Podcasts about neural networks

As AI agents become more capable and autonomous, they also introduce new security challenges. In this 'Fully Connected' episode, Dan and Chris unpack Anthropic's Zero Trust for AI Agents security framework and what it means for organizations deploying agentic systems. They examine the key security risks facing agentic systems and discuss how organizations can apply Zero Trust principles to deploy AI agents safely. Along the way, they break down practical security controls and discuss how traditional cybersecurity principles must evolve for the age of AI agents.Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks: Zero Trust for AI AgentsOWASP GenAI Project Sponsors:Prediction Guard: A self-hosted AI control plane for running agents in high impact environments. predictionguard.com/practicalaiUpcoming Events: Register for upcoming webinars here!Midwest AI Summit 2026

AI models can win math olympiads… but still struggle to read an analog clock. In this fully connected episode, Dan and Chris break down the latest Stanford AI Index Report and explore what it reveals about the current state of AI. They discuss AI adoption and safety, disappearing junior tech jobs, robotics, AI's “jagged frontier” of intelligence, and the growing race between the U.S. and China. Along the way, they debate whether AI should optimize everything, or if some things are better left human. Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:The 2026 AI Index ReportSponsors:Prediction Guard: A self-hosted AI control plane for running agents in high impact environments. predictionguard.com/practicalaiUpcoming Events: Register for upcoming webinars here!Midwest AI Summit 2026

I've decided that the biggest challenge we have in AI is now keeping track of the new words being created. Words like harness, layer, mesh, vector, orchestrator, tools, surface, memory – they all mean very special things. And engineers and marketing people keep dreaming up new ones (spine? pattern? control plane? MCP? LangChain? headless? MCP? mesh? ontology?). In this podcast I do my best to explain what these words mean, and give you a non-technical understanding of how all this stuff works. If people like this I'll keep you up to date on all these new words. Additional Information (Note that all our research and podcasts are in Galileo) AI Prices Are Going Up, Up, Up – And What This Means For Enterprise AI The Reinvention of Workday: From System of Record to Platform of Agents Could Microsoft Win The War For Enterprise AI? The AI vs. Labor Economy, Why Benefits Are Being Cut, The Role of Legacy Systems The Context Layer (Semantic Layer) In Enterprise AI (And Where Business Rules Go) Jensen Huang's Taipei Speech (filled with this jargon) The Superagent for HR: Galileo Mars Release Chapters (00:00:00) - The Trouble With Words in the AI Era(00:07:28) - Three Words of the Real-World Model (RAG, M(00:10:16) - Hiring with a Neural Network(00:16:27) - What is the Microsoft SQL Server Fabric or Mesh?(00:18:07) - The issue of governance in the HCM(00:19:36) - A Little More About Machine Learning

What happens when AI agents start acting less like chatbots and more like coworkers? In this episode, Dan and Chris sit down with Craig McLuckie, CEO of Stacklok to explore MCP, Kubernetes, ToolHive, enterprise AI, and the emerging infrastructure powering AI-native applications. From identity management to agent orchestration and system architecture, this conversation dives into how organizations may soon manage entire fleets of AI agents working behind the scenes.Featuring:Craig McLuckie – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:StacklokToolhiveSponsors:Prediction Guard: A self-hosted AI control plane for running agents in high impact environments. predictionguard.com/practicalaiUpcoming Events: Register for upcoming webinars here!Midwest AI Summit 2026

Open Source AI is entering a new era, one shaped by self-improving AI Agents, recursive learning systems, and rapidly evolving AI Tools that blur the line between software and autonomous collaborators. In this episode, Daniel and Chris sit down with Nous Research co-founder and CTO Jeffrey Quesnelle to explore Hermes Agent. Along the way, they discuss models vs. harnesses, the changing role of developers, and one of the biggest questions facing the AI Future: what remains uniquely human as AI capabilities continue to accelerate?Featuring:Jeffrey Quesnelle – Website, LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:Nous ResearchHermes AgentSponsors:Framer: The enterprise-grade website builder that lets your team ship faster. Get 30% off at framer.com/practicalaiPrediction Guard: A self-hosted AI control plane for running agents in high impact environments. predictionguard.com/practicalaiUpcoming Events: Register for upcoming webinars here!Midwest AI Summit 2026

At Marketecture Live, Brad Fox, SVP, Health Media, dentsuX, joins Josh Walsh, Co-Founder & CEO, BranchLab, and Zach Rodgers, Founder, Sensical, to explore how AI and neural networks are reshaping healthcare advertising. From the rise of AI-driven patient behavior to the decline of traditional targeting methods, the conversation dives into privacy-safe audience strategies, evolving patient journeys, and what the future holds for pharma marketers in an AI-first world. Takeaways AI is rapidly changing how patients search for and consume health information Healthcare advertising is shifting away from search, contextual, and retargeting Neural networks enable privacy-safe prediction of patient populations Audience targeting must be rebuilt using AI-driven models Patient journeys are complex and require more nuanced segmentation AI is making both patients and doctors more informed True one-to-one personalization remains limited due to regulation AI platforms may eventually monetize healthcare interactions Chapters 00:00 Introduction to healthcare, AI, and advertising 00:18 Overview of the panel and discussion focus 01:04 AI as a primary tool for health information 01:53 Surge in AI-driven health queries and behavior shift 02:45 Changing role of search and health websites 03:40 Adoption of AI tools by physicians 04:31 Impact of AI on patient and doctor outcomes 06:33 Decline of traditional targeting and need for new strategies 07:40 Future of pharma ad spend in an AI-driven world 08:45 Neural networks and privacy-safe targeting explained 10:31 AI-driven audience targeting and patient lifecycle 12:28 Predictive modeling for healthcare populations 13:03 Importance of understanding patient journeys 15:57 Scaling AI audiences across media channels 17:11 Faster audience creation and activation 18:40 Personalization limits in healthcare marketing 20:12 Future of AI platforms and healthcare ads 22:01 Regulation and the future of pharma advertising Learn more about your ad choices. Visit megaphone.fm/adchoices

The sensei discuss the rise of young players Yagiz Erdogmus and Faustino Oro and the next generation of players and training.  Join the Dojo - https://chessdojo.club Watch Live - https://twitch.tv/chessdojo Play Chess - https://go.chess.com/chessdojo Merch - https://www.chessdojo.club/shop Want to support the channel? Patreon - https://patreon.com/chessdojo Donate - https://streamelements.com/chessdojo/tip Find all of our chess book & supplies recommendations (& more!) on our Amazon storefront: https://www.amazon.com/shop/chessdojo Shopping through our link is a great way to support the Dojo. We earn a small affiliate % but at no cost to you. Website: https://chessdojo.club Twitch: https://twitch.tv/chessdojo Discord: https://discord.gg/GhKsJtjpFw Twitter: https://twitter.com/chessdojo Patreon: https://patreon.com/chessdojo Instagram: https://www.instagram.com/chessdojo Facebook: https://www.facebook.com/chessdojo Podcast: https://chessdojotalks.podbean.com TikTok: https://tiktok.com/@/chessdojoclips 0:00 Intro 1:23 Erdogmas Breaks 2700 at 14 5:20 Proving Himself Against Elite Competition 10:42 Is This a New Era of Chess Talent? 16:17 Engines, Neural Networks, and Human Limits 22:57 How AI Changed Modern Chess Training 32:55 Fearless Juniors and the New Generation 46:11 Fino Oro Becomes GM at 12 56:29 Blitz Monsters and the Future of Chess

In a 1982 paper, the Nobel laureate created his namesake recurrent neural network—work that taught Maria Geffen to always ground research questions in biology.

