Machine Learning Street Talk (MLST)

Machine Learning Street Talk (MLST)

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/).

  1. He won a Nobel here for AlphaFold. Then he left. - John Jumper

    13 hr ago

    He won a Nobel here for AlphaFold. Then he left. - John Jumper

    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

    53 min
  2. When AI Decides You're a Threat — Brad Carson

    31 May

    When AI Decides You're a Threat — Brad Carson

    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 rests on one line: the genie is not out of the bottle. We have pulled dangerous tech back before. Asilomar halted recombinant DNA in 1975, and the West still controls the chips AI runs on. Calling it unstoppable, he says, is the most dangerous idea in the room. Then Keith drags him somewhere darker. A Palantir heat map scores you 0.73 on whether you are a combatant, and a strike follows. The model is wrong some accepted share of the time, and when it is, nobody answers for it. You cannot court-martial a model, and not even the interpretability researchers can say why it picked you. — Note: after recording, we learned that Americans for Responsible Innovation is backed by EA-aligned philanthropy (not sponsored) --- TIMESTAMPS: 00:00:00 From the Pentagon to AI governance 00:04:52 Regulatory capture vs Silicon Valley networks 00:07:56 Transparency and the Claude tier changes 00:09:40 Tort liability when AI tools cause harm 00:13:40 AI is a product, not a person 00:16:01 Children, suicide, and the suicide business 00:19:59 Opaque neural nets and the law of war 00:25:54 Probabilistic targeting and the death of accountability 00:28:47 The arms race fallacy: Asilomar and restraint 00:34:02 Talking to China: track 2 talks and chip leverage 00:39:45 Air power never wins: capital for labour 00:43:29 Anthropic vs the Department of War 00:51:29 Concentration, open source, and brain drain 01:00:18 DeepSeek, Chinese culture, and AI as diplomacy 01:12:25 Upskilling Congress and why public trust matters --- REFERENCES: organization: [00:02:45] ICRC position on autonomous weapons https://www.icrc.org/en/law-and-policy/autonomous-weapons [00:05:22] Americans for Responsible Innovation (ARI) https://ari.us [00:07:20] Andreessen Horowitz (a16z) https://a16z.com/ [01:16:05] Office of Technology Assessment https://en.wikipedia.org/wiki/Office_of_Technology_Assessment other: [00:03:35] Beneficial AGI 2019 Conference (Future of Life Institute, Puerto Rico) https://futureoflife.org/event/beneficial-agi-2019/ [00:18:30] Section 230 of the Communications Decency Act https://en.wikipedia.org/wiki/Section_230 [00:19:59] Lethal Autonomous Weapons (LAWS) https://en.wikipedia.org/wiki/Lethal_autonomous_weapon [00:31:35] Strategic Arms Limitation Talks (SALT) https://en.wikipedia.org/wiki/Strategic_Arms_Limitation_Talks [00:32:28] Asilomar Conference on Recombinant DNA (1975) https://en.wikipedia.org/wiki/Asilomar_Conference_on_Recombinant_DNA [00:39:45] The New Iron Triangle (ARI policy byte) https://ari.us/policy-bytes/the-new-iron-triangle/ [00:48:05] Defense Production Act https://en.wikipedia.org/wiki/Defense_Production_Act person: [00:03:35] Anthony Aguirre https://en.wikipedia.org/wiki/Anthony_Aguirre [00:06:48] Dean Ball — Hyperdimensional https://www.hyperdimensional.co/ [00:23:13] Neel Nanda — mechanistic interpretability https://www.neelnanda.io/ [00:36:02] Jack Clark (Anthropic) on Conversations with Tyler https://conversationswithtyler.com/episodes/jack-clark/ [00:39:15] Robert Trager — Centre for the Governance of AI https://www.governance.ai/team/robert-trager [00:41:55] Giulio Douhet https://en.wikipedia.org/wiki/Giulio_Douhet [01:15:05] Don Beyer (US Congress) https://en.wikipedia.org/wiki/Don_Beyer tool: [00:22:19] Phalanx CIWS https://en.wikipedia.org/wiki/Phalanx_CIWS --- ReScript: https://app.rescript.info/public/share/9405ff35c0215b7cdae6402d41284171 https://app.rescript.info/api/public/sessions/0a6c081b8e5fe413/pdf

