Machine Learning Street Talk (MLST)

Machine Learning Street Talk (MLST)

Welcome! We engage in fascinating discussions with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI, cognitive science, neuroscience and philosophy of mind with in-depth analysis. Our approach is unrivalled in terms of scope and rigour – we believe in intellectual diversity in AI, and we touch on all of the main ideas in the field with the hype surgically removed. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/) and features regular appearances from MIT Doctor of Philosophy Keith Duggar (https://www.linkedin.com/in/dr-keith-duggar/).

  1. 1 day ago

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

    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. 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 Cosmos as a starting point for robot policies 00:19:00 Cosmos Dreams and robot safety 00:22:04 Super, Nano and Edge model sizes 00:24:24 Open models, the Cosmos repo and feedback --- REFERENCES: tool: [00:00:13] Cosmos 3 (NVIDIA Cosmos Lab project page) https://research.nvidia.com/labs/cosmos-lab/cosmos3/ [00:18:27] NVIDIA Cosmos GitHub repository https://github.com/NVIDIA/cosmos [00:22:05] Cosmos3-Edge model card https://huggingface.co/nvidia/Cosmos3-Edge [00:22:15] Cosmos3-Super model card https://huggingface.co/nvidia/Cosmos3-Super [00:22:16] Cosmos3-Nano model card https://huggingface.co/nvidia/Cosmos3-Nano [00:22:50] NVIDIA Jetson Thor https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-thor/ [00:22:52] NVIDIA Jetson Orin https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/ [00:22:53] NVIDIA DGX Spark https://www.nvidia.com/en-us/products/workstations/dgx-spark/ [00:24:42] Cosmos 3 collection on Hugging Face https://huggingface.co/collections/nvidia/cosmos3 other: [00:01:07] Cosmos-Dreams closed-loop simulators (NVIDIA SIGGRAPH 2026 blog) https://blogs.nvidia.com/blog/siggraph-news-2026/ paper: [00:08:54] Cosmos 3: Omnimodal World Models for Physical AI https://arxiv.org/abs/2606.02800 [00:17:43] DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset https://arxiv.org/abs/2403.12945 --- RESCRIPT: https://app.rescript.info/share/e2385948cf465f0d6a2c0930150fc3ab

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

  2. 2 days ago

    Speech Recognition Is Not a Solved Problem — Pavan Muddireddy

    Pavankumar Reddy Muddireddy 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. 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 drop outside the top few languages. Cascades survive because each component stays separately adaptable, observable and constrainable. And voice alone is cognitive debt: absorbing information and deciding in one serial stream is harder than glancing at a menu. Voice becomes ubiquitous beside a screen, not instead of one. --- TIMESTAMPS: 00:00:00 Cold open 00:00:46 Why Mistral moved into audio 00:09:27 Inside Voxtral: trunk, encoder, dual streams 00:20:22 Speech that works in real time 00:30:52 How a voice becomes tokens 00:39:59 Flow matching, FSQ and the new codec 00:52:51 When speech models lose the speaker 01:03:23 Correcting hallucinations with preferences 01:12:12 Controlling synthetic speech 01:20:06 Why cascades still win 01:29:25 Speech in the wild 01:33:46 Audio models as interfaces 01:37:54 Why voice still needs a screen --- REFERENCES: paper: [00:01:42] Mistral 7B https://arxiv.org/abs/2310.06825 [00:09:38] Voxtral https://arxiv.org/abs/2507.13264 [00:14:41] Whisper: Robust Speech Recognition https://arxiv.org/abs/2212.04356 [00:19:11] Voxtral Realtime https://arxiv.org/abs/2602.11298 [00:21:52] Delayed Streams Modeling (Kyutai) https://arxiv.org/abs/2509.08753 [00:30:52] Voxtral TTS https://arxiv.org/abs/2603.25551 [00:32:38] SoundStream neural audio codec https://arxiv.org/abs/2107.03312 [00:34:59] Flow Matching for Generative Modeling https://arxiv.org/abs/2210.02747 [00:37:03] EnCodec: High Fidelity Neural Audio Compression https://arxiv.org/abs/2210.13438 [00:37:42] Moshi and the Mimi codec https://arxiv.org/abs/2410.00037 [00:39:05] Finite Scalar Quantization (FSQ) https://arxiv.org/abs/2309.15505 [01:03:33] Direct Preference Optimization (DPO) https://arxiv.org/abs/2305.18290 dataset: [00:46:14] Mozilla Common Voice https://commonvoice.mozilla.org/en/datasets organization: [00:50:47] Hugging Face https://huggingface.co/

