Machine Learning Street Talk (MLST)

Machine Learning Street Talk (MLST)/Jul 31, 2026/1h 19m

How Researchers Test AI for Hidden Goals — Apollo Research

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

Researcher

Researcher

Researcher

Machine Learning Street Talk (MLST)/Jul 13, 2026/56m

Why a Nation Can't Outsource Its Frontier AI - Alistair Pullen (Cosine AI)

This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at https://notion.com/mlstBritain's most capable coding model can't be exported, and that ban is the whole reason Cosine set out to build one from scratch. Alistair Pullen, CEO and co-founder of Cosine, sits down with Tim Scarfe to explain how a frontier system he calls Fable, locked behind US export controls, became the founding case for a UK sovereign model trained on the Isambard supercomputer in Bristol.The bet underneath it is economic...

Alistair Pullen

Co-founder & CEO, Cosine

Cosine CACosine AI

Machine Learning Street Talk (MLST)/Jul 1, 2026/1h 25m

The Benchmark With No Instructions — ARC-AGI-3 (winning team!)

Tim Scarfe travels to Zurich to sit down with the Tufa Labs ARC-AGI-3 team — founder Benjamin Crouzier, with Jeroen Cottaar, Dries Smit, Stefano Viel and Michal Tesnar — to work out what their leaderboard-topping system does and what the benchmark is really testing.The cut opens on the games: a walkthrough of the Locksmith game, where you read the rules of an unfamiliar world straight from raw frames. ARC-AGI-3 makes ARC interactive and agentic, so the model has to *discover* the goal rather than transduce a static grid. It stays easy for humans and breaks LLMs, and it runs through ever...

Founder

Machine Learning Street Talk (MLST)/Jun 28, 2026/1h 3m

The Thermodynamic AI Computing Chip - Thomas Ahle

Thomas Ahle wants Normal Computing to be the Lovable for chip design: type your intent, and a swarm of agents carries it from design through optimisation, formalisation and verification to tape-out. To get there, his team at wrote their own open-source Verilog simulator, 580,000 lines in 43 days, because commercial EDA verifiers run about $10,000 per core and there are no decent open-source compilers to build on. That sets up the question Tim keeps pressing: if an agent can produce a chip design, a proof, or a working program, how do you actually know it is correct? Passing 70% of tests is not the same as being right, and a single fabricated bug can cost a company a fortune. They dig into ProgramBench (rebuild a program from its tests, roughly 0% success), the difference between structure and competence, and the "understanding debt" you take on when nobody reads the code. From there: auto-formalisation in Lean and the AlphaProof trick of training on prove-or-disprove; why there is no single true representation of a spec (Petri nets, TLA+, Erik Curiel's "math does not represent"); and thermodynamic computing, where Normal Computing's CN101 chip is built so that its physical noise *is* the computation, settling a stochastic differential equation in hardware to invert a matrix. Plus Bayesian uncertainty, specialisation, the Chomsky hierarchy, AI slop, and whether performance is all that matters. Recorded in Zurich. Disclosure: Normal Computing paid our production and travel costs for this show. We retained full editorial control. They did not see the video before publication, and we did not show it to them or discuss it with them beforehand. --- TIMESTAMPS: 00:00:00 Meet Thomas Ahle: the Lovable for chip design 00:03:41 Why hardware needs formal verification 00:06:36 Ten thousand dollars per core and a six-month agent run 00:07:40 Rebuilding programs from tests: ProgramBench and zero percent 00:12:15 Structure vs competence: can you learn a program from behavior? 00:15:27 Continual learning, abstraction, and Claude as an ecosystem 00:23:17 Autoformalization and the AlphaProof trick 00:29:31 No single true representation: specs, Petri nets and TLA+ 00:34:43 Thermodynamic computing: when noise is the computation 00:37:32 Bayesian uncertainty in the age of token streams 00:41:12 Hybrid compute: vibe-coding loops, binaries and Stockfish 00:44:44 Co-design, central-AI apps and API pricing 00:49:45 Chain of thoughtlessness and the Chomsky hierarchy 00:53:40 AI psychosis, slop and the broken social contract 00:57:34 Typing it yourself, teamwork and performance vs competence --- REFERENCES: person: [00:00:10] Thomas Ahle https://thomasahle.com organization: [00:00:27] Normal Computing https://normalcomputing.com/ paper: [00:11:21] ProgramBench: Can Language Models Rebuild Programs From Scratch? https://arxiv.org/abs/2605.03546 [00:31:55] Autoformalizing Memory Device Specifications with Agents https://arxiv.org/abs/2605.00058 [00:35:20] Thermo AI and the Fluctuation Frontier https://arxiv.org/abs/2302.06584 [00:36:40] Thermo Comp System for AI Applications https://arxiv.org/abs/2312.04836 [00:37:05] Thermodynamic Linear Algebra https://arxiv.org/abs/2308.05660 [00:44:50] An efficient probabilistic hardware architecture for diffusion-like models https://arxiv.org/abs/2510.23972 tool: other: [00:01:00] Building an Open-Source Verilog Simulator with AI: 580K Lines in 43 Days https://normalcomputing.com/blog/building-an-open-source-verilog-simulator-with-ai-580k-lines-in-43-days [00:02:55] Normal Computing Announces Tape-Out of the World's First Thermodynamic Computing Chip (CN101) https://www.normalcomputing.com/blog/normal-computing-announces-tape-out-of-worlds-first-thermodynamic-computing-chip [00:32:02] DRAMBench: Autoformalizing DRAM Specifications with Timed Petri Nets https://www.iese.fraunhofer.de/blog/drambench-autoformalizing-dram-specifications/ --- ReScript: https://app.rescript.info/share/ff9684a112ab37744096adaeb097a263

Thomas Ahle

Founder

Normal Computing

Machine Learning Street Talk (MLST)/Jun 22, 2026/53m

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

John Jumper

Research Scientist

Anthropic

Machine Learning Street Talk (MLST)/May 31, 2026/1h 21m

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

Brad Carson

Co-Founder and President

Americans for Responsible Innovation

Machine Learning Street Talk (MLST)/May 21, 2026/1h 17m

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 <truncated, see ReScript link or YT VD>

Michael I. Jordan

Professor

UC Berkeley

Machine Learning Street Talk (MLST)/May 4, 2026/1h 53m

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)

Founder

Researcher

Machine Learning Street Talk (MLST)/Mar 13, 2026/1h 18m

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 <trunc, see ReScript/YT> PDF : https://app.rescript.info/api/sessions/b8a9dcf60623657c/pdf/download Transcript: https://app.rescript.info/public/share/SDOD_3oXOcli3zTqcAtR8eibT5U3gam84oo4KRtI-Vk

Robert Lange

Founding Researcher

Sakana AI

Technical University Berlin

Machine Learning Street Talk (MLST)/Mar 3, 2026/1h 27m

"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

Jeremy Howard

Founder, Answer.AI

Answer.AI

fast.ai

Machine Learning Street Talk (MLST)/Feb 16, 2026/56m

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

Blaise Agüera y Arcas

VP and Fellow

Google

Machine Learning Street Talk (MLST)/Jan 25, 2026/47m

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 <trunc, see rescript> --- RESCRIPT: https://app.rescript.info/public/share/DJlSbJ_Qx080q315tWaqMWn3PixCQsOcM4Kf1IW9_Eo PDF: https://app.rescript.info/api/public/sessions/0efec296b9b6e905/pdf

Jeff Beck

Computational Neuroscientist

Duke University

Machine Learning Street Talk (MLST)/Jan 23, 2026/54m

Abstraction & Idealization: AI's Plato Problem [Mazviita Chirimuuta]

