Machine Learning Street Talk (MLST)

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1. Mathematics Just Changed Forever — Kevin Hartnett

   26 oktober 2026 · 1 uur 34 min

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   Kevin Hartnett is the author of *The Proof in the Code*.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---In 1998 Thomas Hales finished a proof of the Kepler conjecture that leaned so heavily on computer code that the Annals of Mathematics could not say whether it was true. That impasse opens Kevin Hartnett's book *The Proof in the Code*, and this documentary.Built around Tim's conversation with Hartnett, the film follows Lean from a side project at Microsoft Research to the system mathematicians now use to check their own field. Leonardo de Moura, Lean's creator, explains why everything rests on a small trusted kernel. Jeremy Avigad recalls the argument over foundations that changed Lean. Kevin Buzzard describes his fear that his corner of mathematics was becoming a house of cards, and Paul Lessard separates a correct proof from a useful one. Mathlib, Peter Scholze's Liquid Tensor challenge and Terence Tao's Equational Theories project all pass through.The second half turns to AI. AlphaProof, natural-language olympiad proofs and autoformalisation sharpen the question the book keeps asking: when a machine produces a proof nobody can read, what has actually been checked? The answer lies in the formalisation gap, the distance between the mathematics people mean and the statement a checker certifies. The film closes on the human story behind the software.---TIMESTAMPS:0:00 Cold open: proof, code and the wrong problem1:50 Kepler's oranges and the proof nobody could check6:54 How mathematicians decide a proof is true9:04 Correct is not the same as useful12:17 Sponsor: Cyber Fund12:53 What Lean is, and the small kernel it trusts15:23 From Microsoft's Z3 to Lean18:22 The argument over Lean's foundations24:52 One researcher versus a growing user base26:16 Kevin Buzzard's house of cards30:43 Mathlib as a barn raising37:16 The perfectoid-spaces stunt4… Lees meer

2. What Most People Get Wrong About Evolution | Akarsh Kumar

   10 oktober 2026 · 48 min

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   A lot of people in AI treat evolution as a dumb fallback, basically random search for when you can't take a gradient. Akarsh Kumar thinks that is wrong. Selection hangs on to partial solutions, so mutations only need to be useful about 1% of the time for the search to keep making progress.Akarsh is a PhD student at MIT working with Phillip Isola, works with Sakana AI, and is first author of the Fractured Entangled Representation paper with Kenneth Stanley, Jeff Clune and Joel Lehman. He tells Tim Scarfe why the path a learner takes may shape the structure of what it learns, and why that is a different way of looking at intelligence from the statistical one.Most of the conversation is about ASAL, the method he led for searching whole spaces of artificial worlds. Rather than predict what a rule will do, ASAL runs the simulation and asks a foundation model what happened. Mapped across all 262,144 Life-like rules, the most interesting worlds sit on one small island. Along the way: Game of Life, Lenia (with a clip from its creator, Bert Chan), neural cellular automata, Boids, Particle Life and the emergence of persistence.---TIMESTAMPS:00:00:00 Intro: artificial life, ASAL and Core War in four minutes00:04:19 Life as it could be, not just as it is00:05:46 Why the order you learn things in matters00:08:33 No shortcuts: Wolfram, novelty search and regularisation00:10:44 Kenneth Stanley: the path matters, not just the destination00:11:31 Statistical intelligence vs regularity-based intelligence00:13:49 Artificial chemistry and other possible universes00:15:59 Convergent patterns: shadows of the substrate?00:18:02 How Conway's Game of Life works00:20:49 Change one cell, change everything?00:22:31 Lenia (with Bert Chan) and neural cellular automata00:25:26 Boids, Particle Life and cell-like creatures00:28:47 Persistence, entropy and what life is00:30:15 ASAL: a foundation model as the critic00:34:04 Impostors inside the simulation00:36:50 From primordial soup to alien animal… Lees meer