    Speech Recognition Is Not a Solved Problem — Pavan Muddireddy

  3. 5 days ago

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

    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. 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 01:12:31 Faraday beats Codex, Claude and GLM 5.2 on held-out tasks 01:15:31 Replication to innovation: how the Transformer happened 01:18:26 Deep replication: what Faraday learns from Voyager and GNoME 01:23:37 Can the AI scientist cheat? Goodharting the judge 01:29:09 Inside Replica: scale-down, 8xB300 runs, per-task rubrics 01:34:11 The RL crisis: getting GRPO to work with per-turn credit 01:39:43 Weights vs harnesses: AlphaEvolve, DGM and EvoTune 01:45:45 The recursive company: agents cross a phase transition 01:50:35 Collective intelligence and the electric dynamo 01:55:46 What replaces OKRs? Incumbents and the burden of knowledge --- REFERENCES: MLST Creativity Article: https://archive.mlst.ai/read/why-creativity-cannot-be-interpolated organization: [00:01:47] Inherent https://inherentlabs.ai/ other: [00:20:51] Marcel Duchamp, Fountain https://www.tate.org.uk/art/artworks/duchamp-fountain-t07573 [00:05:19] Human-Timescale Adaptation in an Open-Ended Task Space (Adaptive Agent) https://arxiv.org/abs/2301.07608 [00:06:05] The AI Scientist https://arxiv.org/abs/2408.06292 [00:12:13] Training AI Scientists to Replicate Research (Replica and Faraday) https://arxiv.org/abs/2608.13331 [01:44:46] Evolutionary Principles in Self-Referential Learning https://people.idsia.ch/~juergen/diploma.html [01:59:33] Are Ideas Getting Harder to Find? https://www.nber.org/papers/w23782 book: [00:16:04] Creativity: Flow https://search.worldcat.org/title/254487436 [00:26:22] Why Greatness Cannot Be Planned https://link.springer.com/book/10.1007/978-3-319-15524-1 [00:33:03] The Beginning of Infinity https://www.penguinrandomhouse.com/books/293575/the-beginning-of-infinity-by-david-deutsch/ [01:55:47] Laws of Knowledge https://www.penguin.co.nz/books/the-infinite-alphabet-9780241655672 (Full list refs on YT/rescript) --- RESCRIPT: https://app.rescript.info/session/670296ba913761d0?share=6281911cac9bdbff637f10819d4d1e5c