In this fully connected episode, Dan and Chris break down one of the biggest questions in AI today: do open vs. closed models still matter? From the rise of physical AI and edge devices to the shifting landscape of open-source models like LLaMA, they explore whether the “model wars” are becoming irrelevant. The conversation then dives into a bigger transformation, the rise of agentic systems, workflows, and AI-driven infrastructure.Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XUpcoming Events: Register for upcoming webinars here!Midwest AI Summit 2026

In this Fully-Connected episode, Dan and Chris start with Anthropic's Mythos frontier model, parsing what is publicly known about its cybersecurity capabilities and projecting its possible implications from "We've been here before.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Mauro Schilman, CTO and Co-founder of Tuki, the distribution standard for the AI agent era in travel, for a wide-ranging conversation that moves from the joys of international travel and the beauty of mathematics to the fast-evolving world of AI and large language models. Mauro shares his background as a math Olympiad competitor and later a coach, his time training coding models at the AI company Cohere, and his thoughts on how frontier models are progressing — or plateauing — at the foundational level while innovation accelerates at the application layer. The two also get into the mechanics of agentic AI, MCP and agent-to-agent protocols, hierarchical memory systems, red-green test-driven development as a powerful coding workflow, and the philosophical murkiness of open-source AI. They wrap up discussing Tuki Travel's mission to build AI-ready infrastructure for the travel industry, connecting hotels, suppliers, and online travel agencies to prepare for the coming wave of agentic commerce. You can learn more about Tuki Travel and reach out to the team at tukiclub.com.Timestamps00:00 - Stewart welcomes Mauro Schilman, CTO and Co-founder of Tuki Travel, who shares how traveling since age 15 through high school exchanges opened his mind to cultural similarities and differences.05:00 - Mauro explains Math Olympiad coaching culture and mentorship, noting LLMs now solve competition-level problems while Terence Tao explores AI assisting frontier unsolved mathematics.10:00 - Discussion turns to ChatGPT revealing Mauro's birthdate unprompted, exposing opaque application layers, preference tuning, and system prompts hidden within closed models.15:00 - Mauro argues true open source AI requires full training data, annotation protocols, and alignment processes, not just model weights, while scaling laws appear to be slowing.20:00 - Hierarchical memory models replace flat vector databases, using three-level retrieval systems improving context accuracy as knowledge management becomes AI's core challenge.25:00 - Mauro describes travel's fragmented infrastructure of aggregators, bed banks, and intermediaries, explaining Tuki builds agent-ready unification protocols for AI commerce.30:00 - MCP versus API debate clarifies natural language capability descriptions help agents consume services, while agent-to-agent communication embeds negotiating agents inside supplier systems.35:00 - Hallucinations and consumer trust block agentic payments, industries must build mistake-resilience into bookings before autonomous agent transactions become viable.40:00 - Mauro reveals red-green test-driven development methodology where agents write failing tests first then implementations, creating Oracle verification loops dramatically improving code quality.45:00 - Blockchain's potential for transparent distributed AI training discussed, distinguishing democratization from decentralization while stable coins and regulatory momentum build toward agentic commerce infrastructure.Key Insights1. Travel broadens perspective by revealing both universal human similarities and deep cultural differences. Mauro Schilman began traveling at fifteen through math olympiad competitions and found that people across the world share fundamental traits while also being shaped in profoundly different ways by their cultures. This tension between sameness and difference is what makes travel meaningful.2. Mathematics transitions from structured problem-solving in olympiads to genuine uncertainty in graduate school and research. Olympiad problems are carefully designed with elegant solutions meant to encourage creative thinking, but once a mathematician enters academia, the answers are unknown and the work becomes navigating that uncertainty.3. AI is now assisting mathematicians at the frontier, not just solving olympiad-level problems. Terence Tao, one of the greatest living mathematicians, has written publicly about how AI tools can help tackle unsolved problems, though the role of AI remains assistive rather than independent at the research level.4. Large language models are not truly transparent even when described as open source. Releasing model weights alone does not reveal the training data, annotation protocols, alignment tuning, or system prompts that shape model behavior. Real openness would require access to the entire pipeline.5. Memory and retrieval remain core unsolved challenges in AI systems. Researchers are moving from flat vector database approaches toward hierarchical memory structures with roughly three layers, which improves retrieval accuracy and reduces how much context gets consumed with each search.6. The travel industry is structurally unprepared for AI agents. A hidden web of bed banks, aggregators, and aggregators of aggregators sits between hotels and consumers, each taking a fee. Tuki Travel is building infrastructure to unify this distribution layer and make it consumable by AI agents through protocols like MCP and emerging agent-to-agent communication standards.7. Test-driven development using a red-green approach significantly improves AI-generated code quality. By asking the model to write failing tests before writing any implementation, developers create a verification oracle that guides the model toward correct solutions and avoids the bias of writing tests that simply confirm existing flawed code.

Autonomous driving is not just a big tech or closed-source game, it's becoming accessible through open innovation and real-world deployment. Dan and Chris sit down with Harald Schäfer, CTO at Comma AI, to explore how OpenPilot is bringing self-driving to everyday vehicles using open source AI. We dive into the intersection of machine learning, robotics, and simulation, including how world models are enabling training at scale and shaping the future of autonomy.Featuring:Harald Schäfer – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:Comma

In this fully connected episode, Dan and Chris break down the Anthropic Claude Code leak, what went wrong and what it reveals about agentic systems, AI architecture, and AI safety. They also explore how the open source community is responding and why this moment could reshape how AI systems are built and secured.Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XUpcoming Events: Register for upcoming webinars here!

AI is rapidly transforming how software is built, shifting economic incentives from open source code and collaboration toward on-demand, personalized development through agentic coding a.k.a. vibe coding. In this episode, Chris speaks with Miklós Koren of Central European University about how AI is reshaping open source and the software industry. They explore the economics of incentives, evolving collaboration patterns, and what this shift means for software development, the future of AI, and its broader impact on the technology sector.Featuring:Miklós Koren – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XLinks:Vibe Coding Kills Open SourceThe Directions of Technical ChangeThe Tailwind storyUpcoming Events: Register for upcoming webinars here!