    1hr 21min
  3. Intelligence is collective, not artificial — Prof. Michael I. Jordan (UC Berkeley / Inria)

    21 May

    Intelligence is collective, not artificial — Prof. Michael I. Jordan (UC Berkeley / Inria)

    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 builders 00:02:04 CyberFund sponsor read 00:02:50 From symbolic AI to machine learning systems 00:05:42 Why AGI is mostly a PR term 00:08:48 A collectivist, economic perspective on AI 00:11:33 Why LLMs need system design, not hype 00:14:50 Predictability beats faux understanding 00:17:55 AlphaFold, bias, and prediction-powered inference 00:21:48 Stop anthropomorphizing intelligence 00:27:44 Drug discovery as an incentive problem 00:32:29 The three-layer data market 00:38:07 Social knowledge, markets, and culture 00:45:39 Creator economics beyond Spotify 00:48:30 How science-fiction AI narratives mislead young builders 00:51:45 AI should improve humans, not replace them 00:56:42 Safety is a property of the whole system 00:58:12 Silicon Valley gurus and the cream off the top 01:00:47 Game theory, mechanism design, and contracts 01:04:39 Conformal prediction, e-values, and anytime inference 01:08:11 A new liberal arts triangle for the AI era 01:11:30 The Bayesian duck and markets as uncertainty reduction ReScript (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 AI https://arxiv.org/abs/2507.06268 [00:18:09] AlphaFold https://www.nature.com/articles/s41586-021-03819-2 [00:20:36] Prediction-Powered Inference https://arxiv.org/abs/2301.09633 [00:33:47] On Three-Layer Data Markets https://arxiv.org/abs/2402.09697 [01:04:39] Conformal Prediction with Conditional Guarantees https://arxiv.org/abs/2107.07511 [01:04:51] A Tutorial on Conformal Prediction https://www.jmlr.org/papers/v9/shafer08a.html [01:06:00] E-Values Expand the Scope of Conformal Prediction https://arxiv.org/abs/2503.13050 [01:08:23] Computational Thinking https://www.cs.cmu.edu/~CompThink/papers/Wing06.pdf other: [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

    1hr 17min
  4. The AI Models Smart Enough to Know They're Cheating — Beth Barnes & David Rein [METR]

    4 May

    The AI Models Smart Enough to Know They're Cheating — Beth Barnes & David Rein [METR]

    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 founded METR after leaving OpenAI alignment. David is first author on GPQA and co-author on HCAST and the METR Time Horizons paper. Together they built the measurement Daniel Kokotajlo called the single most important piece of evidence on AI timelines: the log-linear line of "how long a task a frontier model can complete at 50% reliability" vs release date.The conversation opens on reward hacking. Current models can articulate in chat why a behaviour is undesired and then execute it anyway as agents. From there: construct validity, Melanie Mitchell's four-problem taxonomy, and the ARC-AGI 1-to-2 collapse as a worked example of adversarially-selected benchmarks regressing once labs target them. Beth's counter: METR deliberately does not adversarially select. David's: models do not have to do the right thing for the right reasons.Methodology, then specification — David's compiler analogy, Beth on four-month tasks as expensive to evaluate rather than unspecifiable. Then the SWE-bench reality check, the METR finding that half of passing PRs would not be merged, and Beth's horses-versus-bank-tellers analogy for the labour market.The close: monitorability, the coin-spinning boat, two-year recursive self-improvement, and Beth's line that "overhyped now" and "big deal later" are not correlated claims.---TIMESTAMPS:00:00:00 Intro00:02:06 Sponsor break: Prolific human-feedback infrastructure00:02:33 Welcome and the scalable oversight motivation00:06:02 Construct validity, benchmark pathologies and the Chollet worry00:15:45 Time Horizons: human time, HCAST tasks and the 50% logistic00:24:50 Is human difficulty really one variable?00:33:05 Agent harness evolution and the inference-compute dividend00:40:00 Scaffolding bells, token budgets and the credit-assignment problem00:44:15 Look at the damn graph: regularisation bug and reliability nuance00:50:00 Why 50%? Reliability, reward hacking and pizza-party transcripts00:55:20 Extrapolation risk and straight lines on graphs00:59:25 Software engineering as a specification acquisition problem01:07:40 Compilers also made ugly code: vibe-coding quality and Claude on METR Slack01:15:15 Strongest defensible claim, Carlini's compiler swarm and AI 202701:23:45 SWE-bench merge rates, the bank-teller analogy and horses01:31:45 Scheming, alignment faking and the mentalistic vocabulary problem01:40:45 Reward hacking, monitorability and chain-of-thought faithfulness01:45:25 Recursive self-improvement, knowledge vs intelligence and closing ReScript: https://app.rescript.info/public/share/de3bb40cc02ee39fdf36e2c60366eb4d (PDF, refs, transcript etc)