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 Brain Abstracted,* brings a unique perspective shaped by her background in both neuroscience research and philosophy. She challenges us to think critically about the metaphors we use to understand cognition — from the reflex theory of the late 19th century to today's dominant view of the brain as a computer.*Key topics explored:**The problem of oversimplification* — Why scientific models necessarily leave things out, and how this can sometimes lead entire fields astray. The cautionary tale of reflex theory shows how elegant explanations can blind us to biological complexity.*Is the brain really a computer?* — Mazviita unpacks the philosophical assumptions behind computational neuroscience and asks: if we can model anything computationally, what makes brains special? The answer might challenge everything you thought you knew about AI.*Haptic realism* — A fresh way of thinking about scientific knowledge that emphasizes interaction over passive observation. Knowledge isn't about reading the "source code of the universe" — it's something we actively construct through engagement with the world.*Why embodiment matters for understanding* — Can a disembodied language model truly understand? Mazviita makes a compelling case that human cognition is deeply entangled with our sensory-motor engagement and biological existence in ways that can't simply be abstracted away.*Technology and human finitude* — Drawing on Heidegger, we discuss how the dream of transcending our physical limitations through technology might reflect a fundamental misunderstanding of what it means to be a knower.This conversation is essential viewing for anyone interested in AI, consciousness, philosophy of mind, or the future of cognitive science. Whether you're skeptical of strong AI claims or a true believer in machine consciousness, Mazviita's careful philosophical analysis will give you new tools for thinking through these profound questions.---TIMESTAMPS:00:00:00 The Problem of Generalizing Neuroscience00:02:51 Abstraction vs. Idealization: The "Kaleidoscope"00:05:39 Platonism in AI: Discovering or Inventing Patterns?00:09:42 When Simplification Fails: The Reflex Theory00:12:23 Behaviorism and the "Black Box" Trap00:14:20 Haptic Realism: Knowledge Through Interaction00:20:23 Is Nature Protean? The Myth of Converging Truth00:23:23 The Computational Theory of Mind: A Useful Fiction?00:27:25 Biological Constraints: Why Brains Aren't Just Neural Nets00:31:01 Agency, Distal Causes, and Dennett's Stances00:37:13 Searle's Challenge: Causal Powers and Understanding00:41:58 Heidegger's Warning & The Experiment on Children---REFERENCES:Book:[00:01:28] The Brain Abstractedhttps://mitpress.mit.edu/9780262548045/the-brain-abstracted/[00:11:05] The Integrated Action of the Nervous Systemhttps://www.amazon.sg/integrative-action-nervous-system/dp/9354179029[00:18:15] The Quest for Certainty (Dewey)https://www.amazon.com/Quest-Certainty-Relation-Knowledge-Lectures/dp/0399501916[00:19:45] Realism for Realistic People (Chang)https://www.cambridge.org/core/books/realism-for-realistic-people/ACC93A7F03B15AA4D6F3A466E3FC5AB7<truncated, see ReScript>---RESCRIPT:https://app.rescript.info/public/share/A6cZ1TY35p8ORMmYCWNBI0no9ChU3-Kx7dPXGJURvZ0PDF Transcript:https://app.rescript.info/api/public/sessions/0fb7767e066cf712/pdf

Mazviita Chirimuuta

Professor

University of Edinburgh

Machine Learning Street Talk (MLST)/Jan 23, 2026/42m

Why Every Brain Metaphor in History Has Been Wrong [SPECIAL EDITION]

What if everything we think we know about the brain is just a really good metaphor that we forgot was a metaphor?This episode takes you on a journey through the history of scientific simplification, from a young Karl Friston watching wood lice in his garden to the bold claims that your mind is literally software running on biological hardware.We bring together some of the most brilliant minds we've interviewed — Professor Mazviita Chirimuuta, Francois Chollet, Joscha Bach, Professor Luciano Floridi, Professor Noam Chomsky, Nobel laureate John Jumper, and more — to wrestle with a deceptively simple question: *When scientists simplify reality to study it, what gets captured and what gets lost?**Key ideas explored:**The Spherical Cow Problem* — Science requires simplification. We're limited creatures trying to understand systems far more complex than our working memory can hold. But when does a useful model become a dangerous illusion?*The Kaleidoscope Hypothesis* — Francois Chollet's beautiful idea that beneath all the apparent chaos of reality lies simple, repeating patterns — like bits of colored glass in a kaleidoscope creating infinite complexity. Is this profound truth or Platonic wishful thinking?*Is Software Really Spirit?* — Joscha Bach makes the provocative claim that software is literally spirit, not metaphorically. We push back on this, asking whether the "sameness" we see across different computers running the same program exists in nature or only in our descriptions.*The Cultural Illusion of AGI* — Why does artificial general intelligence seem so inevitable to people in Silicon Valley? Professor Chirimuuta suggests we might be caught in a "cultural historical illusion" — our mechanistic assumptions about minds making AI seem like destiny when it might just be a bet.*Prediction vs. Understanding* — Nobel Prize winner John Jumper: AI can predict and control, but understanding requires a human in the loop. Throughout history, we've described the brain as hydraulic pumps, telegraph networks, telephone switchboards, and now computers. Each metaphor felt obviously true at the time. This episode asks: what will we think was naive about our current assumptions in fifty years?Featuring insights from *The Brain Abstracted* by Mazviita Chirimuuta — possibly the most influential book on how we think about thinking in 2025.---TIMESTAMPS:00:00:00 The Wood Louse & The Spherical Cow00:02:04 The Necessity of Abstraction00:04:42 Simplicius vs. Ignorantio: The Boxing Match00:06:39 The Kaleidoscope Hypothesis00:08:40 Is the Mind Software?00:13:15 Critique of Causal Patterns00:14:40 Temperature is Not a Thing00:18:24 The Ship of Theseus & Ontology00:23:45 Metaphors Hardening into Reality00:25:41 The Illusion of AGI Inevitability00:27:45 Prediction vs. Understanding00:32:00 Climbing the Mountain vs. The Helicopter00:34:53 Haptic Realism & The Limits of Knowledge---REFERENCES:Person:[00:00:00] Karl Friston (UCL)https://profiles.ucl.ac.uk/1236-karl-friston[00:06:30] Francois Chollethttps://fchollet.com/[00:14:41] Cesar Hidalgo, MLST interview.https://www.youtube.com/watch?v=vzpFOJRteeI[00:30:30] Terence Tao's Bloghttps://terrytao.wordpress.com/Book:[00:02:25] The Brain Abstractedhttps://mitpress.mit.edu/9780262548045/the-brain-abstracted/[00:06:00] On Learned Ignorancehttps://www.amazon.com/Nicholas-Cusa-learned-ignorance-translation/dp/0938060236[00:24:15] Science and the Modern Worldhttps://amazon.com/dp/0684836394<truncated, see ReScript> RESCRIPT:https://app.rescript.info/public/share/CYy0ex2M2kvcVRdMnSUky5O7H7hB7v2u_nVhoUiuKD4PDF Transcript: https://app.rescript.info/api/public/sessions/6c44c41e1e0fa6dd/pdf Thank you to Dr. Maxwell Ramstead for early script work on this show (Ph.D student of Friston) and the woodlice story came from him!

Machine Learning Street Talk (MLST)/Dec 31, 2025/1h 17m

Bayesian Brain, Scientific Method, and Models [Dr. Jeff Beck]

Dr. Jeff Beck, mathematician turned computational neuroscientist, joins us for a fascinating deep dive into why the future of AI might look less like ChatGPT and more like your own brain. **SPONSOR MESSAGES START** — Prolific - Quality data. From real people. For faster breakthroughs. https://www.prolific.com/?utm_source=mlst — **END** *What if the key to building truly intelligent machines isn't bigger models, but smarter ones?* In this conversation, Jeff makes a compelling case that we've been building AI backwards. While the tech industry races to scale up transformers and language models, Jeff argues we're missing something fundamental: the brain doesn't work like a giant prediction engine. It works like a scientist, constantly testing hypotheses about a world made of *objects* that interact through *forces* — not pixels and tokens. *The Bayesian Brain* — Jeff explains how your brain is essentially running the scientific method on autopilot. When you combine what you see with what you hear, you're doing optimal Bayesian inference without even knowing it. This isn't just philosophy — it's backed by decades of behavioral experiments showing humans are surprisingly efficient at handling uncertainty. *AutoGrad Changed Everything* — Forget transformers for a moment. Jeff argues the real hero of the AI boom was automatic differentiation, which turned AI from a math problem into an engineering problem. But in the process, we lost sight of what actually makes intelligence work. *The Cat in the Warehouse Problem* — Here's where it gets practical. Imagine a warehouse robot that's never seen a cat. Current AI would either crash or make something up. Jeff's approach? Build models that *know what they don't know*, can phone a friend to download new object models on the fly, and keep learning continuously. It's like giving robots the ability to say "wait, what IS that?" instead of confidently being wrong. *Why Language is a Terrible Model for Thought* — In a provocative twist, Jeff argues that grounding AI in language (like we do with LLMs) is fundamentally misguided. Self-report is the least reliable data in psychology — people routinely explain their own behavior incorrectly. We should be grounding AI in physics, not words. *The Future is Lots of Little Models* — Instead of one massive neural network, Jeff envisions AI systems built like video game engines: thousands of small, modular object models that can be combined, swapped, and updated independently. It's more efficient, more flexible, and much closer to how we actually think. Rescript: https://app.rescript.info/public/share/D-b494t8DIV-KRGYONJghvg-aelMmxSDjKthjGdYqsE --- TIMESTAMPS: 00:00:00 Introduction & The Bayesian Brain 00:01:25 Bayesian Inference & Information Processing 00:05:17 The Brain Metaphor: From Levers to Computers 00:10:13 Micro vs. Macro Causation & Instrumentalism 00:16:59 The Active Inference Community & AutoGrad 00:22:54 Object-Centered Models & The Grounding Problem 00:35:50 Scaling Bayesian Inference & Architecture Design 00:48:05 The Cat in the Warehouse: Solving Generalization 00:58:17 Alignment via Belief Exchange 01:05:24 Deception, Emergence & Cellular Automata --- REFERENCES: Paper: [00:00:24] Zoubin Ghahramani (Google DeepMind) https://pmc.ncbi.nlm.nih.gov/articles/PMC3538441/pdf/rsta201 [00:19:20] Mamba: Linear-Time Sequence Modeling https://arxiv.org/abs/2312.00752 [00:27:36] xLSTM: Extended Long Short-Term Memory https://arxiv.org/abs/2405.04517 [00:41:12] 3D Gaussian Splatting https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/ [01:07:09] Lenia: Biology of Artificial Life https://arxiv.org/abs/1812.05433 [01:08:20] Growing Neural Cellular Automata https://distill.pub/2020/growing-ca/ [01:14:05] DreamCoder https://arxiv.org/abs/2006.08381 [01:14:58] The Genomic Bottleneck https://www.nature.com/articles/s41467-019-11786-6 Person: [00:16:42] Karl Friston (UCL) https://www.youtube.com/watch?v=PNYWi996Beg