What does “AI at the edge” really mean in 2026, and why does it matter now more than ever before? In this episode, we're joined by Brandon Shibley, Edge AI Solutions Engineering Lead at Qualcomm's Edge Impulse, to discuss the current state and future of Edge AI in 2026. We discuss Gen AI, Small Models, and Cascades of Models, along with real-world constraints like latency, power, and privacy. We also dive into the role of MLOps, evolving hardware, and how developers can start building practical edge AI systems today.Featuring:Brandon Shibley – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:Read our Ultimate Guide to Edge AIDownload your copy of O'Reilly's AI at the Edge Check out the Edge Impulse blogSign-up for an expert led trial of Edge ImpulseUpcoming Events: Register for upcoming webinars here!

In this episode of See See by Ceci, Paul Thagard, one of the most influential thinkers at the crossroads of philosophy, psychology, and artificial intelligence, takes us on a journey through the architecture of thought, emotion, and coherence that defines the human mind. A distinguished professor emeritus at the University of Waterloo, Fellow of the Royal Society of Canada, recipient of the Killam and Molson Prizes, and author of eighteen books, Thagard has spent decades asking the hardest questions about intelligence: what it is, where it comes from, and whether machines will ever truly share it with us. His pioneering theory of explanatory coherence reimagines the brain not as a logic machine but as a coherence engine, a system that makes sense of the world by satisfying countless constraints simultaneously, weaving perception, reasoning, and emotion into a single fabric. In this wide-ranging conversation, Thagard reflects on the difference between intelligence and consciousness; on the devastating role of social media in the spread of misinformation; on the power of analogy as a tool of creativity, from Darwin's theory of natural selection to the everyday act of reading a stranger's gesture. And on why computers, despite their cognitive capacities, remain fundamentally psychopathic. "They are highly intelligent," he says, "but they lack empathy and are therefore incapable of caring." That incapacity sits at the heart of the episode's most urgent theme: the alarming rise of human-AI relationships, and what we risk losing when we mistake imitation for intimacy. Drawing on his recent book Dreams, Jokes, and Songs: How Brains Build Consciousness and the forthcoming AI Boom or Doom?, Thagard offers a remarkably clear-eyed view of minds both human and artificial, one that is at once scientifically rigorous and deeply humane. This is an episode about the mind as a coherence engine: hot and cold, rational and emotional, individual and social. About how neurons firing together can produce something as extraordinary as humor, as mysterious as dreams, and as dangerous as political delusion. And about the light, and the peril, that lies ahead as human and artificial intelligence continue to converge.

BUFFALO, NY — March 23, 2026 — A new #research paper was #published in Volume 18 of Aging-US on March 12, 2026, titled “Blood biochemical and gut microbiotic neural network models forecasting human biological age.” Led by Anastasia A. Kobelyatskaya from the Russian Clinical Research Center for Gerontology, Pirogov Russian National Research Medical University, and the Institute of Biology of Aging and Healthy Longevity Medicine with Preventive Medicine Clinic, Petrovsky Russian Research Centre of Surgery — with corresponding author Alexey Moskalev from the Institute of Biology of Aging and Healthy Longevity Medicine with Preventive Medicine Clinic, Petrovsky Russian Research Centre of Surgery — the study builds a gender-specific biochemical model (seven routine clinical markers, e.g., cystatin-C, IGF-1, DHEAS, plus sex-specific sets) and a microbiota model (45 species measured by full-length 16S sequencing). Both models were trained and tested on the same 637-person dataset and achieved mean absolute errors of around six years and R² values above 0.8. The team emphasised interpretability: they applied SHapley Additive exPlanations (SHAP) to convert each model from a “black box” into a more interpretable tool, showing how individual predictors (for example, DHEAS, cystatin-C, NT-proBNP in the blood model, and species such as Blautia obeum in the microbiota model) shift predicted age in years for a given individual. The biochemical clock yielded a small (clinically accessible) predictor set (7 markers) to ease clinical translation, while the microbiota clock used a 45-species signature and highlighted microbiome taxa whose abundance gradients correlate with predicted microbiotic age. “As the proposed models possess both global and local explainability, they hold future potential for application in monitoring the effectiveness of various interventions in clinical trials.” The authors note limitations and next steps: the cohort was restricted to a Caucasian population, and the microbiota model requires sequencing resources that may limit immediate clinical rollout. They call for external validation in larger, ethnically diverse cohorts, prospective testing to link model predictions to health outcomes, and application of the explainable models to monitor responses in intervention trials (for example, lifestyle, diet, or drug studies) where a change in predicted biological age would be an early, interpretable signal of benefit. DOI - https://doi.org/10.18632/aging.206360 Corresponding author - Alexey Moskalev - amoskalev@med.ru Abstract video - https://www.youtube.com/watch?v=wg3YEwXMKWY Sign up for free Altmetric alerts about this article - https://aging.altmetric.com/details/email_updates?id=10.18632%2Faging.206360 Subscribe for free publication alerts from Aging - https://www.aging-us.com/subscribe-to-toc-alerts Keywords - aging, biological age, blood biochemistry, gut microbiome, neural network To learn more about the journal, please visit https://www.Aging-US.com​​ and connect with us on social media at: Bluesky - https://bsky.app/profile/aging-us.bsky.social ResearchGate - https://www.researchgate.net/journal/Aging-1945-4589 X - https://twitter.com/AgingJrnl Facebook - https://www.facebook.com/AgingUS/ Instagram - https://www.instagram.com/agingjrnl/ LinkedIn - https://www.linkedin.com/company/aging/ Reddit - https://www.reddit.com/user/AgingUS/ Pinterest - https://www.pinterest.com/AgingUS/ YouTube - https://www.youtube.com/@Aging-US Spotify - https://open.spotify.com/show/1X4HQQgegjReaf6Mozn6Mc MEDIA@IMPACTJOURNALS.COM

What happens when an AI hater starts building with AI agents? In this episode, we talk with software engineer Steve Klabnik, known for his work on the Rust programming language, about his journey from criticizing AI to experimenting with it firsthand. We explore Steve's programming language Rue, largely built with the help of AI tools like Claude, and discuss what this means for software engineering and the future of coding in an AI-driven world.Featuring:Steve Klabnik – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:The Rust Programming LanguageRustRueDaniel's RSA Meeting link for March 23, 2026Daniel's RSA Meeting link for March 24-25, 2026Upcoming Events: Register for upcoming webinars here!