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

  4. 2 Sept

    Designing How AI Grows — Tom McGrath

    Tom McGrath is co-founder and Chief Scientist at Goodfire, and a former Google DeepMind researcher. He joins Tim Scarfe to ask what neural networks actually learn, whether their internal representations converge on structures in the world, and whether interpretability can extract new scientific knowledge rather than merely explain model outputs. Beginning with AlphaZero and learned modularity, the conversation moves into neural geometry: concept manifolds, reusable computation inside Llama, and why activation steering can fail when it pushes a model off-manifold. McGrath then makes the case for intentional design, using interpretability as part of the training loop. They examine controlled generalisation, features as rewards, predictive data debugging, and the uncomfortable fact that a model may recognise a hallucination or reward hack and still produce it. The discussion closes on grader awareness, oversight and collusion between adaptive agents, then returns to sparse autoencoders. SAEs are useful, McGrath argues, but they may fracture the higher-dimensional structures networks actually use. This episode was made with support from Goodfire. --- TIMESTAMPS: 00:00:00 Introduction: Can interpretability speed-run science? 00:02:03 The invisible grader 00:06:51 What AlphaZero learned from the world 00:12:24 Interpretability as a control loop 00:21:54 The forbidden method and safer interventions 00:37:36 Why models catch hallucinations too late 00:46:19 Debug the dataset before training 00:50:44 Why neural networks become modular 00:55:57 Finding the geometry inside a network 01:02:55 Why steering falls off the manifold 01:12:10 A reusable calculator inside Llama 01:17:19 From abstractions to goals 01:25:28 Reward hacking, oversight and collusion 01:37:23 Are sparse autoencoders dead? --- REFERENCES: paper: [00:05:45] Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs https://arxiv.org/abs/2502.17424v7 [00:11:05] Acquisition of Chess Knowledge in AlphaZero https://arxiv.org/abs/2111.09259 [00:25:30] Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning https://arxiv.org/abs/2507.16795 [00:29:30] Persona Vectors: Monitoring and Controlling Character Traits in Language Models https://arxiv.org/abs/2507.21509 [00:41:14] Features as Rewards: Scalable Supervision for Open-Ended Tasks via Interpretability https://arxiv.org/abs/2602.10067 [00:47:03] Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signal https://arxiv.org/abs/2606.12360 [01:00:26] Do Sparse Autoencoders Capture Concept Manifolds? https://arxiv.org/abs/2604.28119 [01:03:04] Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior https://arxiv.org/abs/2605.05115 [01:14:20] Arithmetic in the Wild: Llama uses Base-10 Addition to Reason About Cyclic Concepts https://arxiv.org/abs/2605.01148 [01:29:35] Measuring Reward-Seeking via Contrastive Belief Updates https://arxiv.org/abs/2607.18966v1 other: [00:15:44] Intentional Design https://www.goodfire.com/blog/intentional-design [00:56:12] The World Inside Neural Networks https://www.goodfire.com/research/the-world-inside-neural-networks [01:37:28] A Pragmatic Vision for Interpretability https://www.alignmentforum.org/posts/StENzDcD3kpfGJssR/a-pragmatic-vision-for-interpretability --- RESCRIPT: https://app.rescript.info/share/846cfee4131b664fd09209cc3b98018e

    Designing How AI Grows — Tom McGrath

  5. 22 Aug

    Stealing Reasoning Traces from Proprietary LLM APIs — Ilia Shumailov & Alexander Panfilov

    Tim Scarfe speaks with Ilia Shumailov and Alexander Panfilov about their paper, Stealing Reasoning Traces from Proprietary LLM APIs.The core bug sounds deceptively simple: providers return encrypted reasoning state so conversations can be resumed or forked. But those blobs can be replayed across users and sibling models. A smaller model can ask the provider to decrypt the trace, then repeat the hidden reasoning in plain text. The discussion covers leaked private data, a broadly reusable jailbreak, poisoned agent traces, chain-of-thought monitoring, responsible disclosure, and possible defenses.Ilia Shumailov is an AI and security researcher, formerly at Google DeepMind, who completed his Cambridge PhD under Ross Anderson. Alexander Panfilov is a PhD researcher at the ELLIS Institute Tübingen and the Max Planck Institute for Intelligent Systems, working on AI safety, adversarial machine learning, and LLM red-teaming. They close by separating the demonstrated jailbreaking threat from ordinary benign distillation, and by arguing for controlled experiments over sweeping claims.---TIMESTAMPS:00:00:00 Intro montage00:01:33 Portable encrypted thought and decoded reasoning00:24:55 How the attack works and what it means00:39:04 Doom, defense, and scientific restraint---REFERENCES:paper:[00:00:00] Stealing Reasoning Traces from Proprietary LLM APIshttps://arxiv.org/abs/2608.09867[00:09:22] Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safetyhttps://arxiv.org/abs/2507.11473[00:11:30] Reasoning Models Don’t Always Say What They Thinkhttps://www.anthropic.com/research/reasoning-models-dont-say-think[00:37:22] PostTrainBench: Can LLM Agents Automate LLM Post-Training?https://arxiv.org/abs/2603.08640[00:41:02] Large-scale online deanonymization with LLMshttps://arxiv.org/abs/2602.16800other:[00:09:28] OpenAI and Hugging Face partner to address security incident during model evaluationhttps://openai.com/index/hugging-face-model-evaluation-security-incident/[00:10:22] Claude, GPT, and Gemini All Struggle to Evade Monitorshttps://metr.org/notes/2025-08-22-claude-gpt-gemini-struggle-evade-monitors/tool:[00:42:08] Isabelle proof assistanthttps://isabelle.in.tum.de/---RESCRIPT: https://app.rescript.info/share/07fc38276e0823dc9b8986c32e202c7f