Jeff Beck

Computational Neuroscientist

Duke University

Machine Learning Street Talk (MLST)/Dec 30, 2025/3h 17m

Your Brain is Running a Simulation Right Now [Max Bennett]

Tim sits down with Max Bennett to explore how our brains evolved over 600 million years—and what that means for understanding both human intelligence and AI. Max isn't a neuroscientist by training. He's a tech entrepreneur who got curious, started reading, and ended up weaving together three fields that rarely talk to each other: comparative psychology (what different animals can actually do), evolutionary neuroscience (how brains changed over time), and AI (what actually works in practice). *Your Brain Is a Guessing Machine* You don't actually "see" the world. Your brain builds a simulation of what it *thinks* is out there and just uses your eyes to check if it's right. That's why optical illusions work—your brain is filling in a triangle that isn't there, or can't decide if it's looking at a duck or a rabbit. *Rats Have Regrets* *Chimps Are Machiavellian* *Language Is the Human Superpower* *Does ChatGPT Think?* (truncated description, more on rescript) Understanding how the brain evolved isn't just about the past. It gives us clues about: - What's actually different between human intelligence and AI - Why we're so easily fooled by status games and tribal thinking - What features we might want to build into—or leave out of—future AI systems Get Max's book: https://www.amazon.com/Brief-History-Intelligence-Humans-Breakthroughs/dp/0063286343 Rescript: https://app.rescript.info/public/share/R234b7AXyDXZusqQ_43KMGsUSvJ2TpSz2I3emnI6j9A --- TIMESTAMPS: 00:00:00 Introduction: Outsider's Advantage & Neocortex Theories 00:11:34 Perception as Inference: The Filling-In Machine 00:19:11 Understanding, Recognition & Generative Models 00:36:39 How Mice Plan: Vicarious Trial & Error 00:46:15 Evolution of Self: The Layer 4 Mystery 00:58:31 Ancient Minds & The Social Brain: Machiavellian Apes 01:19:36 AI Alignment, Instrumental Convergence & Status Games 01:33:07 Metacognition & The IQ Paradox 01:48:40 Does GPT Have Theory of Mind? 02:00:40 Memes, Language Singularity & Brain Size Myths 02:16:44 Communication, Language & The Cyborg Future 02:44:25 Shared Fictions, World Models & The Reality Gap --- REFERENCES:Person: [00:00:05] Karl Friston (UCL) https://www.youtube.com/watch?v=PNYWi996Beg [00:00:06] Jeff Hawkins https://www.youtube.com/watch?v=6VQILbDqaI4 [00:12:19] Hermann von Helmholtz https://plato.stanford.edu/entries/hermann-helmholtz/ [00:38:34] David Redish (U. Minnesota) https://redishlab.umn.edu/ [01:10:19] Robin Dunbar https://www.psy.ox.ac.uk/people/robin-dunbar [01:15:04] Emil Menzel https://www.sciencedirect.com/bookseries/behavior-of-nonhuman-primates/vol/5/suppl/C [01:19:49] Nick Bostrom https://nickbostrom.com/ [02:28:25] Noam Chomsky https://linguistics.mit.edu/user/chomsky/ [03:01:22] Judea Pearl https://samueli.ucla.edu/people/judea-pearl/ Concept/Framework: [00:05:04] Active Inference https://www.youtube.com/watch?v=KkR24ieh5Ow Paper: [00:35:59] Predictions not commands [Rick A Adams] https://pubmed.ncbi.nlm.nih.gov/23129312/ Book: [01:25:42] The Elephant in the Brain https://www.amazon.com/Elephant-Brain-Hidden-Motives-Everyday/dp/0190495995 [01:28:27] The Status Game https://www.goodreads.com/book/show/58642436-the-status-game [02:00:40] The Selfish Gene https://amazon.com/dp/0198788606 [02:14:25] The Language Game https://www.amazon.com/Language-Game-Improvisation-Created-Changed/dp/1541674987 [02:54:40] The Evolution of Language https://www.amazon.com/Evolution-Language-Approaches/dp/052167736X [03:09:37] The Three-Body Problem https://amazon.com/dp/0765377063

Max Bennett

Author

Turing

Machine Learning Street Talk (MLST)/Dec 27, 2025/1h 37m

The 3 Laws of Knowledge [César Hidalgo]

César Hidalgo has spent years trying to answer a deceptively simple question: What is knowledge, and why is it so hard to move around? We all have this intuition that knowledge is just... information. Write it down in a book, upload it to GitHub, train an AI on it—done. But César argues that's completely wrong. Knowledge isn't a thing you can copy and paste. It's more like a living organism that needs the right environment, the right people, and constant exercise to survive. Guest: César Hidalgo, Director of the Center for Collective Learning 1. Knowledge Follows Laws (Like Physics) 2. You Can't Download Expertise 3. Why Big Companies Fail to Adapt 4. The "Infinite Alphabet" of Economies If you think AI can just "copy" human knowledge, or that development is just about throwing money at poor countries, or that writing things down preserves them forever—this conversation will change your mind. Knowledge is fragile, specific, and collective. It decays fast if you don't use it. The Infinite Alphabet [César A. Hidalgo] https://www.penguin.co.uk/books/458054/the-infinite-alphabet-by-hidalgo-cesar-a/9780241655672 https://x.com/cesifoti Rescript link. https://app.rescript.info/public/share/eaBHbEo9xamwbwpxzcVVm4NQjMh7lsOQKeWwNxmw0JQ --- TIMESTAMPS: 00:00:00 The Three Laws of Knowledge 00:02:28 Rival vs. Non-Rival: The Economics of Ideas 00:05:43 Why You Can't Just 'Download' Knowledge 00:08:11 The Detective Novel Analogy 00:11:54 Collective Learning & Organizational Networks 00:16:27 Architectural Innovation: Amazon vs. Barnes & Noble 00:19:15 The First Law: Learning Curves 00:23:05 The Samuel Slater Story: Treason & Memory 00:28:31 Physics of Knowledge: Joule's Cannon 00:32:33 Extensive vs. Intensive Properties 00:35:45 Knowledge Decay: Ise Temple & Polaroid 00:41:20 Absorptive Capacity: Sony & Donetsk 00:47:08 Disruptive Innovation & S-Curves 00:51:23 Team Size & The Cost of Innovation 00:57:13 Geography of Knowledge: Vespa's Origin 01:04:34 Migration, Diversity & 'Planet China' 01:12:02 Institutions vs. Knowledge: The China Story 01:21:27 Economic Complexity & The Infinite Alphabet 01:32:27 Do LLMs Have Knowledge? --- REFERENCES: Book: [00:47:45] The Innovator's Dilemma (Christensen) https://www.amazon.com/Innovators-Dilemma-Revolutionary-Change-Business/dp/0062060244 [00:55:15] Why Greatness Cannot Be Planned https://amazon.com/dp/3319155237 [01:35:00] Why Information Grows https://amazon.com/dp/0465048994 Paper: [00:03:15] Endogenous Technological Change (Romer, 1990) https://web.stanford.edu/~klenow/Romer_1990.pdf [00:03:30] A Model of Growth Through Creative Destruction (Aghion & Howitt, 1992) https://dash.harvard.edu/server/api/core/bitstreams/7312037d-2b2d-6bd4-e053-0100007fdf3b/content [00:14:55] Organizational Learning: From Experience to Knowledge (Argote & Miron-Spektor, 2011) https://www.researchgate.net/publication/228754233_Organizational_Learning_From_Experience_to_Knowledge [00:17:05] Architectural Innovation (Henderson & Clark, 1990) https://www.researchgate.net/publication/200465578_Architectural_Innovation_The_Reconfiguration_of_Existing_Product_Technologies_and_the_Failure_of_Established_Firms [00:19:45] The Learning Curve Equation (Thurstone, 1916) https://dn790007.ca.archive.org/0/items/learningcurveequ00thurrich/learningcurveequ00thurrich.pdf [00:21:30] Factors Affecting the Cost of Airplanes (Wright, 1936) https://pdodds.w3.uvm.edu/research/papers/others/1936/wright1936a.pdf [00:52:45] Are Ideas Getting Harder to Find? (Bloom et al.) https://web.stanford.edu/~chadj/IdeaPF.pdf [01:33:00] LLMs/ Emergence https://arxiv.org/abs/2506.11135 Person: [00:25:30] Samuel Slater https://en.wikipedia.org/wiki/Samuel_Slater [00:42:05] Masaru Ibuka (Sony) https://www.sony.com/en/SonyInfo/CorporateInfo/History/SonyHistory/1-02.html