“I can point to things. But is that a systemic explanation? I think there the answer is a little less clear. I mean, surely people need love and all of that, but then there's this risk of just devolving into platitude.” — David SussilloDavid Sussillo is a big time neural reverse engineer. The Stanford brain scientist worked at Google Brain with Geoffrey Hinton, and now is at Meta Reality Labs. What distinguishes Sussillo, however, is not his Silicon Valley good luck, but the bad luck of his origins. In his memoir, Emergent: A Memoir of Boyhood, Computation, and the Mysteries of the Mind, Sussillo begins at the Albuquerque Christian Children's Home — a modern-day orphanage — and the Milton Hershey School, the boarding school endowed by the chocolate magnate for kids with nowhere else to go. Both his parents were addicts. His mom died young. His dad spent his life as an untrained preacher ministering to homeless people on the streets of Albuquerque while managing a lifelong heroin habit.The book's thesis borrows from the science he studies: “emergence” — simple things interacting to produce complex behaviour that none of them could produce alone. His life is both proof of and a challenge to this concept. He made it out. Most of the kids he grew up with didn't. He can point to moments — a gifted-and-talented test in third grade, an aunt and uncle's intervention at nine, a first love in college — but he can't build an explanatory system from these haphazard events. The Sussillo quilt doesn't have an innate pattern. It just has patches.What makes Sussillo unusual as a memoirist is his refusal to sentimentalise. Twenty years of psychotherapy, he confesses, has taught him something most authors never learn: that understanding your own story doesn't mean you've explained it. His science can't explain his childhood either. “The big dirty secret of neuroscience,” he says, “is that we don't really understand much in the ways that people would love us to understand.” The man who reverse-engineers neural networks can't reverse-engineer himself.I asked him whether having children would have been harder than writing the book. Yes, he said. With the book, you can take a break. With kids, you relive things through a very specific way of relating. He and his wife chose not to. His mentors all told him he'd have been great at it. He's not so sure. That honesty — the willingness to say “I don't know” and mean it — runs through everything Sussillo does. He says he's happy, claiming to have found peace with his past. But he still carries the baggage. Who wouldn't? He's just learned to manage it. Emergent, not emerged. Five Takeaways•       From Orphanage to Google Brain: Both parents were heroin addicts. Sussillo grew up in a modern-day orphanage in Albuquerque and then the Milton Hershey School. He went on to work at Google Brain with Geoffrey Hinton, now works at Meta Reality Labs, teaches at Stanford. Most of the kids he grew up with didn't make it.•       Emergence as Autobiography: The book's thesis borrows from the science he studies: simple pieces combining into complicated outcomes. His life is the proof of concept and the counter-example simultaneously. The quilt doesn't have a pattern. It just has patches.•       The Dirty Secret of Neuroscience: The man who reverse-engineers neural networks can't reverse-engineer himself. “We don't really understand much in the ways that people would love us to understand.” Twenty years of therapy taught him more than the science.•       Would Kids Have Been Harder Than the Book? Yes. With the book, you can take a break. With kids, you relive trauma through a very specific way of relating. He and his wife chose not to have children. His mentors told him he'd have been great at it. He's not so sure.•       Emergent, Not Emerged: Sussillo has found peace with his past. He's happy. He still carries the baggage from his childhood. He's just learned how to manage it. The emergence is ongoing. About the GuestDavid Sussillo is a research scientist at Meta Reality Labs and a consulting professor at Stanford University. He previously worked at Google Brain. His memoir is Emergent: A Memoir of Boyhood, Computation, and the Mysteries of the Mind. He grew up in the Albuquerque Christian Children's Home and the Milton Hershey School. He lives in New Mexico.References:•       Emergent: A Memoir of Boyhood, Computation, and the Mysteries of the Mind by David Sussillo — the book under discussion.•       The Albuquerque Christian Children's Home — the group home where Sussillo spent five years of his childhood.•       The Milton Hershey School — founded in 1906 by the Hershey chocolate magnate for children with nowhere else to go. Sussillo spent four years there.•       Google Brain — the lab where Sussillo worked alongside Geoffrey Hinton on the neural network research that became the foundation of modern AI.•       John Conway's Game of Life — the cellular automaton simulation Sussillo cites as an early example of emergence: complicated outcomes from simple rules.About Keen On AmericaNobody asks more awkward questions than the Anglo-American writer and filmmaker Andrew Keen. In Keen On America, Andrew brings his pointed Transatlantic wit to making sense of the United States — hosting daily interviews about the history and future of this now venerable Republic. With nearly 2,800 episodes since the show launched on TechCrunch in 2010, Keen On America is the most prolific intellectual interview show in the history of podcasting.WebsiteSubstackYouTubeApple PodcastsSpotify Chapters:(00:00) - Introduction (01:30) - The Albuquerque Christian Children's Home and Milton Hershey School (03:30) - Why write a memoir? Five years and twenty years of therapy (05:00) - Heroin-addicted parents: the origin story (08:00) - A father as untrained preacher on the streets of Albuquerque (10:00) - Which parent had more impact? (12:00) - The gifted-and-talented test that changed everything (15:00) - From Milton Hershey to Carnegie Mellon: the jump (18:00) - Life falls apart at 23: panic attacks and psychotherapy (21:00) - Neural networks, Google Brain, and the dirty secret of neuroscience (25:00) - Would having kids have been harder than writing the book? (28:00) - The Albanian friend and the beach: what America gets right (31:00) - Silicon...

AI is reshaping global power, from chip manufacturing and computing power to AI governance and US-China relations.  In this episode, Ben Buchanan, Assistant Professor at The Johns Hopkins University and former White House Special Advisor for AI, explores how AI policy, geopolitics, and international cooperation intersect with AI  innovation and AI safety. We discuss the strategic importance of computing power, the future of AI governance, and what it will take for democracies to lead responsibly in the age of AI.Featuring:Ben Buchanan – LinkedIn Chris Benson – Website, LinkedIn, Bluesky, GitHub, XLinks:The AI Grand BargainUpcoming Events: Register for upcoming webinars here!

In this episode, host Frank La Vigne and co-host Candice Gillhoolley sit down with Danny Wall, the founder, CEO, and CTO of OA Quantum Labs, for an in-depth conversation about the real-world intersection of quantum computing and artificial intelligence.You'll hear Danny Wall pull the curtain back on how OA Quantum Labs is pushing quantum solutions beyond the research phase and into commercially viable applications. From accelerating AI training and inference to spinning out novel materials at lightning speed, Danny shares firsthand stories about quantum-enhanced breakthroughs in material science, finance, and more.This episode dives into common misconceptions—like the idea that AI is actually running on quantum computers—and Danny explains the nuanced, current reality: quantum as an incredible mathematical accelerator and enhancement for AI, rather than a full replacement. You'll also get practical advice for developers, researchers, and investors eager to get started with quantum, and insights on what it really takes to stay ahead in a field moving as fast as quantum.If you're curious about how quantum technologies are escaping the confines of the lab and making real commercial impact, this is the episode you've been waiting for!Time Stamps00:00 "Quantum Labs Driving AI Innovation"03:31 "Quantum Computing Enhances AI Efficiency"09:32 Advanced Materials Breakthroughs Revolutionizing Industries12:58 Quantum Investing: Beyond PhD Pedigrees15:47 "Quantum, Solutions, and Strategic Investment"18:05 "Jump Into Quantum Development"20:27 "Quantum Enhancement for AI Solutions"25:38 AI Limits and Misconceptions27:02 "AI Creativity Hack with Roles"33:16 "Challenges in Quantum Error Correction"36:37 Quantum Computing's Material Challenges38:02 "AI Progress Hitting Limits"42:49 "Quantum Encryption and Neural Networks"47:19 "Schrödinger's Cat Explained Simply"48:16 "Quantum Physics Misconceptions Explained"