    Stealing Reasoning Traces from Proprietary LLM APIs — Ilia Shumailov & Alexander Panfilov

  6. 20 Aug

    Every Exponential Ends — Silicon Valley Forgot — Adam Becker

    Astrophysicist Adam Becker, author of "What Is Real?", joins Tim Scarfe to take apart the futures Silicon Valley keeps selling: the 2045 singularity, mind uploading, Mars colonies, and the AI apocalypse. His new book *More Everything Forever* argues these ideas are hugely influential, mostly evidence-free, and bankrolled by tech billionaires who need a story in which growth never ends.Becker does the physics the boosters skip. Kurzweil's "law of accelerating returns" rests on cherry-picked data, and every exponential ends. Grant Bezos his perpetual energy growth and humanity boils the oceans within a few centuries, then exhausts the observable universe in under 4,000 years. The stars are too far away, Mars dirt is poison, and the day the dinosaur-killing asteroid hit Earth was still nicer than any day on Mars. On AI, Becker calls LLMs pocket calculators for language: hallucination is the model doing exactly what it always does, and the intelligence explosion assumes intelligence is a single number you can buy with compute.The sting is that Becker thinks the doomers are sincere. Yudkowsky, Bostrom and the effective altruists are not grifters, he says, just wrong, and their warnings that AI could end the world feed the same growth story the money depends on. He closes with his own prescription: take social problems seriously, regulate the whole tech industry, and tax billionaires out of existence.---TIMESTAMPS:00:00:00 Cold open and the thesis of More Everything Forever00:04:24 Kurzweil's singularity and the physical limits of exponential growth00:14:02 High agency and the fantasy of imprinting humanity on the cosmos00:16:55 Mind uploading, functionalism, and embodied cognition00:24:24 AI psychosis and anthropomorphizing LLMs00:26:24 Calculators, hallucination, and the limits of scale00:32:20 Yudkowsky and the intelligence-explosion argument00:40:37 True believers, venture capital, and the sci-fi growth narrative00:47:21 From Extropians to EA: utilitarianism and longtermism00:53:50 Brain worms and Becker's prescription: take social science seriously00:56:49 Why the AI-ethics discourse is broken01:01:42 The eugenics and IQ argument against 'intelligence'01:06:07 Why space settlement fails: Mars, the moon, and orbital data centers01:10:42 Billionaire myths and the search for purpose01:13:38 Tax billionaires, regulate tech: closing prescriptions---REFERENCES:book:[00:00:07] More Everything Forever (Adam Becker, 2025)https://www.hachettebookgroup.com/titles/adam-becker/more-everything-forever/9781541619593/[00:00:15] What Is Real? (Adam Becker, 2018)https://en.wikipedia.org/wiki/What_Is_Real%3F[00:15:46] What We Owe the Future (Will MacAskill, 2022)https://www.hachettebookgroup.com/titles/william-macaskill/what-we-owe-the-future/9781541618626/other:[00:00:27] Dreaming Against the Machine (podcast)https://www.dreamingagainstthemachine.com[00:01:04] The Useful Idiots of AI Doomsaying (Adam Becker, The Atlantic, 2025)https://www.theatlantic.com/books/archive/2025/09/what-ais-doomers-and-utopians-have-in-common/684270/ RESCRIPT: https://app.rescript.info/share/d6e37f9866673d8f74a39076efa5926b

