Welcome! We engage in fascinating discussions with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI, cognitive science, neuroscience and philosophy of mind with in-depth analysis. Our approach is unrivalled in terms of scope and rigour – we believe in intellectual diversity in AI, and we touch on all of the main ideas in the field with the hype surgically removed. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/) and features regular appearances from MIT Doctor of Philosophy Keith Duggar (https://www.linkedin.com/in/dr-keith-duggar/).
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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 ha...
Brad Carson was the Army's General Counsel, served two terms in Congress and was Acting Under Secretary of Defense for Personnel and Readiness. He now heads Americans for Responsible Innovation, the AI-policy advocacy group he co-founded. Keith Duggar spends roughly eighty minutes pushing back. 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 --- Carson's whole case res...
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 le...
Beth Barnes and David Rein on the one graph that ate the AI timelines discourse, and why the two people who built it are the most careful about how you read it.**SPONSOR**Prolific - Quality data. From real people. For faster breakthroughs.https://www.prolific.com/?utm_source=mlstInterview: https://youtu.be/cnxZZTl1tkk---Beth Barnes and David Rein from METR on the one graph that ate the AI timelines discourse, and why the people who built it are the most careful about how it gets read.Beth founde...
Robert Lange from Sakana AI introduces Shinka Evolve, a novel framework that integrates large language models with evolutionary algorithms for open-ended program search. He explains how Shinka addresses AlphaEvolve's limitations by automatically inventing new problems alongside optimizing solutions, drawing inspiration from concepts like POET and MAP-Elites. The discussion covers Shinka's architecture, adaptive model selection using UCB bandits, and its practical applications in diverse fields, envisioning a future where AI acts as a "co-scientist" amplifying human creativity in research.
Dive into the realities of AI-assisted coding, the origins of modern fine-tuning, and the cognitive science behind machine learning with fast.ai founder Jeremy Howard. In this episode, we unpack why AI might be turning software engineering into a slot machine and how to maintain true technical intuition in the age of large language models. GTC is coming, the premier AI conference, great opportunity to learn about AI. NVIDIA and partners will showcase breakthroughs in physical AI, AI factories, a...
What if life itself is just a really sophisticated computer program that wrote itself into existence? Blaise Agüera y Arcas presenting at ALife 2025 — the most technically detailed public walkthrough of the ideas in his *What is Life?* and *What is Intelligence?* books that we've come across.He covers the BFF experiments (self-replicating programs emerging spontaneously from random noise), the mathematical framework connecting Lotka-Volterra population dynamics with Smoluchowski coagulation, eig...
This episode features Dr. Jeff Beck discussing the core concepts of agency and intelligence, questioning if a rock can be an agent and differentiating between simulated and physical intelligent systems. He explains Energy-Based Models, their Bayesian connection, and LeCun's JEPA for learning in latent spaces. The conversation also touches on the brain's modular evolution and practical AI safety, advocating for using inverse reinforcement learning to guide AI behavior responsibly.
Professor Mazviita Chirimuuta joins us for a fascinating deep dive into the philosophy of neuroscience and what it really means to understand the mind.*What can neuroscience actually tell us about how the mind works?* In this thought-provoking conversation, we explore the hidden assumptions behind computational theories of the brain, the limits of scientific abstraction, and why the question of machine consciousness might be more complicated than AI researchers assume.Mazviita, author of *The Br...
This special edition episode delves into the history of scientific simplification, questioning whether our understanding of the brain is based on forgotten metaphors. Featuring insights from top thinkers like Karl Friston, Mazviita Chirimuuta, Francois Chollet, and John Jumper, the discussion examines the "spherical cow problem," the "kaleidoscope hypothesis," and the implications of viewing the mind as software. It explores the tension between prediction and understanding, the perceived inevitability of AGI, and the limits of human cognition, prompting listeners to consider what we might find naive about our current assumptions in the future.
Computational neuroscientist Dr. Jeff Beck argues that current AI development is backwards, advocating for an architecture that mimics the brain's scientific method and object-centered understanding. He explains how intelligence requires structured, causal models that can learn continually and know what they don't know, rather than relying solely on massive function approximation or language-based grounding. Beck proposes a "lots of little models" approach, akin to video game engines, to achieve human-like intelligence and address challenges like alignment and generalization.