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Our Titans of Science series continues with Mike Wooldridge, Ashall Professor of Foundations of Artificial Intelligence at the University of Oxford. He has conducted extensive work in the field of agentic AI, systems comprising multiple interacting AIs. In this episode, he tells Chris Smith what drew him to computers and AI in the first place, the pioneering work of Geoff Hinton, why ChatGPT isn't made to speak the truth, and what's in store for us as AI continues to develop... Like this podcast? Please help us by supporting the Naked Scientists

A Note from James:In the last episode, we talked about whether Martin Shkreli really deserves the label “most hated man in America.” My conclusion was no, and I hope you came to the same conclusion after hearing his perspective.In this episode, we shift gears completely. We talk about Bitcoin, crypto, AI, energy, optical computing, and what the future of technology might actually look like.Martin has a very unusual combination of skills—finance, biotech, programming—and I always enjoy hearing how he connects ideas across different fields. That's what this conversation is about.Episode Description:What happens when AI demand collides with the limits of computing power and energy?In Part 2, Martin Shkreli and James explore the future of technology—from crypto vulnerabilities to optical computing, GPU scaling, and the potential energy crisis driven by artificial intelligence.They discuss whether Bitcoin can survive quantum computing, why stablecoins solve real-world financial problems, and how computing architecture may shift beyond traditional silicon chips. The conversation then moves into AI economics: why companies might spend billions on compute to make better decisions, how energy constraints could shape innovation, and why optical computing could become the next major breakthrough.This episode isn't about controversy—it's about technological leverage, incentives, and where computation is heading next.What You'll Learn:Why quantum computing could eventually threaten Bitcoin's encryptionThe real-world advantages of stablecoins and decentralized paymentsHow AI demand could create massive new energy constraintsWhy optical (photonic) computing may outperform traditional silicon chipsHow businesses might use large-scale AI compute for strategic decisionsTimestamped Chapters:[00:02:00] Bitcoin, Encryption & Quantum Computing Risks[00:03:02] A Note from James[00:03:34] Crypto Markets: Speculation vs. Utility[00:05:23] Banking Control, Debanking & Stablecoins[00:07:40] Moore's Law, Huang's Law & The Limits of Silicon[00:08:45] Optical Computing Explained[00:09:12] NVIDIA, Parallelization & Power Consumption[00:10:24] Energy Constraints & The Electrical Grid[00:11:41] AI Energy Demand vs. Countries[00:12:24] Corporate AI Decision-Making at Scale[00:13:37] The Coming Explosion of AI Compute[00:14:20] Energy Efficiency vs. Speed[00:15:17] GPU Efficiency Improvements & Jevons Paradox[00:17:00] Why AI Is Different from Traditional Computing[00:17:47] Optical vs. Quantum vs. DNA Computing[00:18:19] Why Optical Computing Fits AI Perfectly[00:19:28] Precision, Bits & Neural Networks[00:21:24] Error Tolerance in AI Systems[00:22:00] Fiber Optics & Existing Infrastructure[00:23:16] New Computing Paradigms Beyond Silicon[00:24:00] Matrix Multiplication & AI Workloads[00:24:53] Closing ThoughtsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Send a textTom Griffiths, Henry R. Luce Professor at Princeton University, joins the show to explore the surprising science behind how we actually think. His new book, The Laws of Thought, bridges computational cognitive science and AI—challenging assumptions about decision-making, neural networks, and the path to artificial general intelligence.Show NotesTimestamps 01:21 – Meet Tom Griffiths 05:27 – Tom's Book 06:58 – A Neural Network 09:55 – AGI? 19:10 – Writing the Book 20:45 – The Laws of Thought 27:24 – The Neural Network Surprise 31:33 – Learning from Experts 35:19 – Decision Making vs. Probability 42:36 – Government AI ConsiderationsLinks LinkedIn: linkedin.com/in/tom-griffiths-7b31a0364 Book: The Laws of Thought – Macmillan#TheLawsOfThought, #CognitiveScience, #ArtificialIntelligence, #AGI, #NeuralNetworks, #DecisionMaking, #Probability, #AIResearch, #Princeton, #TechPodcast, #MakingDataSimple, #AIGovernment, #MachineLearningWant to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

Send a textTom Griffiths, Henry R. Luce Professor at Princeton University, joins the show to explore the surprising science behind how we actually think. His new book, The Laws of Thought, bridges computational cognitive science and AI—challenging assumptions about decision-making, neural networks, and the path to artificial general intelligence.Show NotesTimestamps 01:21 – Meet Tom Griffiths 05:27 – Tom's Book 06:58 – A Neural Network 09:55 – AGI? 19:10 – Writing the Book 20:45 – The Laws of Thought 27:24 – The Neural Network Surprise 31:33 – Learning from Experts 35:19 – Decision Making vs. Probability 42:36 – Government AI ConsiderationsLinks LinkedIn: linkedin.com/in/tom-griffiths-7b31a0364 Book: The Laws of Thought – Macmillan#TheLawsOfThought, #CognitiveScience, #ArtificialIntelligence, #AGI, #NeuralNetworks, #DecisionMaking, #Probability, #AIResearch, #Princeton, #TechPodcast, #MakingDataSimple, #AIGovernment, #MachineLearningWant to be featured as a guest on Making Data Simple? Reach out to us at almartintalksdata@gmail.com and tell us why you should be next. The Making Data Simple Podcast is hosted by Al Martin, WW VP Technical Sales, IBM, where we explore trending technologies, business innovation, and leadership ... while keeping it simple & fun.