César Hidalgo

Director

Center for Collective Learning

MIT Media Lab

Machine Learning Street Talk (MLST)/Dec 24, 2025/2h 56m

"I Desperately Want To Live In The Matrix" - Dr. Mike Israetel

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 coming? 2. Does AI actually understand anything? 3. The Simulation Debate (The Spiciest Part) 4. Will AI kill us all? (The Doomer Debate) 5. What happens to human jobs and purpose? 6. Do we need suffering? Mikes channel: https://www.youtube.com/channel/UCfQgsKhHjSyRLOp9mnffqVg RESCRIPT INTERACTIVE PLAYER: https://app.rescript.info/public/share/GVMUXHCqctPkXH8WcYtufFG7FQcdJew_RL_MLgMKU1U --- TIMESTAMPS: 00:00:00 Introduction & Workout Demo 00:04:15 ASI Timelines & Definitions 00:10:24 The Embodiment Debate 00:18:28 Neutrinos & Abstract Knowledge 00:25:56 Can AI Learn From YouTube? 00:31:25 Diversity of Intelligence 00:36:00 AI Slop & Understanding 00:45:18 The Simulation Argument: Fire & Water 00:58:36 Consciousness & Zombies 01:04:30 Do Reasoning Models Actually Reason? 01:12:00 The Live Learning Problem 01:19:15 Superintelligence & Benevolence 01:28:59 What is True Agency? 01:37:20 Game Theory & The "Kill All Humans" Fallacy 01:48:05 Regulation & The China Factor 01:55:52 Mind Uploading & The Future of Love 02:04:41 Economics of ASI: Will We Be Useless? 02:13:35 The Matrix & The Value of Suffering 02:17:30 Transhumanism & Inequality 02:21:28 Debrief: AI Medical Advice & Final Thoughts --- REFERENCES: Paper: [00:10:45] Alchemy and Artificial Intelligence (Dreyfus) https://www.rand.org/content/dam/rand/pubs/papers/2006/P3244.pdf [00:10:55] The Chinese Room Argument (John Searle) https://home.csulb.edu/~cwallis/382/readings/482/searle.minds.brains.programs.bbs.1980.pdf [00:11:05] The Symbol Grounding Problem (Stephen Harnad) https://arxiv.org/html/cs/9906002 [00:23:00] Attention Is All You Need https://arxiv.org/abs/1706.03762 [00:45:00] GPT-4 Technical Report https://arxiv.org/abs/2303.08774 [01:45:00] Anthropic Agentic Misalignment Paper https://www.anthropic.com/research/agentic-misalignment [02:17:45] Retatrutide https://pubmed.ncbi.nlm.nih.gov/37366315/ Organization: [00:15:50] CERN https://home.cern/ [01:05:00] METR Long Horizon Evaluations https://evaluations.metr.org/ MLST Episode: [00:23:10] MLST: Llion Jones - Inventors' Remorse https://www.youtube.com/watch?v=DtePicx_kFY [00:50:30] MLST: Blaise Agüera y Arcas Interview https://www.youtube.com/watch?v=rMSEqJ_4EBk [01:10:00] MLST: David Krakauer https://www.youtube.com/watch?v=dY46YsGWMIc Event: [00:23:40] ARC Prize/Challenge https://arcprize.org/ Book: [00:24:45] The Brain Abstracted https://www.amazon.com/Brain-Abstracted-Simplification-Philosophy-Neuroscience/dp/0262548046 [00:47:55] Pamela McCorduck https://www.amazon.com/Machines-Who-Think-Artificial-Intelligence/dp/1568812051 [01:23:15] The Singularity Is Nearer (Ray Kurzweil) https://www.amazon.com/Singularity-Nearer-Ray-Kurzweil-ebook/dp/B08Y6FYJVY [01:27:35] A Fire Upon The Deep (Vernor Vinge) https://www.amazon.com/Fire-Upon-Deep-S-F-MASTERWORKS-ebook/dp/B00AVUMIZE/ [02:04:50] Deep Utopia (Nick Bostrom) https://www.amazon.com/Deep-Utopia-Meaning-Solved-World/dp/1646871642 [02:05:00] Technofeudalism (Yanis Varoufakis) https://www.amazon.com/Technofeudalism-Killed-Capitalism-Yanis-Varoufakis/dp/1685891241 Visual Context Needed: [00:29:40] AT-AT Walker (Star Wars) https://starwars.fandom.com/wiki/All_Terrain_Armored_Transport Person: [00:33:15] Andrej Karpathy https://karpathy.ai/ Video: [01:40:00] Mike Israetel vs Liron Shapira AI Doom Debate https://www.youtube.com/watch?v=RaDWSPMdM4o Company: [02:26:30] Examine.com https://examine.com/

Machine Learning Street Talk (MLST)/Dec 22, 2025/44m

Making deep learning perform real algorithms with Category Theory (Andrew Dudzik, Petar Velichkovich, Taco Cohen, Bruno Gavranović, Paul Lessard)

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 exploration, *Andrew Dudzik*, *Petar Velichkovich*, *Taco Cohen*, *Bruno Gavranović*, and *Paul Lessard* join host *Tim Scarfe* to discuss the fundamental limitations of today’s AI and the radical mathematical framework that might fix them. TRANSCRIPT: https://app.rescript.info/public/share/LMreunA-BUpgP-2AkuEvxA7BAFuA-VJNAp2Ut4MkMWk --- Key Insights in This Episode: * *The "Addition" Problem:* *Andrew Dudzik* explains why LLMs don't actually "know" math—they just recognize patterns. When you change a single digit in a long string of numbers, the pattern breaks because the model lacks the internal "machinery" to perform a simple carry operation. * *Beyond Alchemy:* deep learning is currently in its "alchemy" phase—we have powerful results, but we lack a unifying theory. Category Theory is proposed as the framework to move AI from trial-and-error to principled engineering. [00:13:49] * *Algebra with Colors:* To make Category Theory accessible, the guests use brilliant analogies—like thinking of matrices as *magnets with colors* that only snap together when the types match. This "partial compositionality" is the secret to building more complex internal reasoning. [00:09:17] * *Synthetic vs. Analytic Math:* *Paul Lessard* breaks down the philosophical shift needed in AI research: moving from "Analytic" math (what things are made of) to "Synthetic" math [00:23:41] --- Why This Matters for AGI If we want AI to solve the world's hardest scientific problems, it can't just be a "stochastic parrot." It needs to internalize the rules of logic and computation. By imbuing neural networks with categorical priors, researchers are attempting to build a future where AI doesn't just predict the next word—it understands the underlying structure of the universe. --- TIMESTAMPS: 00:00:00 The Failure of LLM Addition & Physics 00:01:26 Tool Use vs Intrinsic Model Quality 00:03:07 Efficiency Gains via Internalization 00:04:28 Geometric Deep Learning & Equivariance 00:07:05 Limitations of Group Theory 00:09:17 Category Theory: Algebra with Colors 00:11:25 The Systematic Guide of Lego-like Math 00:13:49 The Alchemy Analogy & Unifying Theory 00:15:33 Information Destruction & Reasoning 00:18:00 Pathfinding & Monoids in Computation 00:20:15 System 2 Reasoning & Error Awareness 00:23:31 Analytic vs Synthetic Mathematics 00:25:52 Morphisms & Weight Tying Basics 00:26:48 2-Categories & Weight Sharing Theory 00:28:55 Higher Categories & Emergence 00:31:41 Compositionality & Recursive Folds 00:34:05 Syntax vs Semantics in Network Design 00:36:14 Homomorphisms & Multi-Sorted Syntax 00:39:30 The Carrying Problem & Hopf Fibrations Petar Veličković (GDM) https://petar-v.com/ Paul Lessard https://www.linkedin.com/in/paul-roy-lessard/ Bruno Gavranović https://www.brunogavranovic.com/ Andrew Dudzik (GDM) https://www.linkedin.com/in/andrew-dudzik-222789142/ --- REFERENCES: Model: [00:01:05] Veo https://deepmind.google/models/veo/ [00:01:10] Genie https://deepmind.google/blog/genie-3-a-new-frontier-for-world-models/ Paper: [00:04:30] Geometric Deep Learning Blueprint https://arxiv.org/abs/2104.13478 https://www.youtube.com/watch?v=bIZB1hIJ4u8 [00:16:45] AlphaGeometry https://arxiv.org/abs/2401.08312 [00:16:55] AlphaCode https://arxiv.org/abs/2203.07814 [00:17:05] FunSearch https://www.nature.com/articles/s41586-023-06924-6 [00:37:00] Attention Is All You Need https://arxiv.org/abs/1706.03762 [00:43:00] Categorical Deep Learning https://arxiv.org/abs/2402.15332

Researcher

Researcher

Researcher

Qualcomm Technologies

Bruno Gavranović

Researcher

University of Strathclyde

Paul Lessard

Researcher

Mila

Machine Learning Street Talk (MLST)/Dec 20, 2025/16m

Are AI Benchmarks Telling The Full Story? [SPONSORED] (Andrew Gordon and Nora Petrova - Prolific)