    Every Exponential Ends — Silicon Valley Forgot — Adam Becker

  7. 10 Aug

    AI Is Learning at the Wrong Level of Abstraction — Matthieu Wyart

    This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at https://notion.com/mlstWhy can deep networks discover abstractions that shallow models miss? Statistical physicist Matthieu Wyart joins Tim Scarfe to argue that the answer lies in the hidden hierarchy of data. Language and images are built from parts within parts; depth lets a network recover those coarse-grained variables and escape the curse of dimensionality.The conversation moves from jamming transitions and rough loss surfaces to Chomsky, context-free grammars and machine creativity. Wyart explains why next-token prediction can still recover compositional structure, where current systems fall short of genuine scientific invention, and why predicting latent representations rather than raw tokens could make learning far more sample-efficient.They also examine diffusion models, neural scaling laws and the limits of physics-inspired theory. The final question is on a personal note: if mistakes are the price of leaving the beaten path, how much scientific risk is worth taking?---TIMESTAMPS:00:00:00 Can machines learn abstractions from data?00:02:00 Notion agentic workspace00:02:49 From statistical physics to machine learning00:06:40 What physics can explain about learning00:16:37 From Carnot to Chomsky bulldozer00:21:21 How deep networks recover hidden hierarchies00:32:43 Where machine creativity still falls short00:40:48 How deep nets escape the curse of dimensionality00:52:19 Why predict latents instead of tokens01:02:49 The sample-efficiency case for latent prediction01:08:31 Diffusion, scaling laws and text entropy01:16:40 The scientists we learn from and the mistakes we make---REFERENCES:person:[00:00:43] Noam Chomskyhttps://linguistics.mit.edu/user/chomsky/tool:[00:02:08] Notion Developer Platformhttps://www.notion.com/en-gb/blog/introducing-developer-platformpaper:[00:04:43] Mastering the game of Go with deep neural networks and tree searchhttps://www.nature.com/articles/nature16961[00:05:52] Reconciling modern machine-learning practice and the bias-variance trade-offhttps://arxiv.org/abs/1812.11118[00:25:54] How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Modelhttps://arxiv.org/abs/2307.02129[00:42:12] Efficient Estimation of Word Representations in Vector Spacehttps://arxiv.org/abs/1301.3781[00:52:46] Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecturehttps://arxiv.org/abs/2301.08243[00:52:54] Learn from your own latents and not from tokens: A sample-complexity theoryhttps://arxiv.org/abs/2605.27734[01:08:31] A Phase Transition in Diffusion Models Reveals the Hierarchical Nature of Datahttps://arxiv.org/abs/2402.16991[01:11:39] Scaling Laws for Neural Language Modelshttps://arxiv.org/abs/2001.08361[01:12:17] Deriving Neural Scaling Laws from the statistics of natural languagehttps://arxiv.org/abs/2602.07488[01:13:34] Prediction and Entropy of Printed Englishhttps://ieeexplore.ieee.org/document/6773263---LINKS:Download PDF transcript: https://app.rescript.info/share/f7644cdaa86c5cc1e41e484e290f2bd4