How did we go from digital computers to AI seemingly everywhere? Neil deGrasse Tyson, Chuck Nice, & Gary O'Reilly dive into the mechanics of thinking, how AI got its start, and what deep learning really means with cognitive and computer scientist, Nobel Laureate, and one of the architects of AI, Geoffrey Hinton. Subscribe to SiriusXM Podcasts+ to listen to new episodes of StarTalk Radio ad-free and a whole week early.Start a free trial now on Apple Podcasts or by visiting siriusxm.com/podcastsplus. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode of the Crazy Wisdom podcast, host Stewart Alsop sits down with Markus Buehler, the McAfee Professor of Engineering at MIT, to explore how seemingly different systems—from proteins and music to knowledge structures and AI reasoning—share underlying patterns through hierarchy, self-organization, and scale-free networks. The conversation ranges from the limits of current AI interpolation versus true discovery (using the fire-to-fusion example), to the emergence of agent swarms and their non-linear effects, to practical questions about ontologies, knowledge graphs, and whether humans will remain necessary in the creative discovery process. Markus discusses his lab's work automating scientific discovery through AI agents that can generate hypotheses, run simulations, and even retrain themselves, while Stewart shares his own experiences building applications with AI coding agents and grapples with questions about intellectual property, material science constraints, and the future of human creativity in an AI-abundant world.Timestamps00:00 - Introduction to Marcus Buehler's work on knowledge graphs, structural grammar across proteins, music, and AI reasoning05:00 - Discussion of AI discovery versus interpolation, using fire and fusion as examples of fundamental versus incremental innovation10:00 - Language models as connective glue between agents, enabling communication despite imperfect outputs and canonical averaging15:00 - Embodiment and agency in AI systems, creating adversarial agents that challenge theories and expand world models20:00 - Emergent properties in materials and AI, comparing dislocations in metals to behaviors in agent swarms25:00 - Human role-playing and phase separation in society, parallels to composite materials and heterogeneity30:00 - Physical world challenges, atom-by-atom manufacturing at MIT.nano, limitations of lithography machines35:00 - Synthetic biology as alternative to nanotechnology, programming microorganisms for materials discovery40:00 - Intellectual property debates, commodification of AI models, control layers more valuable than model architecture45:00 - Automation of ontologies, agent self-testing, daughter's coding success at age 1150:00 - Graph theory for knowledge compression, neurosymbolic approaches combining symbolic and neural methods55:00 - Nonlinear acceleration in AI, emergence from accumulated innovations, restaurant owner embracing AI01:00:00 - Future generations possibly rejecting AI, democratization of knowledge, social media as real-time scientific discourseKey Insights1. Universal Patterns Across Disciplines: Seemingly different systems in nature—proteins, music, social networks, and knowledge itself—share fundamental structural patterns including hierarchy, self-organization, and scale-free networks. This commonality allows creative thinkers to draw insights across disciplines, applying principles from one domain to solve problems in another. As an engineer and materials scientist, Buehler has leveraged these isomorphisms to advance scientific understanding by mapping the "plumbing" of different systems onto each other, revealing hidden relationships that enable extrapolation beyond what's observable in any single domain.2. The Discovery Versus Interpolation Problem: Current AI systems, particularly large language models, excel at interpolation—recombining existing knowledge in new ways—but struggle with genuine discovery that requires fundamental rewiring of world models. Using the example of fire versus fusion, Buehler explains that an AI trained on combustion chemistry would propose bigger fires or new fuels, but couldn't conceive of fusion because that requires stepping back to more fundamental physics. True discovery demands the ability to recognize when existing theories have boundaries and to develop entirely new frameworks, something current AI architectures aren't designed to achieve due to their training objective of predicting the most likely outcome.3. The Role of Ontologies and Knowledge Graphs: While some AI researchers argue that ontologies are unnecessary because models form internal representations, Buehler advocates for explicit knowledge graphs as essential discovery tools. External ontologies provide sharp, analytical, symbolic representations that complement the fuzzy internal representations of neural networks. They enable verification of rare connections—like obscure papers that might hold key insights—which would be averaged away in standard AI training. This neurosymbolic approach combines the generalization capabilities of neural networks with the precision of formal knowledge structures, creating more powerful discovery systems.4. Emergent Properties and Agent Swarms: Just as materials science shows that collections of atoms exhibit properties impossible to predict from individual components, AI agent swarms demonstrate emergent behaviors beyond single models. When agents are incentivized not just to answer questions but to challenge each other adversarially, propose theories, and test hypotheses, they can spawn new copies of themselves and evolve understanding beyond their initial programming. This emergence isn't surprising from a materials science perspective—dislocations, grain boundaries, and other collective phenomena only appear at scale, fundamentally determining material behavior in ways unpredictable from studying just a few atoms.5. The Commoditization of Intelligence: The fundamental AI models themselves are becoming commodities, as evidenced by events like the Moldbug phenomenon where people built agents using various providers interchangeably. The real value is shifting from who has the smartest model to how models are orchestrated, integrated, and deployed. This parallels historical technology adoption patterns—just as we moved past debating who makes the best electricity to focusing on applications, AI is transitioning from a horse race over model capabilities to questions of infrastructure, energy, access speed, and agent coordination at the systems level.6. Human-AI Collaboration and Creative Control: Rather than wholesale replacement, AI enables humans to operate in an intensely creative space as orchestrators sampling from vast possibility spaces. Similar to how Buehler's 11-year-old daughter now builds sophisticated applications that would have required professional developers years ago, AI democratizes access to capabilities while humans retain the creative judgment about direction and meaning. The human role becomes curating emergence, finding rare connections, playing at the edges of knowledge, and exercising the kind of curiosity-driven exploration that AI systems lack without embodied stakes in their own survival and continuation.7. Technology as Evolutionary Inevitability: The development of AI represents not an unnatural threat but the next stage of human evolution—an extension of our innate drive to build models of ourselves and our world. From cave paintings to partial differential equations to artificial intelligence, humans continuously create increasingly sophisticated representations and tools. Attempting to stop this technological evolution is futile; instead, the focus should be on steering it ...

As AI accelerates innovation and adoption, leaders are facing rising cognitive load, shifting systems, and new emotional realities inside their organizations. In this episode, Deloitte's Chief Innovation Officer Deborah Golden joins us to explore how AI is reshaping leadership, why vulnerability and empathy are critical in this moment, and how anti-fragility, not just resilience, will define the future of work.Featuring:Deborah Golden – LinkedIn Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:DeloitteSponsor: Framer - The website builder that turns your dot com from a formality into a tool for growth. Check it out at framer.com/PRACTICALAIUpcoming Events: Register for upcoming webinars here!

AI is moving fast from research to real-world deployment, and when things go wrong, the consequences are no longer hypothetical. In this episode, Sean McGregor, co-founder of the AI Verification & Evaluation Research Institute and also the founder of the AI Incident Database, joins Chris and Dan to discuss AI safety, verification, evaluation, and auditing. They explore why benchmarks often fall short, what red-teaming at DEF CON reveals about machine learning risks, and how organizations can better assess and manage AI systems in practice.Featuring:Sean McGregor– LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks:AI Verification & Evaluation Research InstituteAI Incident Database38th convening of IAAIBenchRiskState of Global AI Incident ReportingUpcoming Events: Register for upcoming webinars here!

AI agents are moving from demos to real workplaces, but what actually happens when they run a company? In this episode, journalist Evan Ratliff, host of Shell Game, joins Chris to discuss his immersive journalism experiment building a real startup staffed almost entirely by AI agents. They explore how AI agents behave as coworkers, how humans react when interacting with them, and where ethical and workplace boundaries begin to break down.Featuring:Evan Ratliff  – LinkedIn, XChris Benson – Website, LinkedIn, Bluesky, GitHub, XLinks:Shell GameUpcoming Events: Register for upcoming webinars here!