    1hr 53min
  5. When AI Discovers The Next Transformer - Robert Lange (Sakana)

    13 Mar

    When AI Discovers The Next Transformer - Robert Lange (Sakana)

    Robert Lange, founding researcher at Sakana AI, joins Tim to discuss *Shinka Evolve* — a framework that combines LLMs with evolutionary algorithms to do open-ended program search. The core claim: systems like AlphaEvolve can optimize solutions to fixed problems, but real scientific progress requires co-evolving the problems themselves. GTC is coming, the premier AI conference, great opportunity to learn about AI. NVIDIA and partners will showcase breakthroughs in physical AI, AI factories, agentic AI, and inference, exploring the next wave of AI innovation for developers and researchers. Register for virtual GTC for free, using my link and win NVIDIA DGX Spark (https://nvda.ws/4qQ0LMg) • Why AlphaEvolve gets stuck — it needs a human to hand it the right problem. Shinka tries to invent new problems automatically, drawing on ideas from POET, PowerPlay, and MAP-Elites quality-diversity search. • The *architecture* of Shinka: an archive of programs organized as islands, LLMs used as mutation operators, and a UCB bandit that adaptively selects between frontier models (GPT-5, Sonnet 4.5, Gemini) mid-run. The credit-assignment problem across models turns out to be genuinely hard. • Concrete results — state-of-the-art circle packing with dramatically fewer evaluations, second place in an AtCoder competitive programming challenge, evolved load-balancing loss functions for mixture-of-experts models, and agent scaffolds for AIME math benchmarks. • Are these systems actually thinking outside the box, or are they parasitic on their starting conditions? When LLMs run autonomously, "nothing interesting happens." Robert pushes back with the stepping-stone argument — evolution doesn't need to extrapolate, just recombine usefully. • The AI Scientist question: can automated research pipelines produce real science, or just workshop-level slop that passes surface-level review? Robert is honest that the current version is more co-pilot than autonomous researcher. • Where this lands in 5-20 years — Robert's prediction that scientific research will be fundamentally transformed, and Tim's thought experiment about alien mathematical artifacts that no human could have conceived. Robert Lange: https://roberttlange.com/ --- TIMESTAMPS: 00:00:00 Introduction: Robert Lange, Sakana AI and Shinka Evolve 00:04:15 AlphaEvolve's Blind Spot: Co-Evolving Problems with Solutions 00:09:05 Unknown Unknowns, POET, and Auto-Curricula for AI Science 00:14:20 MAP-Elites and Quality-Diversity: Shinka's Evolutionary Architecture 00:28:00 UCB Bandits, Mutations and the Vibe Research Vision 00:40:00 Scaling Shinka: Meta-Evolution, Democratisation and the Three-Axis Model 00:47:10 Applications, ARC-AGI and the Future of Work 00:57:00 The AI Scientist and the Human Co-Pilot: Who Steers the Search? 01:06:00 AI Scientist v2, Slop Critique and the Future of Scientific Publishing --- REFERENCES: paper: [00:03:30] ShinkaEvolve: Towards Open-Ended And Sample-Efficient Program Evolution https://arxiv.org/abs/2509.19349 [00:04:15] AlphaEvolve: A Coding Agent for Scientific and Algorithmic Discovery https://arxiv.org/abs/2506.13131 [00:06:30] Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents https://arxiv.org/abs/2505.22954 [00:09:05] Paired Open-Ended Trailblazer (POET) https://arxiv.org/abs/1901.01753 [00:10:00] PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem https://arxiv.org/abs/1112.5309 [00:10:40] Automated Capability Discovery via Foundation Model Self-Exploration https://arxiv.org/abs/2502.07577 [00:15:30] Illuminating Search Spaces by Mapping Elites (MAP-Elites) https://arxiv.org/abs/1504.04909 [00:47:10] Automated Design of Agentic Systems (ADAS) https://arxiv.org/abs/2408.08435 PDF : https://app.rescript.info/api/sessions/b8a9dcf60623657c/pdf/download Transcript: https://app.rescript.info/public/share/SDOD_3oXOcli3zTqcAtR8eibT5U3gam84oo4KRtI-Vk

