products, aerospace, computing, and defense, and get them into every day use:“We count it as a discovery when you pick up your phone and there's a new material sitting inside of it.”How does Joseph plan on accelerating the rate of discovery? To understand this, it's important to understand why this is such a hard problem in the first place. The first thing to keep in mind is that the material that is manufactured is far more than a chemical formula going into it. The process of mixing, annealing, growing, or generating the final material can result in wildly different outcomes. The entire materials discovery process, both from early discovery to large scale manufacturing, needs to be understood and characterized.The Self-Driving LabThis philosophy has grown into a key insight at Radical AI: The construction of the self-driving lab. This lab is one that is not just automated, but in fact uses an “AI scientist” that combines scientific knowledge, computational techniques, and human intuition to generate and test hypotheses in an automated lab. Creating an AI scientist was key to making Radical's self-driving labs work, since Joseph argues that no single AI model can one-shot materials.“In materials, the ground truth is the material itself. You have to be able to test it and characterize it.”Joseph talked at length about the self-driving labs at Radical. Joseph argues that experimental data is the true “moat” in this industry. An SDL functions as a closed-loop system where an AI scientist generates hypotheses, and automated robotics synthesize and characterize materials, running research campaigns in parallel rather than serially. The successes here were both on the automation side and on the science side. Radical has managed to scale their alloy discovery pipeline up to producing and characterizing 1200 alloys in six months — this nearly 10x speedup over the DARPA/GE MACH program that aimed to create 500 new alloys in a year. Joseph claims they can scale this up even more and estimates they can produce a hundred new alloys tested and characterized in a day. A truly new paradigm in high-throughput alloy experimentation.On the science side, their AI scientist proposed and tested 300 new materials, ten of which were found to have novel state-of-the-art properties that are already being further developed for commercial applications. The robustness of this first materials campaign reinforces Joseph's claim that the moat is the lab and data.“It's moved into elemental families or alloy families no one has ever published on before.”Interestingly, Radical's AI scientist has made some novel discoveries, expanding into elements that just were not explored prior. This is fascinating from a scientific perspective, but it's also important for helping reduce supply chain bottlenecks for vital industries!Joseph spent a lot of time in D.C. before founding Radical, and he's clear-eyed about the competitive threat. China's centralized model lets it stand up manufacturing hubs and immediately scale new materials from lab to production. We can't replicate that, and Joseph is very clear we shouldn't try. But we do need an answer. For Joseph, that means transforming the scientific workforce, investing in self-driving lab infrastructure at the national lab level, and leaning hard into public-private partnerships.“Now imagine every scientist in the United States doing 10 times the research output. That's fundamental. That just changes the trajectory of discovery.”Before we close, we'd like to give a shout out to Joseph and Radical for publishing and open sourcing much of their internal tooling pipeline. This includes:* TorchSim (preprint, blog): an open-source PyTorch-based MD simulation framework, which has been spun off into its own non-profit.* MATRIX/MATRIX-PT (preprint, blog): An open-source dataset for benchmarking autonomous self-driving labs (MATRIX), along with with an open source model based upon this dataset (MATRIX-PT). We could talk about this extensively, but a fun data point is that improving reasoning in the area of materials also improved reasoning for biological systems! This is a truly unexpected result.Big shout-out to the Radical team for sharing their work!Materials discovery has been stuck on a 20–30 year timeline for generations. Joseph thinks that's about to change, and Radical AI is putting that thesis to the test in the lab, one sample at a time.We had a great time talking with Joseph. We hope you give it a listen!Timestamps* 0:00 Introduction to the challenges of AI in material science* 0:52 Welcome and introduction to Joseph Krause and Radical AI* 1:38 Why Radical AI is different: The focus on experimental data and Self-Driving Labs (SDLs)* 6:19 The process: Candidate generation, synthesis, and characterization* 11:05 The application of exotic alloys in extreme environments (aerospace and defense)* 13:20 Barriers to entry: The slow process of qualification and manufacturing* 16:06 Supply chain constraints in material science* 19:24 Human-in-the-loop: Training the AI using scientific intuition* 20:35 The engineering challenges of automating a laboratory* 23:17 Defining the “Self-Driving Lab”: Research campaigns vs. just automation* 24:39 Mechanical challenges: Handling high-temperature samples* 27:41 Future scaling plans and the “Vertical Integration” strategy* 30:08 Validation timelines for high-tech industries (semiconductors, aerospace)* 31:47 The active learning loop and handling “negative results”* 35:32 AI exploring elemental families beyond human bias* 39:13 Throughput targets and the difference between AI and human exploration* 43:52 Why the dataset size is less critical than the quality of experimental feedback* 46:20 Addressing the lack of an “AlphaFold” for materials* 53:49 War stories from the lab: Building the infrastructure* 58:12 The shift in industry sentiment toward SDLs and tool interfaces* 1:01:14 Geopolitical considerations and the race in material science innovation* 1:06:12 Calls to action for ML and AI engineers: Rethinking the scientific stack* 1:09:53 The Matrix model and using VLM for scientific knowledge extraction* 1:13:10 Why Radical AI is open-sourcing their work This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.