3. How a Voice Agent Learns the Rhythm of Conversation — Shawn Wen

   1 oktober 2026 · 1 uur 10 min

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   Tsung-Hsien (Shawn) Wen, CTO of PolyAI, tells Tim Scarfe why voice agents are harder than text agents. Voice adds time, and a good conversation depends on adapting to the person on the line, not just on reasoning to the best answer. Shawn describes an audio-native model (Dialog-RSN-1) that first predicts a turn-taking signal, then replies in text with citations, and writes the transcript last so enterprises can audit it.Along the way: training on real, noisy calls with synthetic noise added, and why over-cleaned audio made the new model worse. Latency, and what a voice agent should do while it thinks. Why a voice with a hint of regional accent beats a generic one. Why public benchmarks fall short for voice, why enterprises want to own their agent harness, and whether behaviour belongs in the harness or in the weights.The last stretch is about working with agents: cognitive debt, the shift from producing content to checking it, Wispr Flow, building tools that agents can use, and whether slop is in the eye of the reader.This episode was produced in partnership with PolyAI.https://poly.ai CHAPTERS0:00 Why voice agents are harder than text4:27 What enterprises want, and why PolyAI built its own model9:04 How an audio-native voice model works15:21 Training data, spectrograms and synthetic noise20:36 The cocktail party problem and the future of turn-taking25:21 Latency, adaptive reasoning and keeping callers' trust31:33 Voices, personality and the uncanny valley36:32 How do you benchmark a voice agent?42:00 Harness engineering and owning the intelligence45:09 Well-specified problems and auditable agents49:46 Weight adaptation and cognitive debt56:31 Agents at work: Wispr Flow, voice and tool building1:02:50 The next decade of voice, and what counts as slopREFERENCESThe Bitter Lesson: http://www.incompleteideas.net/IncIdeas/BitterLesson.html [9:05]Retrieval-augmented generation: https://arxiv.org/abs/2005.11401 [13:23]Mel scale: https://en.wikipedia.org/wiki/Mel_scale [17… Lees meer

4. Who Checks a Proof No Human Can Read? — Leo de Moura

   30 september 2026 · 1 uur 14 min

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   Leonardo de Moura created Lean and co-created Z3. ---This episode is sponsored by Parallel.Parallel, where agents find answers: web search, extraction and deep research APIs built for AI agents.Start free with the Parallel MCP server and $5 of credits every month: https://parallel.ai/mlst?utm_source=creator&utm_medium=podcast&utm_content=MLST---Tim Scarfe talks with Leo about how Lean escaped its original audience, why dependent types and Mathlib made it useful to working mathematicians, and what happens when formal verification leaves the lab. De Moura explains the small trusted kernel and independent checkers, and gives his account of the recent Collatz incident, in which a purported proof was accepted by both Lean's official kernel and Nanoda, apparently by exploiting a different bug in each.---TIMESTAMPS:00:00:00 Cold open: the green checkmark can lie00:00:59 Cathedral or bazaar: who controls Lean's core?00:04:55 Why Lean's core stays small and protected00:08:07 The Slack purge, Brandolini's law and the Lean FRO00:11:12 The Collatz exploit: two kernels, two bugs00:16:44 More kernels, reward hacking and safety by transparency00:21:04 Sponsor: Parallel00:21:59 Kim Morrison, Claude and the zlib proof00:25:25 Can we specify complex systems?00:28:04 Specs change: proofs are cheaper to redo with AI00:31:27 From Lean 1 to Lean 400:34:57 Dependent types in plain terms00:37:25 Lean 4's extensibility and Mathlib's growth00:41:44 Mathlib as infrastructure: Formal Frontiers00:44:15 Creativity, abstraction and nut-sniping00:48:46 Breadcrumbs, not learning: what AI agents lack00:53:03 Competence without comprehension, and verified guardrails00:56:21 Is the human still the author?01:01:44 AlphaProof, LLMs and why certificates still matter01:06:38 What's next for Lean, and its legacy01:11:42 How to start learning Lean---REFERENCES:tool:[00:00:48] Leanhttps://lean-lang.org/[00:01:24] Mathlibhttps://github.com/leanprover-community/mathlib4[00:12:09] nanoda_libhttps://github.com/… Lees meer