    1hr 18min
  6. "Vibe Coding is a Slot Machine" - Jeremy Howard

    3 Mar

    "Vibe Coding is a Slot Machine" - Jeremy Howard

    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, agentic AI, and inference, exploring the next wave of AI innovation for developers and researchers. Register for virtual GTC for free, using my link and win NVIDIA DGX Spark (https://nvda.ws/4qQ0LMg) Jeremy Howard is a renowned data scientist, researcher, entrepreneur, and educator. As the co-founder of fast.ai, former President of Kaggle, and the creator of ULMFiT, Jeremy has spent decades democratizing deep learning. His pioneering work laid the foundation for modern transfer learning and the pre-training and fine-tuning paradigm that powers today's language models. Key Topics and Main Insights Discussed: - The Origins of ULMFiT and Fine-Tuning - The Vibe Coding Illusion and Software Engineering - Cognitive Science, Friction, and Learning - The Future of Developers RESCRIPT: https://app.rescript.info/public/share/BhX5zP3b0m63srLOQDKBTFTooSzEMh_ARwmDG_h_izk Jeremy Howard: https://x.com/jeremyphoward https://www.answer.ai/ --- TIMESTAMPS (fixed): 00:00:00 Introduction & GTC Sponsor 00:04:30 ULMFiT & The Birth of Fine-Tuning 00:12:00 Intuition & The Mechanics of Learning 00:18:30 Abstraction Hierarchies & AI Creativity 00:23:00 Claude Code & The Interpolation Illusion 00:27:30 Coding vs. Software Engineering 00:30:00 Cosplaying Intelligence: Dennett vs. Searle 00:36:30 Automation, Radiology & Desirable Difficulty 00:42:30 Organizational Knowledge & The Slope 00:48:00 Vibe Coding as a Slot Machine 00:54:00 The Erosion of Control in Software 01:01:00 Interactive Programming & REPL Environments 01:05:00 The Notebook Debate & Exploratory Science 01:17:30 AI Existential Risk & Power Centralization 01:24:20 Current Risks, Privacy & Enfeeblement --- REFERENCES: Blog Post: [00:03:00] fast.ai Blog: Self-Supervised Learning https://www.fast.ai/posts/2020-01-13-self_supervised.html [00:13:30] DeepMind Blog: Gemini Deep Think https://deepmind.google/blog/accelerating-mathematical-and-scientific-discovery-with-gemini-deep-think/ [00:19:30] Modular Blog: Claude C Compiler analysis https://www.modular.com/blog/the-claude-c-compiler-what-it-reveals-about-the-future-of-software [00:19:45] Anthropic Engineering Blog: Building C Compiler https://www.anthropic.com/engineering/building-c-compiler [00:48:00] Cursor Blog: Scaling Agents https://cursor.com/blog/scaling-agents [01:05:15] fast.ai Blog: NB Dev Merged Driver https://www.fast.ai/posts/2022-08-25-jupyter-git.html [01:17:30] Jeremy Howard: Response to AI Risk Letter https://www.normaltech.ai/p/is-avoiding-extinction-from-ai-really Book: [00:08:30] M. Chirimuuta: The Brain Abstracted https://mitpress.mit.edu/9780262548045/the-brain-abstracted/ [00:30:00] Daniel Dennett: Consciousness Explained https://www.amazon.com/Consciousness-Explained-Daniel-C-Dennett/dp/0316180661 [00:42:30] Cesar Hidalgo: Infinite Alphabet / Laws of Knowledge https://www.amazon.com/Infinite-Alphabet-Laws-Knowledge/dp/0241655676 Archive Article: [00:13:45] MLST Archive: Why Creativity Cannot Be Interpolated https://archive.mlst.ai/read/why-creativity-cannot-be-interpolated Research Study: [00:24:30] METR Study: AI OS Development https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/ Paper: [00:24:45] Fred Brooks: No Silver Bullet https://www.cs.unc.edu/techreports/86-020.pdf [00:30:15] John Searle: Minds, Brains, and Programs https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/minds-brains-and-programs/DC644B47A4299C637C89772FACC2706A