This episode features Max Bennett discussing his book on the evolutionary history of intelligence, weaving together comparative psychology, neuroscience, and AI. He explains how the brain evolved from a "guessing machine" that infers reality to complex social and linguistic capabilities. The conversation covers topics like model-based reinforcement learning in animals, the origins of self-modeling, and the profound impact of language on human culture and collective intelligence. Bennett also draws parallels and distinctions between biological brains and modern AI, touching on the challenges of AI alignment, the nature of "world models," and the future of human-AI "cyborg" cognition.
This episode features César Hidalgo discussing his book, "The Infinite Alphabet," which outlines three fundamental laws of knowledge. He challenges the notion of knowledge as simple information, asserting it's a non-fungible, embodied, and collective phenomenon that decays rapidly without continuous application. The conversation delves into organizational learning, the dynamics of disruptive innovation, and how factors like migration and economic complexity shape a nation's ability to grow. Hidalgo also explores whether large language models contribute to collective intelligence, concluding they are valuable aids rather than independent knowledge holders.
This is a lively, no-holds-barred debate about whether AI can truly be intelligent, conscious, or understand anything at all — and what happens when (or if) machines become smarter than us. Dr. Mike Israetel is a sports scientist, entrepreneur, and co-founder of RP Strength (a fitness company). He describes himself as a "dilettante" in AI but brings a fascinating outsider's perspective. Jared Feather (IFBB Pro bodybuilder and exercise physiologist) The Big Questions: 1. When is superintelligence...
We often think of Large Language Models (LLMs) as all-knowing, but as the team reveals, they still struggle with the logic of a second-grader. Why can’t ChatGPT reliably add large numbers? Why does it "hallucinate" the laws of physics? The answer lies in the architecture. This episode explores how *Category Theory* —an ultra-abstract branch of mathematics—could provide the "Periodic Table" for neural networks, turning the "alchemy" of modern AI into a rigorous science. In this deep-dive explorat...
Is a car that wins a Formula 1 race the best choice for your morning commute? Probably not. In this sponsored deep dive with Prolific, we explore why the same logic applies to Artificial Intelligence. While models are currently shattering records on technical exams, they often fail the most important test of all: **the human experience.** Why High Benchmark Scores Don’t Mean Better AI Joining us are **Andrew Gordon** (Staff Researcher in Behavioral Science) and **Nora Petrova** (AI Researcher) f...
What if everything we think we know about AI understanding is wrong? Is compression the key to intelligence? Or is there something more—a leap from memorization to true abstraction? In this fascinating conversation, we sit down with **Professor Yi Ma**—world-renowned expert in deep learning, IEEE/ACM Fellow, and author of the groundbreaking new book *Learning Deep Representations of Data Distributions*. Professor Ma challenges our assumptions about what large language models actually do, reveals...
Pedro Domingos, author of the bestselling book "The Master Algorithm," introduces his latest work: Tensor Logic - a new programming language he believes could become the fundamental language for artificial intelligence. Think of it like this: Physics found its language in calculus. Circuit design found its language in Boolean logic. Pedro argues that AI has been missing its language - until now. **SPONSOR MESSAGES START** — Build your ideas with AI Studio from Google - http://ai.studio/build — P...
The Transformer architecture (which powers ChatGPT and nearly all modern AI) might be trapping the industry in a localized rut, preventing us from finding true intelligent reasoning, according to the person who co-invented it. Llion Jones and Luke Darlow, key figures at the research lab Sakana AI, join the show to make this provocative argument, and also introduce new research which might lead the way forwards. **SPONSOR MESSAGES START** — Build your ideas with AI Studio from Google - http://ai....
Ever wonder where AI models actually get their "intelligence"? We reveal the dirty secret of Silicon Valley: behind every impressive AI system are thousands of real humans providing crucial data, feedback, and expertise.Guest: Phelim Bradley, CEO and Co-founder of ProlificPhelim Bradley runs Prolific, a platform that connects AI companies with verified human experts who help train and evaluate their models. Think of it as a sophisticated marketplace matching the right human expertise to the righ...
