Machine Learning Street Talk (MLST)

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:
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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

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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
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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>

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)

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/

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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

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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

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/

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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

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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

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.
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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

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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

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LINKS:
RESCRIPT: https://app.rescript.info/public/share/ff7gb6HpezOR3DF-gr9-rCoMFzzEgUjLQK6voV5XVWY