    1hr 27min
  7. Evolution "Doesn't Need" Mutation - Blaise Agüera y Arcas

    16 Feb

    Evolution "Doesn't Need" Mutation - Blaise Agüera y Arcas

    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, eigenvalue analysis of cooperation matrices, and his central claim that symbiogenesis — not mutation — is the primary engine of evolutionary novelty.The experimental results are genuinely striking: complex self-replicating code arising from random byte strings with zero mutation, a sharp phase transition that looks like gelation, and a proof that blocking deep symbiogenetic ancestry trees prevents the transition entirely.A few things worth flagging for critical viewers:— The substrate is more carefully engineered than the framing sometimes suggests. The choice of language, tape length, interaction protocol, and step limits all shape what emerges. Their own SUBLEQ counterexample (where self-replicators *don't* arise despite being theoretically possible) highlights that these design choices matter substantially — and a general theory of which substrates support this transition is still missing.— The leap from "self-replicating programs on fixed-length tapes" to "life was computational and intelligent from the start" involves significant philosophical extrapolation beyond what the experiments directly demonstrate.— The Bedau et al. (2000) open problems paper he references at the start actually sets a higher bar for Challenge 3.2 than BFF currently meets: it asks that "the internal organization of these 'organisms' and the boundaries separating them from their environment arise and be sustained through the activities of lower-level primitives" — whereas BFF's tape boundaries are fixed by design, not emergent. --- TIMESTAMPS: 00:00:00 Introduction: From Noise to Programs & ALife History 00:03:15 Defining Life: Function as the "Spirit" 00:05:45 Von Neumann's Insight: Life is Embodied Computation 00:09:15 Physics of Computation: Irreversibility & Fallacies 00:15:00 The BFF Experiment: Spontaneous Generation of Code 00:23:45 The Mystery: Complexity Growth Without Mutation 00:27:00 Symbiogenesis: The Engine of Novelty 00:33:15 Mathematical Proof: Blocking Symbiosis Stops Life 00:40:15 Evolutionary Implications: It's Symbiogenesis All The Way Down 00:44:30 Intelligence as Modeling Others 00:46:49 Q&A: Levels of Abstraction & Definitions --- REFERENCES: Paper: [00:01:16] Open Problems in Artificial Life https://direct.mit.edu/artl/article/6/4/363/2354/Open-Problems-in-Artificial-Life [00:09:30] When does a physical system compute? https://arxiv.org/abs/1309.7979 [00:15:00] Computational Life https://arxiv.org/abs/2406.19108 [00:27:30] On the Origin of Mitosing Cells https://pubmed.ncbi.nlm.nih.gov/11541392/ [00:42:00] The Major Evolutionary Transitions https://www.nature.com/articles/374227a0 [00:44:00] The ARC gene https://www.nih.gov/news-events/news-releases/memory-gene-goes-viral Person: [00:05:45] Alan Turing https://plato.stanford.edu/entries/turing/ [00:07:30] John von Neumann https://en.wikipedia.org/wiki/John_von_Neumann [00:11:15] Hector Zenil https://hectorzenil.net/ [00:12:00] Robert Sapolsky https://profiles.stanford.edu/robert-sapolsky --- LINKS: RESCRIPT: https://app.rescript.info/public/share/ff7gb6HpezOR3DF-gr9-rCoMFzzEgUjLQK6voV5XVWY

    56 min
  8. VAEs Are Energy-Based Models? [Dr. Jeff Beck]