As AI increasingly shapes geopolitics, elections, and civic life, its impact on democracy is becoming impossible to ignore. In this episode, Daniel and Chris are joined by security expert Bruce Schneier to explore how AI and technology are transforming democracy, governance, and citizenship. Drawing from his book Rewiring Democracy, they explore real examples of AI in elections, legislation, courts, and public AI models, the risks of concentrated power, and how these tools can both strengthen and strain democratic systems worldwide.Featuring:Bruce Schneier – XChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XLinks: Schneier on SecuritySponsors:Framer - The website builder that turns your dot com from a formality into a tool for growth. Check it out at framer.com/PRACTICALAIZapier - The AI orchestration platform that puts AI to work across your company. Check it out at zapier.com/practicalUpcoming Events: Register for upcoming webinars here!

As 2026 gets underway we know that many take time around this new beginning to improve not only their physical, but also their mental health. With that in mind, we're rerunning an episode with Leanne Williams on the future of depression care. Leanne is an expert on clinical depression and is working on new ways to more precisely diagnose depression in order to develop more effective treatment. For anyone who has suffered from depression or knows someone who has, it's an episode that provides hope for what's on the horizon. We hope you'll take another listen and also share this episode with anyone who you think may benefit from the conversation. Episode Reference Links:Stanford Profile: Leanne WilliamsConnect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / FacebookChapters:(00:00:00) IntroductionRuss Altman introduces guest Leanne Williams, a professor of Psychiatry and Behavioral Science at Stanford University.(00:01:43) What Is Depression?Distinguishing clinical depression from everyday sadness.(00:03:31) Current Depression Treatment ChallengesThe trial-and-error of traditional depression treatments and their timelines.(00:06:16) Brain Mapping and Circuit DysfunctionsAdvanced imaging techniques and their role in understanding depression.(00:09:03) Diagnosing with Brain ImagingHow brain imaging can complement traditional diagnostic methods in psychiatry.(00:10:22) Depression BiotypesIdentifying six distinct biotypes of depression through brain imaging.(00:12:31) Cognitive Features of DepressionHow cognitive impairment plays a major role in certain depression biotypes.(00:14:11) Matching Treatments to BiotypesFinding appropriate treatments sooner using brain-based diagnostics.(00:15:38) Expanding Treatment OptionsPersonalizing therapies and improving treatment outcomes based on biotypes.(00:19:03) AI in Depression TreatmentUsing AI to refine biotypes and predict treatment outcomes with greater accuracy.(00:22:15) Psychedelics in Depression TreatmentThe potential for psychedelic drugs to target specific biotypes of depression.(00:23:46) Expanding the Biotypes FrameworkIntegrating multimodal approaches into the biotype framework.(00:27:29) Reducing Stigma in DepressionHow showing patients their brain imaging results reduces self-blame and stigma.(00:29:38) 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.

Think you know the story of AI's rise and fall? This episode upends conventional wisdom with guest historian Thomas Haigh, who reveals why the infamous "AI winter" might just be a myth and why the field's biggest failures fueled today's breakthroughs. Two Thinking Machines Lab Cofounders Are Leaving to Rejoin OpenAI NVDA, GOOGL, META: AI Spending Forecast to Hit $2.53 Trillion This Year Nvidia, Eli Lilly just say yes to making drugs together, using Vera Rubin GPUs Claude Cowork Exfiltrates Files We put Claude Code in Rollercoaster Tycoon How Generative AI is destroying society - by Gary Marcus Anthropic rewrites Claude's guiding principles—and entertains the idea that its AI might have 'some kind of consciousness or moral status' Claude's new constitution Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Thomas Haigh 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: bitwarden.com/twit monarch.com with code IM

Think you know the story of AI's rise and fall? This episode upends conventional wisdom with guest historian Thomas Haigh, who reveals why the infamous "AI winter" might just be a myth and why the field's biggest failures fueled today's breakthroughs. Two Thinking Machines Lab Cofounders Are Leaving to Rejoin OpenAI NVDA, GOOGL, META: AI Spending Forecast to Hit $2.53 Trillion This Year Nvidia, Eli Lilly just say yes to making drugs together, using Vera Rubin GPUs Claude Cowork Exfiltrates Files We put Claude Code in Rollercoaster Tycoon How Generative AI is destroying society - by Gary Marcus Anthropic rewrites Claude's guiding principles—and entertains the idea that its AI might have 'some kind of consciousness or moral status' Claude's new constitution Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Thomas Haigh 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: bitwarden.com/twit monarch.com with code IM

Think you know the story of AI's rise and fall? This episode upends conventional wisdom with guest historian Thomas Haigh, who reveals why the infamous "AI winter" might just be a myth and why the field's biggest failures fueled today's breakthroughs. Two Thinking Machines Lab Cofounders Are Leaving to Rejoin OpenAI NVDA, GOOGL, META: AI Spending Forecast to Hit $2.53 Trillion This Year Nvidia, Eli Lilly just say yes to making drugs together, using Vera Rubin GPUs Claude Cowork Exfiltrates Files We put Claude Code in Rollercoaster Tycoon How Generative AI is destroying society - by Gary Marcus Anthropic rewrites Claude's guiding principles—and entertains the idea that its AI might have 'some kind of consciousness or moral status' Claude's new constitution Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Thomas Haigh 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: bitwarden.com/twit monarch.com with code IM

Think you know the story of AI's rise and fall? This episode upends conventional wisdom with guest historian Thomas Haigh, who reveals why the infamous "AI winter" might just be a myth and why the field's biggest failures fueled today's breakthroughs. Two Thinking Machines Lab Cofounders Are Leaving to Rejoin OpenAI NVDA, GOOGL, META: AI Spending Forecast to Hit $2.53 Trillion This Year Nvidia, Eli Lilly just say yes to making drugs together, using Vera Rubin GPUs Claude Cowork Exfiltrates Files We put Claude Code in Rollercoaster Tycoon How Generative AI is destroying society - by Gary Marcus Anthropic rewrites Claude's guiding principles—and entertains the idea that its AI might have 'some kind of consciousness or moral status' Claude's new constitution Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Thomas Haigh 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: bitwarden.com/twit monarch.com with code IM

Think you know the story of AI's rise and fall? This episode upends conventional wisdom with guest historian Thomas Haigh, who reveals why the infamous "AI winter" might just be a myth and why the field's biggest failures fueled today's breakthroughs. Two Thinking Machines Lab Cofounders Are Leaving to Rejoin OpenAI NVDA, GOOGL, META: AI Spending Forecast to Hit $2.53 Trillion This Year Nvidia, Eli Lilly just say yes to making drugs together, using Vera Rubin GPUs Claude Cowork Exfiltrates Files We put Claude Code in Rollercoaster Tycoon How Generative AI is destroying society - by Gary Marcus Anthropic rewrites Claude's guiding principles—and entertains the idea that its AI might have 'some kind of consciousness or moral status' Claude's new constitution Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Thomas Haigh 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: bitwarden.com/twit monarch.com with code IM

As generative AI moves into production, traditional guardrails and input/output filters can prove too slow, too expensive, and/or too limited. In this episode, Alizishaan Khatri of Wrynx joins Daniel and Chris to explore a fundamentally different approach to AI safety and interpretability. They unpack the limits of today's black-box defenses, the role of interpretability, and how model-native, runtime signals can enable safer AI systems. Featuring:Alizishaan Khatri – LinkedInChris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XUpcoming Events: Register for upcoming webinars here!