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) from **Prolific**. They reveal the hidden flaws in how we currently rank AI and introduce a more rigorous, "humane" way to measure whether these models are actually helpful, safe, and relatable for real people. --- Key Insights in This Episode: * *The F1 Car Analogy:* Andrew explains why a model that excels at the "Humanities Last Exam" might be a nightmare for daily use. Technical benchmarks often ignore the nuances of human communication and adaptability. * *The "Wild West" of AI Safety:* As users turn to AI for sensitive topics like mental health, Nora highlights the alarming lack of oversight and the "thin veneer" of safety training—citing recent controversial incidents like Grok-3’s "Mecha Hitler." * *Fixing the "Leaderboard Illusion":* The team critiques current popular rankings like Chatbot Arena, discussing how anonymous, unstratified voting can lead to biased results and how companies can "game" the system. * *The Xbox Secret to AI Ranking:* Discover how Prolific uses *TrueSkill*—the same algorithm Microsoft developed for Xbox Live matchmaking—to create a fairer, more statistically sound leaderboard for LLMs. * *The Personality Gap:* Early data from the **Humane Leaderboard** suggests that while AI is getting smarter, it is actually performing *worse* on metrics like personality, culture, and "sycophancy" (the tendency for models to become annoying "people-pleasers"). --- About the HUMAINE Leaderboard Moving beyond simple "A vs. B" testing, the researchers discuss their new framework that samples participants based on *census data* (Age, Ethnicity, Political Alignment). By using a representative sample of the general public rather than just tech enthusiasts, they are building a standard that reflects the values of the real world. *Are we building models for benchmarks, or are we building them for humans? It’s time to change the scoreboard.* Rescript link: https://app.rescript.info/public/share/IDqwjY9Q43S22qSgL5EkWGFymJwZ3SVxvrfpgHZLXQc --- TIMESTAMPS: 00:00:00 Introduction & The Benchmarking Problem 00:01:58 The Fractured State of AI Evaluation 00:03:54 AI Safety & Interpretability 00:05:45 Bias in Chatbot Arena 00:06:45 Prolific's Three Pillars Approach 00:09:01 TrueSkill Ranking & Efficient Sampling 00:12:04 Census-Based Representative Sampling 00:13:00 Key Findings: Culture, Personality & Sycophancy --- REFERENCES: Paper: [00:00:15] MMLU https://arxiv.org/abs/2009.03300 [00:05:10] Constitutional AI https://arxiv.org/abs/2212.08073 [00:06:45] The Leaderboard Illusion https://arxiv.org/abs/2504.20879 [00:09:41] HUMAINE Framework Paper https://huggingface.co/blog/ProlificAI/humaine-framework Company: [00:00:30] Prolific https://www.prolific.com [00:01:45] Chatbot Arena https://lmarena.ai/ Person: [00:00:35] Andrew Gordon https://www.linkedin.com/in/andrew-gordon-03879919a/ [00:00:45] Nora Petrova https://www.linkedin.com/in/nora-petrova/ Event: Algorithm: [00:09:01] Microsoft TrueSkill https://www.microsoft.com/en-us/research/project/trueskill-ranking-system/ Leaderboard: [00:09:21] Prolific HUMAINE Leaderboard https://www.prolific.com/humaine [00:09:31] HUMAINE HuggingFace Space https://huggingface.co/spaces/ProlificAI/humaine-leaderboard [00:10:21] Prolific AI Leaderboard Portal https://www.prolific.com/leaderboard Dataset: [00:09:51] Prolific Social Reasoning RLHF Dataset https://huggingface.co/datasets/ProlificAI/social-reasoning-rlhf Organization: [00:10:31] MLCommons https://mlcommons.org/

Andrew Gordon

Staff Researcher in Behavioral Science

Prolific

Nora Petrova

AI Researcher

Prolific

Machine Learning Street Talk (MLST)/Dec 13, 2025/1h 39m

The Mathematical Foundations of Intelligence [Professor Yi Ma]

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 why 3D reconstruction isn't the same as understanding, and presents a unified mathematical theory of intelligence built on just two principles: **parsimony** and **self-consistency**. **SPONSOR MESSAGES START** — Prolific - Quality data. From real people. For faster breakthroughs. https://www.prolific.com/?utm_source=mlst — cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economy Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlst Submit investment deck: https://cyber.fund/contact?utm_source=mlst — **END** Key Insights: **LLMs Don't Understand—They Memorize** Language models process text (*already* compressed human knowledge) using the same mechanism we use to learn from raw data. **The Illusion of 3D Vision** Sora and NeRFs etc that can reconstruct 3D scenes still fail miserably at basic spatial reasoning **"All Roads Lead to Rome"** Why adding noise is *necessary* for discovering structure. **Why Gradient Descent Actually Works** Natural optimization landscapes are surprisingly smooth—a "blessing of dimensionality" **Transformers from First Principles** Transformer architectures can be mathematically derived from compression principles — INTERACTIVE AI TRANSCRIPT PLAYER w/REFS (ReScript): https://app.rescript.info/public/share/Z-dMPiUhXaeMEcdeU6Bz84GOVsvdcfxU_8Ptu6CTKMQ About Professor Yi Ma Yi Ma is the inaugural director of the School of Computing and Data Science at Hong Kong University and a visiting professor at UC Berkeley. https://people.eecs.berkeley.edu/~yima/ https://scholar.google.com/citations?user=XqLiBQMAAAAJ&hl=en https://x.com/YiMaTweets **Slides from this conversation:** https://www.dropbox.com/scl/fi/sbhbyievw7idup8j06mlr/slides.pdf?rlkey=7ptovemezo8bj8tkhfi393fh9&dl=0 **Related Talks by Professor Ma:** - Pursuing the Nature of Intelligence (ICLR): https://www.youtube.com/watch?v=LT-F0xSNSjo - Earlier talk at Berkeley: https://www.youtube.com/watch?v=TihaCUjyRLM TIMESTAMPS: 00:00:00 Introduction 00:02:08 The First Principles Book & Research Vision 00:05:21 Two Pillars: Parsimony & Consistency 00:09:50 Evolution vs. Learning: The Compression Mechanism 00:14:36 LLMs: Memorization Masquerading as Understanding 00:19:55 The Leap to Abstraction: Empirical vs. Scientific 00:27:30 Platonism, Deduction & The ARC Challenge 00:35:57 Specialization & The Cybernetic Legacy 00:41:23 Deriving Maximum Rate Reduction 00:48:21 The Illusion of 3D Understanding: Sora & NeRF 00:54:26 All Roads Lead to Rome: The Role of Noise 00:59:56 All Roads Lead to Rome: The Role of Noise 01:00:14 Benign Non-Convexity: Why Optimization Works 01:06:35 Double Descent & The Myth of Overfitting 01:14:26 Self-Consistency: Closed-Loop Learning 01:21:03 Deriving Transformers from First Principles 01:30:11 Verification & The Kevin Murphy Question 01:34:11 CRATE vs. ViT: White-Box AI & Conclusion REFERENCES: Book: [00:03:04] Learning Deep Representations of Data Distributions https://ma-lab-berkeley.github.io/deep-representation-learning-book/ [00:18:38] A Brief History of Intelligence https://www.amazon.co.uk/BRIEF-HISTORY-INTELLIGEN-HB-Evolution/dp/0008560099 [00:38:14] Cybernetics https://mitpress.mit.edu/9780262730099/cybernetics/ Book (Yi Ma): [00:03:14] 3-D Vision book https://link.springer.com/book/10.1007/978-0-387-21779-6 <TRUNC> refs on ReScript link/YT

Yi Ma

Professor

UC Berkeley

Machine Learning Street Talk (MLST)/Dec 8, 2025/1h 28m

Pedro Domingos: Tensor Logic Unifies AI Paradigms

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 — Prolific - Quality data. From real people. For faster breakthroughs. https://www.prolific.com/?utm_source=mlst — cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economy Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlst Submit investment deck: https://cyber.fund/contact?utm_source=mlst — **END** Current AI is split between two worlds that don't play well together: Deep Learning (neural networks, transformers, ChatGPT) - great at learning from data, terrible at logical reasoning Symbolic AI (logic programming, expert systems) - great at logical reasoning, terrible at learning from messy real-world data Tensor Logic unifies both. It's a single language where you can: Write logical rules that the system can actually learn and modify Do transparent, verifiable reasoning (no hallucinations) Mix "fuzzy" analogical thinking with rock-solid deduction INTERACTIVE TRANSCRIPT: https://app.rescript.info/public/share/NP4vZQ-GTETeN_roB2vg64vbEcN7isjJtz4C86WSOhw TOC: 00:00:00 - Introduction 00:04:41 - What is Tensor Logic? 00:09:59 - Tensor Logic vs PyTorch & Einsum 00:17:50 - The Master Algorithm Connection 00:20:41 - Predicate Invention & Learning New Concepts 00:31:22 - Symmetries in AI & Physics 00:35:30 - Computational Reducibility & The Universe 00:43:34 - Technical Details: RNN Implementation 00:45:35 - Turing Completeness Debate 00:56:45 - Transformers vs Turing Machines 01:02:32 - Reasoning in Embedding Space 01:11:46 - Solving Hallucination with Deductive Modes 01:16:17 - Adoption Strategy & Migration Path 01:21:50 - AI Education & Abstraction 01:24:50 - The Trillion-Dollar Waste REFS Tensor Logic: The Language of AI [Pedro Domingos] https://arxiv.org/abs/2510.12269 The Master Algorithm [Pedro Domingos] https://www.amazon.co.uk/Master-Algorithm-Ultimate-Learning-Machine/dp/0241004543 Einsum is All you Need (TIM ROCKTÄSCHEL) https://rockt.ai/2018/04/30/einsum https://www.youtube.com/watch?v=6DrCq8Ry2cw Autoregressive Large Language Models are Computationally Universal (Dale Schuurmans et al - GDM) https://arxiv.org/abs/2410.03170 Memory Augmented Large Language Models are Computationally Universal [Dale Schuurmans] https://arxiv.org/pdf/2301.04589 On the computational power of NNs [95/Siegelmann] https://binds.cs.umass.edu/papers/1995_Siegelmann_JComSysSci.pdf Sebastian Bubeck https://www.reddit.com/r/OpenAI/comments/1oacp38/openai_researcher_sebastian_bubeck_falsely_claims/ I am a strange loop - Hofstadter https://www.amazon.co.uk/Am-Strange-Loop-Douglas-Hofstadter/dp/0465030793 Stephen Wolfram https://www.youtube.com/watch?v=dkpDjd2nHgo The Complex World: An Introduction to the Foundations of Complexity Science [David C. Krakauer] https://www.amazon.co.uk/Complex-World-Introduction-Foundations-Complexity/dp/1947864629 Geometric Deep Learning https://www.youtube.com/watch?v=bIZB1hIJ4u8 Andrew Wilson (NYU) https://www.youtube.com/watch?v=M-jTeBCEGHc Yi Ma https://www.patreon.com/posts/yi-ma-scientific-141953348 Roger Penrose - road to reality https://www.amazon.co.uk/Road-Reality-Complete-Guide-Universe/dp/0099440687 Artificial Intelligence: A Modern Approach [Russel and Norvig] https://www.amazon.co.uk/Artificial-Intelligence-Modern-Approach-Global/dp/1292153962