"What is life?" - asks Chris Kempes, a professor at the Santa Fe Institute. Chris explains that scientists are moving beyond a purely Earth-based, biological view and are searching for a universal theory of life that could apply to anything, anywhere in the universe. He proposes that things we don't normally consider "alive"—like human culture, language, or even artificial intelligence; could be seen as life forms existing on different "substrates". To understand this, Chris presents a fascinati...
Blaise Agüera y Arcas explores some mind-bending ideas about what intelligence and life really are—and why they might be more similar than we think (filmed at ALIFE conference, 2025 - https://2025.alife.org/). Life and intelligence are both fundamentally computational (he says). From the very beginning, living things have been running programs. Your DNA? It's literally a computer program, and the ribosomes in your cells are tiny universal computers building you according to those instructions. *...
We sat down with Sara Saab (VP of Product at Prolific) and Enzo Blindow (VP of Data and AI at Prolific) to explore the critical role of human evaluation in AI development and the challenges of aligning AI systems with human values. Prolific is a human annotation and orchestration platform for AI used by many of the major AI labs. This is a sponsored show in partnership with Prolific. **SPONSOR MESSAGES** — cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerati...
Dr. Ilia Shumailov - Former DeepMind AI Security Researcher, now building security tools for AI agents Ever wondered what happens when AI agents start talking to each other—or worse, when they start breaking things? Ilia Shumailov spent years at DeepMind thinking about exactly these problems, and he's here to explain why securing AI is way harder than you think. **SPONSOR MESSAGES** —Check out notebooklm for your research project, it's really powerfulhttps://notebooklm.google.com/ — Take the Pro...
We need AI systems to synthesise new knowledge, not just compress the data they see. Jeremy Berman, is a research scientist at Reflection AI and recent winner of the ARC-AGI v2 public leaderboard.**SPONSOR MESSAGES**—Take the Prolific human data survey - https://www.prolific.com/humandatasurvey?utm_source=mlst and be the first to see the results and benchmark their practices against the wider community!—cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating ...
Professor Andrew Wilson from NYU explains why many common-sense ideas in artificial intelligence might be wrong. For decades, the rule of thumb in machine learning has been to fear complexity. The thinking goes: if your model has too many parameters (is "too complex") for the amount of data you have, it will "overfit" by essentially memorizing the data instead of learning the underlying patterns. This leads to poor performance on new, unseen data. This is known as the classic "bias-variance trad...
In this episode, hosts Tim and Keith finally realize their long-held dream of sitting down with their hero, the brilliant neuroscientist Professor Karl Friston. The conversation is a fascinating and mind-bending journey into Professor Friston's life's work, the Free Energy Principle, and what it reveals about life, intelligence, and consciousness itself. **SPONSORS** Gemini CLI is an open-source AI agent that brings the power of Gemini directly into your terminal - https://github.com/google-gemi...
We are joined by Cristopher Moore, a professor at the Santa Fe Institute with a diverse background in physics, computer science, and machine learning. The conversation begins with Cristopher, who calls himself a "frog" explaining that he prefers to dive deep into specific, concrete problems rather than taking a high-level "bird's-eye view". They explore why current AI models, like transformers, are so surprisingly effective. Cristopher argues it's because the real world isn't random; it's full o...
Dr. Michael Timothy Bennett is a computer scientist who's deeply interested in understanding artificial intelligence, consciousness, and what it means to be alive. He's known for his provocative paper "What the F*** is Artificial Intelligence" which challenges conventional thinking about AI and intelligence.**SPONSOR MESSAGES***Prolific: Quality data. From real people. For faster breakthroughs.https://prolific.com/mlst?utm_campaign=98404559-MLST&utm_source=youtube&utm_medium=podcast&...
Deep dive with Dan Hendrycks, a leading AI safety researcher and co-author of the "Superintelligence Strategy" paper with former Google CEO Eric Schmidt and Scale AI CEO Alexandr Wang. *** SPONSOR MESSAGES Gemini CLI is an open-source AI agent that brings the power of Gemini directly into your terminal - https://github.com/google-gemini/gemini-cli Prolific: Quality data. From real people. For faster breakthroughs. https://prolific.com/mlst?utm_campaign=98404559-MLST&utm_source=youtube&ut...