    25 Jan

    VAEs Are Energy-Based Models? [Dr. Jeff Beck]

    What makes something truly *intelligent?* Is a rock an agent? Could a perfect simulation of your brain actually *be* you? In this fascinating conversation, Dr. Jeff Beck takes us on a journey through the philosophical and technical foundations of agency, intelligence, and the future of AI. Jeff doesn't hold back on the big questions. He argues that from a purely mathematical perspective, there's no structural difference between an agent and a rock – both execute policies that map inputs to outputs. The real distinction lies in *sophistication* – how complex are the internal computations? Does the system engage in planning and counterfactual reasoning, or is it just a lookup table that happens to give the right answers? *Key topics explored in this conversation:* *The Black Box Problem of Agency* – How can we tell if something is truly planning versus just executing a pre-computed response? Jeff explains why this question is nearly impossible to answer from the outside, and why the best we can do is ask which model gives us the simplest explanation. *Energy-Based Models Explained* – A masterclass on how EBMs differ from standard neural networks. The key insight: traditional networks only optimize weights, while energy-based models optimize *both* weights and internal states – a subtle but profound distinction that connects to Bayesian inference. *Why Your Brain Might Have Evolved from Your Nose* – One of the most surprising moments in the conversation. Jeff proposes that the complex, non-smooth nature of olfactory space may have driven the evolution of our associative cortex and planning abilities. *The JEPA Revolution* – A deep dive into Yann LeCun's Joint Embedding Prediction Architecture and why learning in latent space (rather than predicting every pixel) might be the key to more robust AI representations. *AI Safety Without Skynet Fears* – Jeff takes a refreshingly grounded stance on AI risk. He's less worried about rogue superintelligences and more concerned about humans becoming "reward function selectors" – couch potatoes who just approve or reject AI outputs. His proposed solution? Use inverse reinforcement learning to derive AI goals from observed human behavior, then make *small* perturbations rather than naive commands like "end world hunger." Whether you're interested in the philosophy of mind, the technical details of modern machine learning, or just want to understand what makes intelligence *tick,* this conversation delivers insights you won't find anywhere else. --- TIMESTAMPS: 00:00:00 Geometric Deep Learning & Physical Symmetries 00:00:56 Defining Agency: From Rocks to Planning 00:05:25 The Black Box Problem & Counterfactuals 00:08:45 Simulated Agency vs. Physical Reality 00:12:55 Energy-Based Models & Test-Time Training 00:17:30 Bayesian Inference & Free Energy 00:20:07 JEPA, Latent Space, & Non-Contrastive Learning 00:27:07 Evolution of Intelligence & Modular Brains 00:34:00 Scientific Discovery & Automated Experimentation 00:38:04 AI Safety, Enfeeblement & The Future of Work --- REFERENCES: Concept: [00:00:58] Free Energy Principle (FEP) https://en.wikipedia.org/wiki/Free_energy_principle [00:06:00] Monte Carlo Tree Search https://en.wikipedia.org/wiki/Monte_Carlo_tree_search Book: [00:09:00] The Intentional Stance https://mitpress.mit.edu/9780262540537/the-intentional-stance/ Paper: [00:13:00] A Tutorial on Energy-Based Learning (LeCun 2006) http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf [00:15:00] Auto-Encoding Variational Bayes (VAE) https://arxiv.org/abs/1312.6114 [00:20:15] JEPA (Joint Embedding Prediction Architecture) https://openreview.net/forum?id=BZ5a1r-kVsf [00:22:30] The Wake-Sleep Algorithm https://www.cs.toronto.edu/~hinton/absps/ws.pdf --- RESCRIPT: https://app.rescript.info/public/share/DJlSbJ_Qx080q315tWaqMWn3PixCQsOcM4Kf1IW9_Eo PDF: https://app.rescript.info/api/public/sessions/0efec296b9b6e905/pdf

    47 min
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About

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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  • Creator
    Machine Learning Street Talk (MLST)
  • Years Active
    2020 - 2026
  • Episodes
    253
  • Rating
    Explicit
  • Copyright
    © Machine Learning Street Talk (MLST)
  • Show Website
    Machine Learning Street Talk (MLST)

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