Pedro Domingos

Professor

University of Washington

Machine Learning Street Talk (MLST)/Nov 23, 2025/1h 13m

He Co-Invented the Transformer. Now: Continuous Thought Machines - Llion Jones and Luke Darlow [Sakana AI]

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.studio/build — Tufa AI Labs is hiring ML Research Engineers https://tufalabs.ai/ — cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economy Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlst Submit investment deck: https://cyber.fund/contact?utm_source=mlst — **END** The "Spiral" Problem – Llion uses a striking visual analogy to explain what current AI is missing. If you ask a standard neural network to understand a spiral shape, it solves it by drawing tiny straight lines that just happen to look like a spiral. It "fakes" the shape without understanding the concept of spiraling. Introducing the Continuous Thought Machine (CTM) Luke Darlow deep dives into their solution: a biology-inspired model that fundamentally changes how AI processes information. The Maze Analogy: Luke explains that standard AI tries to solve a maze by staring at the whole image and guessing the entire path instantly. Their new machine "walks" through the maze step-by-step. Thinking Time: This allows the AI to "ponder." If a problem is hard, the model can naturally spend more time thinking about it before answering, effectively allowing it to correct its own mistakes and backtrack—something current Language Models struggle to do genuinely. https://sakana.ai/ https://x.com/YesThisIsLion https://x.com/LearningLukeD TRANSCRIPT: https://app.rescript.info/public/share/crjzQ-Jo2FQsJc97xsBdfzfOIeMONpg0TFBuCgV2Fu8 TOC: 00:00:00 - Stepping Back from Transformers 00:00:43 - Introduction to Continuous Thought Machines (CTM) 00:01:09 - The Changing Atmosphere of AI Research 00:04:13 - Sakana’s Philosophy: Research Freedom 00:07:45 - The Local Minimum of Large Language Models 00:18:30 - Representation Problems: The Spiral Example 00:29:12 - Technical Deep Dive: CTM Architecture 00:36:00 - Adaptive Computation & Maze Solving 00:47:15 - Model Calibration & Uncertainty 01:00:43 - Sudoku Bench: Measuring True Reasoning REFS: Why Greatness Cannot be planned [Kenneth Stanley] https://www.amazon.co.uk/Why-Greatness-Cannot-Planned-Objective/dp/3319155237 https://www.youtube.com/watch?v=lhYGXYeMq_E The Hardware Lottery [Sara Hooker] https://arxiv.org/abs/2009.06489 https://www.youtube.com/watch?v=sQFxbQ7ade0 Continuous Thought Machines [Luke Darlow et al / Sakana] https://arxiv.org/abs/2505.05522 https://sakana.ai/ctm/ LSTM: The Comeback Story? [Prof. Sepp Hochreiter] https://www.youtube.com/watch?v=8u2pW2zZLCs Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis [Kumar/Stanley] https://arxiv.org/pdf/2505.11581 A Spline Theory of Deep Networks [Randall Balestriero] https://proceedings.mlr.press/v80/balestriero18b/balestriero18b.pdf https://www.youtube.com/watch?v=86ib0sfdFtw https://www.youtube.com/watch?v=l3O2J3LMxqI On the Biology of a Large Language Model [Anthropic, Jack Lindsey et al] https://transformer-circuits.pub/2025/attribution-graphs/biology.html The ARC Prize 2024 Winning Algorithm [Daniel Franzen and Jan Disselhoff] “The ARChitects” https://www.youtube.com/watch?v=mTX_sAq--zY Neural Turing Machine [Graves] https://arxiv.org/pdf/1410.5401 Adaptive Computation Time for Recurrent Neural Networks [Graves] https://arxiv.org/abs/1603.08983 Sudoko Bench [Sakana] https://pub.sakana.ai/sudoku/

Llion Jones

Co-Founder and Researcher

Sakana AI

Luke Darlow

Researcher

Sakana AI

Machine Learning Street Talk (MLST)/Nov 3, 2025/24m

Why Humans Are Still Powering AI [Sponsored]

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 right AI task - whether that's doctors evaluating medical chatbots or coders reviewing AI-generated software.Prolific: https://prolific.com/?utm_source=mlsthttps://uk.linkedin.com/in/phelim-bradley-84300826The discussion dives into:**The human data pipeline**: How AI companies rely on human intelligence to train, refine, and validate their models - something rarely discussed openly**Quality over quantity**: Why paying humans well and treating them as partners (not commodities) produces better AI training data**The matching challenge**: How Prolific solves the complex problem of finding the right expert for each specific task, similar to matching Uber drivers to riders but with deep expertise requirements**Future of work**: What it means when human expertise becomes an on-demand service, and why this might actually create more opportunities rather than fewer**Geopolitical implications**: Why the centralization of AI development in US tech companies should concern Europe and the UK

Phelim Bradley

CEO and Co-Founder

Prolific

Machine Learning Street Talk (MLST)/Oct 25, 2025/41m

The Universal Hierarchy of Life - Prof. Chris Kempes [SFI]

"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 fascinating three-level framework: - Materials: The physical stuff life is made of. He argues this could be incredibly diverse across the universe, and we shouldn't expect alien life to share our biochemistry. - Constraints: The universal laws of physics (like gravity or diffusion) that all life must obey, regardless of what it's made of. This is where different life forms start to look more similar. - Principles: At the highest level are abstract principles like evolution and learning. Chris suggests these computational or "optimization" rules are what truly define a living system. A key idea is "convergence" – using the example of the eye. It's such a complex organ that you'd think it evolved only once. However, eyes evolved many separate times across different species. This is because the physics of light provides a clear "target", and evolution found similar solutions to the problem of seeing, even with different starting materials. **SPONSOR MESSAGES** — Prolific - Quality data. From real people. For faster breakthroughs. https://www.prolific.com/?utm_source=mlst — Check out NotebookLM from Google here - https://notebooklm.google.com/ - it’s really good for doing research directly from authoritative source material, minimising hallucinations. — cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economy Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlst Submit investment deck: https://cyber.fund/contact?utm_source=mlst — Prof. Chris Kempes: https://www.santafe.edu/people/profile/chris-kempes TRANSCRIPT: https://app.rescript.info/public/share/Y2cI1i0nX_-iuZitvlguHvaVLQTwPX1Y_E1EHxV0i9I TOC: 00:00:00 - Introduction to Chris Kempes and the Santa Fe Institute 00:02:28 - The Three Cultures of Science 00:05:08 - What Makes a Good Scientific Theory? 00:06:50 - The Universal Theory of Life 00:09:40 - The Role of Material in Life 00:12:50 - A Hierarchy for Understanding Life 00:13:55 - How Life Diversifies and Converges 00:17:53 - Adaptive Processes and Defining Life 00:19:28 - Functionalism, Memes, and Phylogenies 00:22:58 - Convergence at Multiple Levels 00:25:45 - The Possibility of Simulating Life 00:28:16 - Intelligence, Parasitism, and Spectrums of Life 00:32:39 - Phase Changes in Evolution 00:36:16 - The Separation of Matter and Logic 00:37:21 - Assembly Theory and Quantifying Complexity REFS: Developing a predictive science of the biosphere requires the integration of scientific cultures [Kempes et al] https://www.pnas.org/doi/10.1073/pnas.2209196121 Seeing with an extra sense (“Dangerous prediction”) [Rob Phillips] https://www.sciencedirect.com/science/article/pii/S0960982224009035 The Multiple Paths to Multiple Life [Christopher P. Kempes & David C. Krakauer] https://link.springer.com/article/10.1007/s00239-021-10016-2 The Information Theory of Individuality [David Krakauer et al] https://arxiv.org/abs/1412.2447 Minds, Brains and Programs [Searle] https://home.csulb.edu/~cwallis/382/readings/482/searle.minds.brains.programs.bbs.1980.pdf The error threshold https://www.sciencedirect.com/science/article/abs/pii/S0168170204003843 Assembly theory and its relationship with computational complexity [Kempes et al] https://arxiv.org/abs/2406.12176