5. When AI Research Starts Moving Faster Than Human Research - Zhengyao Jiang

   26 september 2026 · 44 min

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   Weco let an AI coding agent rewrite the harness around another agent for eight days: its code, prompts and tools, while the underlying language model stayed fixed. Tim Scarfe asks Weco co-founder Zhengyao Jiang what the reported gains over two years of human engineering actually demonstrate.The discussion examines AIDE 85's generated code, held-out evaluation and the difficulty of separating useful discoveries from reward hacking. Jiang explains Weco's four levels of recursive self-improvement and compares the experiment with AlphaEvolve and the Darwin Gödel Machine.The limits matter as much as the gains. Jiang explains why the experiment did not establish that the system had become a better improver. The conversation closes with open-ended search, human-designed primitives and Parameter Golf: where does the next useful idea come from when the agent is searching inside a space that people designed?---TIMESTAMPS:00:00:00 Eight days of self-improvement: what counts?00:03:25 AIDE and the puzzle of useful spaghetti code00:08:38 Four levels of recursive self-improvement00:12:02 What AIDE 85 changed and how it was tested00:20:04 AlphaEvolve, Darwin Gödel Machine and the RSI claim00:26:21 Reward hacking and the limits of detection00:33:09 Open-ended search, harness tuning and creativity00:39:43 Parameter Golf and the limits of self-improvement---REFERENCES:organization:[00:00:30] Weco AIhttps://www.weco.ai/other:[00:00:33] AIDE²: The First Evidence of Recursive Self-Improvementhttps://www.weco.ai/blog/first-evidence-of-recursive-self-improvement[00:14:11] Faulty reward functions in the wildhttps://openai.com/index/faulty-reward-functions/[00:29:59] The Hugging Face incident and the road aheadhttps://openai.com/index/hugging-face-incident-and-the-road-ahead/tool:[00:03:29] AIDEhttps://github.com/WecoAI/aideml[00:04:29] MLE-benchhttps://github.com/openai/mle-bench[00:04:33] ALE-Benchhttps://github.com/SakanaAI/ALE-Bench[00:04:52] WeatherBench 2https://github.com/google-rese… Lees meer

6. How Deep Learning Finally Cracked Messy Tables - Frank Hutter

   23 september 2026 · 1 uur 53 min

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   Frank Hutter, co-founder of Prior Labs, talks about TabPFN, a tabular foundation model that makes predictions in a single forward pass, and the research behind it. Lees meer

   TabPFN is pre-trained on synthetic datasets drawn from a prior over structural causal models, rather than on real data. At prediction time it takes the whole training table as context and outputs an approximation of the Bayesian posterior predictive distribution, without per-dataset training or hyperparameter search. Frank explains how this grew out of his earlier work on AutoML and neural architecture search, how the priors are built and revised, and why tabular data was hard for deep learning for so long.

   The conversation also covers the TabArena benchmark, how the architecture changed from TabPFN v1 to v3, scaling to larger tables, using the model with coding agents, test-time compute, Google's TabFM, causal inference and interventions, and relational data. At the end, a short update Frank recorded after the interview covers the TabPFN-3.5 release.

   Prior Labs:

   TabPFN-3.5: https://priorlabs.ai/tabpfn-3-5

   https://priorlabs.ai/careers

   TOC:

   00:00 Introduction

   00:44 Welcome and Frank's background

   02:05 Why tabular data was hard for deep learning

   10:17 Pre-training on synthetic data

   12:52 The TabArena benchmark

   19:28 From AutoML to neural architecture search

   26:34 TabPFN as a learned algorithm

   30:50 Bayesian prediction in one forward pass

   39:37 Scaling to larger tables

   47:48 Using TabPFN with coding agents

   57:47 Output heads and architecture from v1 to v3

   1:05:29 Test-time compute and adaptation

   1:13:32 Google's TabFM

   1:16:53 How the priors are designed

   1:18:40 Correlation, causation and interventions

   1:35:22 Relational and multimodal data

   1:38:31 Use in organisations

   1:46:38 The open research arm

   1:50:21 Update: TabPFN-3.5

   REFS:

   TabPFN v2, Nature (Hollmann et al., 2025)

   https://www.nature.com/articles/s41586-024-08328-6

   Transformers Can Do Bayesian Inference (Müller et al.)

   ht…

7. Why Scaling Prediction Cannot Create Intelligence - Alexander Mattick

   21 september 2026 · 2 uur 14 min

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   Alexander Mattick is a researcher at Fraunhofer IIS and a PhD researcher at the University of Technology Nuremberg (UTN), and a regular on Yannic Kilcher's Discord. He first came on MLST in 2022, after helping research the Yann LeCun and Randall Balestriero episode on interpolation. Lees meer

   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

   ---

   Alexander treats inference as the thread running through modern machine learning: once you have a model, what does it cost to get an answer out of it? He works through Monte Carlo, GFlowNets, energy-based models, diffusion, normalising flows and flow matching, with four short explainers he recorded himself. He is blunt about energy-based models: you can sample from them in principle, but it is rarely worth the compute. JEPA and "world model", he says, are closer to branding than to technical categories.

   Next: theories of deep learning, none of which he thinks predicts enough yet to guide practice, then reinforcement learning.

   ---

   0:00 Cold open: information is expensive

   0:51 Welcome back, Alexander Mattic

   2:08 Alexander's research background

   2:50 Inference: densities, sampling and Monte Carlo

   6:42 GFlowNets, energy functions and MCMC

   9:45 Explainer: energy-based models

   11:03 Why model a density at all?

   17:30 From learned energies to flow matching

   25:08 Explainers: diffusion and normalising flows

   28:33 Are energy-based models generative?

   33:22 JEPA, contrastive learning and collapse

   41:13 Why non-language modalities need flows

   44:51 Inference as search: branch and bound

   49:43 Q-learning and delayed consequences

   55:14 Flow matching, optimal transport, Fokker-Planck

   1:00:03 Explainer: flow matching

   1:01:49 AlphaFold, latents and scale versus architecture

   1:07:52 Two families of deep learning theory

   1:15:04 What a good theory would predict

   1:23:53 The manifold hypothesis and compressio…

8. How Physical AI Learns Across Language, Video and Action — Ming-Yu Liu

   15 september 2026 · 26 min

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   The car making a left turn at the start of this episode was never filmed. Cosmos 3 generated it. Ming-Yu Liu, who leads the Cosmos research at NVIDIA, explains how one model can describe a video, generate one, and produce robot actions. Lees meer

   He walks Tim through the architecture. A vision language model reasons one token at a time; its weights then initialise a bidirectional diffusion generator for video, audio and action, and a shared temporal position scheme lines up signals that run at different rates. Ming-Yu treats "world model" as a set of tools, not one definition: forward dynamics, inverse dynamics and policy, trained together under a capacity limit so that each helps the others. He also explains why plentiful first-person human video carries over to robots, which have far less data of their own, and why a Cosmos model post-trained on the DROID dataset is a good starting point for pick-and-place policies.

   The most practical thread is testing. A neural simulator does not need accurate success rates. It only needs to rank policy A above policy B the way the real world would, so a team can narrow down which checkpoints deserve a real trial. Cosmos Dreams applies that closed-loop idea to driving and robotics, and Ming-Yu argues that humanoids around children and pets make safety matter even more than it does for cars. The conversation ends on the Super, Nano and Edge sizes (Edge targets Jetson Thor, Orin and DGX Spark) and where to find the open weights, code and data.

   This episode is a paid partnership with NVIDIA.