Send us a textWelcome back Rounds Table Listeners! We are back today with a solo episode with Dr. John Fralick. This week, he discusses a recently published trial examining the effect of a Mediterranean diet, compared with traditional dietary advice, on irritable bowel syndrome (IBS). Here we go!The Mediterranean Diet for Irritable Bowel Syndrome: A Randomized Clinical Trial (0:00 – 4:50).The Good Stuff (4:51 – 5:57):3Blue1Brown's Neural Network educational video series: Check out the playlist on YouTubeQuestions? Comments? Feedback? We'd love to hear from you! @roundstable @InternAtWork @MedicinePods

Traditional vulnerability management is simple: find the flaw, patch it, and verify the fix. But what happens when the "asset" is a neural network that has learned something ethically wrong? In this episode, Sapna Paul (Senior Manager at Dayforce) explains why there are no "Patch Tuesdays" for AI models .Sapna breaks down the three critical layers of AI vulnerability management: protecting production models, securing the data layer against poisoning, and monitoring model behavior for technically correct but ethically flawed outcomes . We discuss how to update your risk register to speak the language of business and the essential skills security professionals need to survive in an AI-first world .The conversation also covers practical ways to use AI within your security team to combat alert fatigue , the importance of explainability tools like SHAP and LIME , and how to align with frameworks like the NIST AI RMF and the EU AI Act .Guest Socials - ⁠⁠Sapna's LinkedinPodcast Twitter - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@CloudSecPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you want to watch videos of this LIVE STREAMED episode and past episodes - Check out our other Cloud Security Social Channels:-⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Podcast- Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠- ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Cloud Security Newsletter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠If you are interested in AI Security, you can check out our sister podcast -⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ AI Security Podcast⁠Questions asked:(00:00) Introduction(02:00) Who is Sapna Paul?(02:40) What is Vulnerability Management in the Age of AI? (05:00) Defining the New Asset: Neural Networks & Models (07:00) The 3 Layers of AI Vulnerability (Production, Data, Behavior) (10:20) Updating the Risk Register for AI Business Risks (13:30) Compliance vs. Innovation: Preventing AI from Going Rogue (18:20) Using AI to Solve Vulnerability Alert Fatigue (23:00) Skills Required for Future VM Professionals (25:40) Measuring AI Adoption in Security Teams (29:20) Key Frameworks: NIST AI RMF & EU AI Act (31:30) Tools for AI Security: Counterfit, SHAP, and LIME (33:30) Where to Start: Learning & Persona-Based Prompts (38:30) Fun Questions: Painting, Mentoring, and Vegan Ramen

THE AI WINTER Colleague Gary Rivlin. The history of Frank Rosenblatt's neural networks, their dismissal by Marvin Minsky in favor of rules-based computing, and the decades-long "winter" before the resurgence of machine learning. NUMBER 11

In this start-of-year FC episode, Chris and Daniel break down what really mattered in AI in 2025, and what to expect in 2026. They explore the rise of AI agents, the practical reality of multimodal AI, and how reasoning models are reshaping workflows. The conversation dives into infrastructure and energy constraints, the continued value of predictive models, and why orchestration (not just better models) is becoming the defining skill for AI teams. The episode wraps with grounded 2026 predictions on where AI systems, tooling, and builders are headed next.Featuring:Chris Benson – Website, LinkedIn, Bluesky, GitHub, XDaniel Whitenack – Website, GitHub, XSponsor:Framer - The enterprise-grade website builder that lets your team ship faster. Get 30% off at framer.com/practicalaiUpcoming Events: Register for upcoming webinars here!

It's faster than a speeding bullet. It's smarter than a polymath genius. It's everywhere but it's invisible. It's artificial intelligence. But what actually is it?Today we ask this simple question and explore why it's so damn hard to answer.Special thanks to Stephanie Yin and the New York Institute of Go for teaching us the game. Mark, Daria and Levon Hoover Brauner for helping bring NETtalk to life. And a huge thank you to Grant Sanderson for his unending patience explaining the math of neural nets to us. To learn more about how these 'thinking machines' actually think, we highly recommend his wonderful youtube channel 3Blue1Brown (https://www.youtube.com/watch?v=aircAruvnKk).EPISODE CREDITS: Reported by - Simon AdlerProduced by - Simon AdlerOriginal music from - Simon AdlerSound design contributed by - Simon AdlerFact-checking by - Anna Pujol-Mazzini Sign up for our newsletter!! It includes short essays, recommendations, and details about other ways to interact with the show. Signup (https://radiolab.org/newsletter)!Radiolab is supported by listeners like you. Support Radiolab by becoming a member of The Lab (https://members.radiolab.org/) today.Follow our show on Instagram, Twitter and Facebook @radiolab, and share your thoughts with us by emailing radiolab@wnyc.org.Leadership support for Radiolab's science programming is provided by the Gordon and Betty

Episode: 3343  Frank Rosenblatt's perceptron and the quest to design machines that can learn.  Today, the origin of learning in artificial neural networks.

Machine learning using neural networks has led to a remarkable leap forward in artificial intelligence, and the technological and social ramifications have been discussed at great length. To understand the origin and nature of this progress, it is useful to dig at least a little bit into the mathematical and algorithmic structures underlying these techniques. Anil Ananthaswamy takes up this challenge in his book Why Machines Learn: The Elegant Math Behind Modern AI. In this conversation we give a brief overview of some of the basic ideas, including the curse of dimensionality, backpropagation, transformer architectures, and more.Blog post with transcript: https://www.preposterousuniverse.com/podcast/2025/11/24/336-anil-ananthaswamy-on-the-mathematics-of-neural-nets-and-ai/Support Mindscape on Patreon.Anil Ananthaswamy received a Masters degree in electrical engineering from the University of Washington, Seattle. He is currently a freelance science writer and feature editor for PNAS Front Matter. He was formerly the deputy news editor for New Scientist, a Knight Science Journalism Fellow at MIT, and journalist-in-residence at the Simon Institute for the Theory of Computing, University of California, Berkeley. He organizes an annual science journalism workshop at the National Centre for Biological Sciences at Bengaluru, India.Web siteAmazon author pageWikipediaSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Episode: 3341 How Warren McCulloch and Walter Pitts laid the foundation for current AI research.  Today, the origin of artificial neural networks.

How is your brain like an ant colony? They both use simple parts following simple rules which allows the whole to be so much more than the sum of the parts. Listen as neuroscientist and author Gaurav Suri explains how the mind emerges from the neural network of the brain, why habits form, why intuition often knows before language does, and why our post-hoc explanations can mislead us. The conversation then grapples with free will and responsibility without mysticism. Ultimately, Suri remains in awe of the emergent mind and at the end of the conversation makes the case for the essential importance of kindness and forgiveness.