Chris Kempes

Professor

Santa Fe Institute

Machine Learning Street Talk (MLST)/Oct 21, 2025/60m

Google Researcher Shows Life "Emerges From Code" - Blaise Agüera y Arcas

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. **SPONSOR MESSAGES** — Prolific - Quality data. From real people. For faster breakthroughs. https://www.prolific.com/?utm_source=mlst — cyber•Fund https://cyber.fund/?utm_source=mlst is a founder-led investment firm accelerating the cybernetic economy Oct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++ Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlst Submit investment deck: https://cyber.fund/contact?utm_source=mlst — Blaise argues that there is more to evolution than random mutations (like most people think). The secret to increasing complexity is *merging* i.e. when different organisms or systems come together and combine their histories and capabilities. Blaise describes his "BFF" experiment where random computer code spontaneously evolved into self-replicating programs, showing how purpose and complexity can emerge from pure randomness through computational processes. https://en.wikipedia.org/wiki/Blaise_Ag%C3%BCera_y_Arcas https://x.com/blaiseaguera?lang=en TRANSCRIPT: https://app.rescript.info/public/share/VX7Gktfr3_wIn4Bj7cl9StPBO1MN4R5lcJ11NE99hLg TOC: 00:00:00 Introduction - New book "What is Intelligence?" 00:01:45 Life as computation - Von Neumann's insights 00:12:00 BFF experiment - How purpose emerges 00:26:00 Symbiogenesis and evolutionary complexity 00:40:00 Functionalism and consciousness 00:49:45 AI as part of collective human intelligence 00:57:00 Comparing AI and human cognition REFS: What is intelligence [Blaise Agüera y Arcas] https://whatisintelligence.antikythera.org/ [Read free online, interactive rich media] https://mitpress.mit.edu/9780262049955/what-is-intelligence/ [MIT Press] Large Language Models and Emergence: A Complex Systems Perspective https://arxiv.org/abs/2506.11135 Our first Noam Chomsky MLST interview https://www.youtube.com/watch?v=axuGfh4UR9Q Chance and Necessity [Jacques Monod] https://monoskop.org/images/9/99/Monod_Jacques_Chance_and_Necessity.pdf Wonderful Life: The Burgess Shale and the History of Nature [Stephen Jay Gould] https://www.amazon.co.uk/Wonderful-Life-Burgess-Nature-History/dp/0099273454 The major evolutionary transitions [E Szathmáry, J M Smith] https://wiki.santafe.edu/images/0/0e/Szathmary.MaynardSmith_1995_Nature.pdf Don't Sleep, There Are Snakes: Life and Language in the Amazonian Jungle [Dan Everett] https://www.amazon.com/Dont-Sleep-There-Are-Snakes/dp/0307386120 The Nature of Technology: What It Is and How It Evolves [W. Brian Arthur] https://www.amazon.com/Nature-Technology-What-How-Evolves-ebook/dp/B002RI9W16/ The MANIAC [Benjamin Labatut] https://www.amazon.com/MANIAC-Benjam%C3%ADn-Labatut/dp/1782279814 When We Cease to Understand the World [Benjamin Labatut] https://www.amazon.com/When-We-Cease-Understand-World/dp/1681375664/ The Boys in the Boat [Dan Brown] https://www.amazon.com/Boys-Boat-Americans-Berlin-Olympics/dp/0143125478 [Petter Johansson] (Split brain) https://www.lucs.lu.se/fileadmin/user_upload/lucs/2011/01/Johansson-et-al.-2006-How-Something-Can-Be-Said-About-Telling-More-Than-We-Can-Know.pdf If Anyone Builds It, Everyone Dies [Eliezer Yudkowsky, Nate Soares] https://www.amazon.com/Anyone-Builds-Everyone-Dies-Superhuman/dp/0316595640 The science of cycology https://link.springer.com/content/pdf/10.3758/bf03195929.pdf <trunc, see YT desc for more>

Blaise Agüera y Arcas

VP and Fellow

Google

Machine Learning Street Talk (MLST)/Oct 18, 2025/1h 20m

The Secret Engine of AI - Prolific [Sponsored] (Sara Saab, Enzo Blindow)

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 accelerating the cybernetic economy Oct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++ Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlst Submit investment deck: https://cyber.fund/contact?utm_source=mlst — While technologists want to remove humans from the loop for speed and efficiency, these non-deterministic AI systems actually require more human oversight than ever before. Prolific's approach is to put "well-treated, verified, diversely demographic humans behind an API" - making human feedback as accessible as any other infrastructure service. When AI models like Grok 4 achieve top scores on technical benchmarks but feel awkward or problematic to use in practice, it exposes the limitations of our current evaluation methods. The guests argue that optimizing for benchmarks may actually weaken model performance in other crucial areas, like cultural sensitivity or natural conversation. We also discuss Anthropic's research showing that frontier AI models, when given goals and access to information, independently arrived at solutions involving blackmail - without any prompting toward unethical behavior. Even more concerning, the more sophisticated the model, the more susceptible it was to this "agentic misalignment." Enzo and Sarah present Prolific's "Humane" leaderboard as an alternative to existing benchmarking systems. By stratifying evaluations across diverse demographic groups, they reveal that different populations have vastly different experiences with the same AI models. Looking ahead, the guests imagine a world where humans take on coaching and teaching roles for AI systems - similar to how we might correct a child or review code. This also raises important questions about working conditions and the evolution of labor in an AI-augmented world. Rather than replacing humans entirely, we may be moving toward more sophisticated forms of human-AI collaboration. As AI tech becomes more powerful and general-purpose, the quality of human evaluation becomes more critical, not less. We need more representative evaluation frameworks that capture the messy reality of human values and cultural diversity. Visit Prolific: https://www.prolific.com/ Sara Saab (VP Product): https://uk.linkedin.com/in/sarasaab Enzo Blindow (VP Data & AI): https://uk.linkedin.com/in/enzoblindow TRANSCRIPT: https://app.rescript.info/public/share/xZ31-0kJJ_xp4zFSC-bunC8-hJNkHpbm7Lg88RFcuLE TOC: [00:00:00] Intro & Background [00:03:16] Human-in-the-Loop Challenges [00:17:19] Can AIs Understand? [00:32:02] Benchmarking & Vibes [00:51:00] Agentic Misalignment Study [01:03:00] Data Quality vs Quantity [01:16:00] Future of AI Oversight REFS: Anthropic Agentic Misalignment https://www.anthropic.com/research/agentic-misalignment Value Compass https://arxiv.org/pdf/2409.09586 Reasoning Models Don’t Always Say What They Think (Anthropic) https://www.anthropic.com/research/reasoning-models-dont-say-think https://assets.anthropic.com/m/71876fabef0f0ed4/original/reasoning_models_paper.pdf Apollo research - science of evals blog post https://www.apolloresearch.ai/blog/we-need-a-science-of-evals Leaderboard Illusion https://www.youtube.com/watch?v=9W_OhS38rIE MLST video The Leaderboard Illusion [2025] Shivalika Singh et al https://arxiv.org/abs/2504.20879 (Truncated, full list on YT)

VP of Product

VP of Data and AI

Machine Learning Street Talk (MLST)/Oct 4, 2025/1h 1m

AI Agents Can Code 10,000 Lines of Hacking Tools In Seconds - Dr. Ilia Shumailov (ex-GDM)