   Learn more about Cosmos: https://nvda.ws/4cJoY1S

   Explore Cosmos Lab: https://research.nvidia.com/labs/cosmos-lab/cosmos3/

   ---

   TIMESTAMPS:

   00:00:00 A road that was never filmed

   00:02:28 Inside Cosmos 3: reasoning and generator towers

   00:05:02 World models: dynamics, policy and one clock

   00:08:59 Learning robot skills from human video

   00:11:06 Ambiguous tasks and system 2 planning

   00:12:53 Neural simulators for policy verification

   00:16:41 Co…

9. Speech Recognition Is Not a Solved Problem — Pavan Kumar Reddy

   14 september 2026 · 1 uur 42 min

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   Pavan Kumar Reddy leads audio research at Mistral AI. He joins Tim Scarfe for a deep technical tour of Voxtral — and explains why the frontier of deployed voice is still a cascade of specialised models rather than one end-to-end system. Lees meer

   IN PARTNERSHIP WITH MISTRAL AI:

   ---

   This episode was produced in partnership with Mistral AI.

   Mistral AI: https://mistral.ai/

   ---

   The conversation opens on architecture. Voxtral Chat feeds a 3B Ministral text trunk with continuous embeddings from an audio encoder, passed to the decoder as direct token input rather than through cross-attention as in Whisper, so the model can answer questions about emotion, timing and who spoke when without an intermediate transcript to lose them. The real-time model becomes a dual-stream decoder that consumes audio and emits text at once, at a target delay down to 160ms, with slower streams in parallel for anything that can wait for more context.

   On generation, Pavan explains why Voxtral TTS predicts continuous latents rather than discrete codec tokens, traces the lineage from SoundStream through EnCodec to Mimi's split of semantic and acoustic codebooks, and places FSQ and flow matching in it. Tim presses on the priors underneath: why a mel spectrogram instead of raw waveform, what noise augmentation buys, and when acoustic overfitting becomes somebody's fine-tuning problem. Then the failure modes. Diarisation is emitted autoregressively inside the transcript rather than by a separate head, which makes streaming diarisation fragile — less context, late speaker changes, invented extra speakers. And because the architecture commits to what it has already predicted, one out-of-distribution mistake compounds into looping or skipped segments, which is what DPO corrects: the negative supervision pre-training and SFT cannot give.

   The last third is the argument Tim keeps returning to. Customers running voice agents over millions of sessions describe scaffolding, not a solved problem, with a sharp dr…

10. How Replication Could Teach Machines What Good Science Looks Like — Edward Hughes

    11 september 2026 · 2 uur 2 min

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    Can a machine learn the judgement that separates a plausible-looking result from a faithful experiment? Edward Hughes, Chief Scientist and co-founder of Inherent, joins Tim Scarfe to argue that creativity is not optimisation, and that the missing capability in AI is choosing which questions are worth asking. Lees meer

    SPONSOR:

    ---

    Cyber Fund built the Monastery to help founders ship products that were impossible a year ago.

    Apply now: https://cyber.fund

    ---

    Edward makes the case that Move 37 was innovative rather than creative, and that the field, not the individual, decides what counts as a discovery. That reframing runs through Csikszentmihalyi, Deutsch and exaptation into open-endedness, where deceptive goals and imperfect world models turn out to be the point rather than the problem. The second half turns to the paper: Replica, a task space built by redacting figures from real papers, and Faraday, a 27-billion-parameter model trained to steer a frontier coding agent that then beats the frontier on held-out replications.

    ---

    TIMESTAMPS:

    00:00:00 Cold open: Move 37, Faraday and collective intelligence

    00:01:08 Sponsor: CyberFund

    00:01:46 Inherent's $50M raise and the road from string theory

    00:09:14 Three timescales of learning: weights, context, culture

    00:13:47 Move 37 was innovative, not creative: the field decides

    00:20:39 Creativity as satisficing: the urinal and evolution

    00:25:06 Exaptation and the Tristan chord: creativity in context

    00:30:56 Coherence for whom? Deutsch's hard-to-vary explanations

    00:35:53 Why copying is creative: Deutsch and the constraint engineer

    00:42:27 Societies of agents and the strong Moravec paradox

    00:45:51 Evaluate in hindsight: from Lean proofs to climate change

    00:51:56 Picbreeder, local goals and why discovery needs deception

    00:57:21 Spaghetti proofs, translation layers and superhuman Go

    01:00:37 Does nature compress? Naturalness and real patterns

    01:07:36 Why replicate? Replica's redacted figures and Faraday

    …

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