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 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 the cybernetic economy Oct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++ Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlst Submit investment deck: https://cyber.fund/contact?utm_source=mlst — We're racing toward a world where AI agents will handle our emails, manage our finances, and interact with sensitive data 24/7. But there is a problem. These agents are nothing like human employees. They never sleep, they can touch every endpoint in your system simultaneously, and they can generate sophisticated hacking tools in seconds. Traditional security measures designed for humans simply won't work. Dr. Ilia Shumailov https://x.com/iliaishacked https://iliaishacked.github.io/ https://sequrity.ai/ TRANSCRIPT: https://app.rescript.info/public/share/dVGsk8dz9_V0J7xMlwguByBq1HXRD6i4uC5z5r7EVGM TOC: 00:00:00 - Introduction & Trusted Third Parties via ML 00:03:45 - Background & Career Journey 00:06:42 - Safety vs Security Distinction 00:09:45 - Prompt Injection & Model Capability 00:13:00 - Agents as Worst-Case Adversaries 00:15:45 - Personal AI & CAML System Defense 00:19:30 - Agents vs Humans: Threat Modeling 00:22:30 - Calculator Analogy & Agent Behavior 00:25:00 - IMO Math Solutions & Agent Thinking 00:28:15 - Diffusion of Responsibility & Insider Threats 00:31:00 - Open Source Security Concerns 00:34:45 - Supply Chain Attacks & Trust Issues 00:39:45 - Architectural Backdoors 00:44:00 - Academic Incentives & Defense Work 00:48:30 - Semantic Censorship & Halting Problem 00:52:00 - Model Collapse: Theory & Criticism 00:59:30 - Career Advice & Ross Anderson Tribute REFS: Lessons from Defending Gemini Against Indirect Prompt Injections https://arxiv.org/abs/2505.14534 Defeating Prompt Injections by Design. Debenedetti, E., Shumailov, I., Fan, T., Hayes, J., Carlini, N., Fabian, D., Kern, C., Shi, C., Terzis, A., & Tramèr, F. https://arxiv.org/pdf/2503.18813 Agentic Misalignment: How LLMs could be insider threats https://www.anthropic.com/research/agentic-misalignment STOP ANTHROPOMORPHIZING INTERMEDIATE TOKENS AS REASONING/THINKING TRACES! Subbarao Kambhampati et al https://arxiv.org/pdf/2504.09762 Meiklejohn, S., Blauzvern, H., Maruseac, M., Schrock, S., Simon, L., & Shumailov, I. (2025). Machine learning models have a supply chain problem. https://arxiv.org/abs/2505.22778 Gao, Y., Shumailov, I., & Fawaz, K. (2025). Supply-chain attacks in machine learning frameworks. https://openreview.net/pdf?id=EH5PZW6aCr Apache Log4j Vulnerability Guidance https://www.cisa.gov/news-events/news/apache-log4j-vulnerability-guidance Bober-Irizar, M., Shumailov, I., Zhao, Y., Mullins, R., & Papernot, N. (2022). Architectural backdoors in neural networks. https://arxiv.org/pdf/2206.07840 Position: Fundamental Limitations of LLM Censorship Necessitate New Approaches David Glukhov, Ilia Shumailov, ... https://proceedings.mlr.press/v235/glukhov24a.html AlphaEvolve MLST interview [Matej Balog, Alexander Novikov] https://www.youtube.com/watch?v=vC9nAosXrJw

Ilia Shumailov

AI Security Researcher

Machine Learning Street Talk (MLST)/Sep 27, 2025/1h 8m

New top score on ARC-AGI-2-pub (29.4%) - Jeremy Berman

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 the cybernetic economyOct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlstSubmit investment deck: https://cyber.fund/contact?utm_source=mlst— Imagine trying to teach an AI to think like a human i.e. solving puzzles that are easy for us but stump even the smartest models. Jeremy's evolutionary approach—evolving natural language descriptions instead of python code like his last version—landed him at the top with about 30% accuracy on the ARCv2.We discuss why current AIs are like "stochastic parrots" that memorize but struggle to truly reason or innovate as well as big ideas like building "knowledge trees" for real understanding, the limits of neural networks versus symbolic systems, and whether we can train models to synthesize new ideas without forgetting everything else. Jeremy Berman:https://x.com/jerber888TRANSCRIPT:https://app.rescript.info/public/share/qvCioZeZJ4Q_NlR66m-hNUZnh-qWlUJcS15Wc2OGwD0TOC:Introduction and Overview [00:00:00]ARC v1 Solution [00:07:20]Evolutionary Python Approach [00:08:00]Trade-offs in Depth vs. Breadth [00:10:33]ARC v2 Improvements [00:11:45]Natural Language Shift [00:12:35]Model Thinking Enhancements [00:13:05]Neural Networks vs. Symbolism Debate [00:14:24]Turing Completeness Discussion [00:15:24]Continual Learning Challenges [00:19:12]Reasoning and Intelligence [00:29:33]Knowledge Trees and Synthesis [00:50:15]Creativity and Invention [00:56:41]Future Directions and Closing [01:02:30]REFS:Jeremy’s 2024 article on winning ARCAGI1-pubhttps://jeremyberman.substack.com/p/how-i-got-a-record-536-on-arc-agiGetting 50% (SoTA) on ARC-AGI with GPT-4o [Greenblatt]https://blog.redwoodresearch.org/p/getting-50-sota-on-arc-agi-with-gpt https://www.youtube.com/watch?v=z9j3wB1RRGA [his MLST interview]A Thousand Brains: A New Theory of Intelligence [Hawkins]https://www.amazon.com/Thousand-Brains-New-Theory-Intelligence/dp/1541675819https://www.youtube.com/watch?v=6VQILbDqaI4 [MLST interview]Francois Chollet + Mike Knoop’s labhttps://ndea.com/On the Measure of Intelligence [Chollet]https://arxiv.org/abs/1911.01547On the Biology of a Large Language Model [Anthropic]https://transformer-circuits.pub/2025/attribution-graphs/biology.html The ARChitects [won 2024 ARC-AGI-1-private]https://www.youtube.com/watch?v=mTX_sAq--zY Connectionism critique 1998 [Fodor/Pylshyn]https://uh.edu/~garson/F&P1.PDF Questioning Representational Optimism in Deep Learning: The Fractured Entangled Representation Hypothesis [Kumar/Stanley]https://arxiv.org/pdf/2505.11581 AlphaEvolve interview (also program synthesis)https://www.youtube.com/watch?v=vC9nAosXrJw ShinkaEvolve: Evolving New Algorithms with LLMs, Orders of Magnitude More Efficiently [Lange et al]https://sakana.ai/shinka-evolve/ Deep learning with Python Rev 3 [Chollet] - READ CHAPTER 19 NOW!https://deeplearningwithpython.io/

Jeremy Berman

Research Scientist

Reflection AI

Machine Learning Street Talk (MLST)/Sep 19, 2025/2h 4m

Deep Learning is Not So Mysterious or Different - Prof. Andrew Gordon Wilson (NYU)

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 trade-off" i.e. a balancing act between a model that's too simple and one that's too complex. **SPONSOR MESSAGES** — Tufa AI Labs is an AI research lab based in Zurich. **They are hiring ML research engineers!** This is a once in a lifetime opportunity to work with one of the best labs in Europe Contact Benjamin Crouzier - https://tufalabs.ai/ — 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 the cybernetic economy Oct SF conference - https://dagihouse.com/?utm_source=mlst - Joscha Bach keynoting(!) + OAI, Anthropic, NVDA,++ Hiring a SF VC Principal: https://talent.cyber.fund/companies/cyber-fund-2/jobs/57674170-ai-investment-principal#content?utm_source=mlst Submit investment deck: https://cyber.fund/contact?utm_source=mlst — Description Continued: Professor Wilson challenges this fundamental belief (fearing complexity). He makes a few surprising points: **Bigger Can Be Better**: massive models don't just get more flexible; they also develop a stronger "simplicity bias". So, if your model is overfitting, the solution might paradoxically be to make it even bigger. **The "Bias-Variance Trade-off" is a Misnomer**: Wilson claims you don't actually have to trade one for the other. You can have a model that is incredibly expressive and flexible while also being strongly biased toward simple solutions. He points to the "double descent" phenomenon, where performance first gets worse as models get more complex, but then surprisingly starts getting better again. **Honest Beliefs and Bayesian Thinking**: His core philosophy is that we should build models that honestly represent our beliefs about the world. We believe the world is complex, so our models should be expressive. But we also believe in Occam's razor—that the simplest explanation is often the best. He champions Bayesian methods, which naturally balance these two ideas through a process called marginalization, which he describes as an automatic Occam's razor. TOC: [00:00:00] Introduction and Thesis [00:04:19] Challenging Conventional Wisdom [00:11:17] The Philosophy of a Scientist-Engineer [00:16:47] Expressiveness, Overfitting, and Bias [00:28:15] Understanding, Compression, and Kolmogorov Complexity [01:05:06] The Surprising Power of Generalization [01:13:21] The Elegance of Bayesian Inference [01:33:02] The Geometry of Learning [01:46:28] Practical Advice and The Future of AI Prof. Andrew Gordon Wilson: https://x.com/andrewgwils https://cims.nyu.edu/~andrewgw/ https://scholar.google.com/citations?user=twWX2LIAAAAJ&hl=en https://www.youtube.com/watch?v=Aja0kZeWRy4 https://www.youtube.com/watch?v=HEp4TOrkwV4 TRANSCRIPT: https://app.rescript.info/public/share/H4Io1Y7Rr54MM05FuZgAv4yphoukCfkqokyzSYJwCK8 Hosts: Dr. Tim Scarfe / Dr. Keith Duggar (MIT Ph.D) REFS: Deep Learning is Not So Mysterious or Different [Andrew Gordon Wilson] https://arxiv.org/abs/2503.02113 Bayesian Deep Learning and a Probabilistic Perspective of Generalization [Andrew Gordon Wilson, Pavel Izmailov] https://arxiv.org/abs/2002.08791 Compute-Optimal LLMs Provably Generalize Better With Scale [Marc Finzi, Sanyam Kapoor, Diego Granziol, Anming Gu, Christopher De Sa, J. Zico Kolter, Andrew Gordon Wilson] https://arxiv.org/abs/2504.15208

Andrew Gordon Wilson

Professor

New York University

Machine Learning Street Talk (MLST)

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Deep Learning is Not So Mysterious or Different - Prof. Andrew Gordon Wilson (NYU)