    AI Is Learning at the Wrong Level of Abstraction — Matthieu Wyart

  8. 13 Jul

    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/mlst Britain'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. Pullen argues that an inference company, rather than a training-first lab, doesn't need billions to compete: millions, a national compute allocation, and a consortium feedback loop can be enough. From there it gets into the machinery, why open-weight models still trail the frontier on size, active parameters and data, the mixture-of-experts versus dense trade-off and why active params dominate how a model actually feels, and the edge that real coding trajectories confer. The back half is about making agents trustworthy. Pullen makes the case for beating "slop" by rewarding the process instead of the final answer, reframes code review as runtime proof (spin the bug up in a VM and force the agent to actually exploit it), and walks through Swarm, Cosine's system running hundreds of sub-agents in one shot. It ends on why memory is still an unsolved hack, how synthetic graders let you run RL on tasks with no built-in test, and why Pullen reads US export controls as an accidental gift, with a supply-chain sting in the tail. --- TIMESTAMPS: 00:00:00 The sovereign mandate and the Fable ban 00:04:02 Millions vs billions: the inference-company model 00:07:19 The consortium feedback loop 00:07:40 Why open models lag the frontier 00:14:59 MoE vs dense, and why active params matter 00:16:29 Trajectories: the process-data advantage 00:19:48 Beating slop: reward the process, not the answer 00:26:06 Reusable abstractions and the epistemic wall 00:29:56 Code review becomes runtime proof 00:37:32 Do agentic harnesses still matter? 00:40:35 Swarm: orchestrating hundreds of sub-agents 00:45:14 Why memory is still unsolved 00:48:25 Synthetic data and graders for RL 00:53:09 The US export gift and supply-chain risk --- REFERENCES: organization: [00:01:15] Cosine https://cosine.sh [00:04:14] Mistral AI https://mistral.ai [00:05:50] Anthropic https://www.anthropic.com [00:07:42] Cohere https://cohere.com [00:08:36] DeepSeek https://www.deepseek.com tool: [00:02:52] Isambard-AI https://isambard.ac.uk [00:05:56] Colossus (xAI) https://en.wikipedia.org/wiki/Colossus_(supercomputer) [00:07:52] GLM (Z.ai) https://z.ai [00:11:52] NVIDIA B300 https://www.nvidia.com/en-us/data-center/dgx-b300/ [00:15:37] gpt-oss-120b https://huggingface.co/openai/gpt-oss-120b [00:15:52] Devstral 2 https://mistral.ai/news/devstral [00:16:01] Llama 70b https://www.llama.com [00:17:05] Claude Code https://www.anthropic.com/claude-code [00:26:23] ARC-AGI (Francois Chollet) https://arcprize.org [00:40:38] Swarm (Cosine) https://cosine.sh [00:40:50] OpenAI Codex https://github.com/openai/codex [00:41:16] Lumen Outpost (Cosine) https://cosine.sh [00:41:18] Kimi K2 (Moonshot) https://huggingface.co/moonshotai/Kimi-K2-Instruct [00:49:55] SWE-bench https://www.swebench.com [00:52:40] SystemVerilog https://en.wikipedia.org/wiki/SystemVerilog person: [00:23:40] Andrej Karpathy https://karpathy.ai paper: [00:27:10] GRPO (DeepSeekMath) https://arxiv.org/abs/2402.03300 [00:27:13] GSPO https://arxiv.org/abs/2507.18071 Incompressible Knowledge Probes, Bojie Li https://arxiv.org/pdf/2604.24827 Estimating the Size of Claude Opus 4.5/4.6 https://unexcitedneurons.substack.com/p/estimating-the-size-of-claude-opus --- ReScript: https://app.rescript.info/session/5852d2b884c4ce4b?share=10b9799160845bb11779f8ac6cd3124f

    Why a Nation Can't Outsource Its Frontier AI - Alistair Pullen (Cosine AI)
See All (265)

About

Welcome! We engage in fascinating discussions with pre-eminent figures in the AI field. Our flagship show covers current affairs in AI, cognitive science, neuroscience and philosophy of mind with in-depth analysis. Our approach is unrivalled in terms of scope and rigour – we believe in intellectual diversity in AI, and we touch on all of the main ideas in the field with the hype surgically removed. MLST is run by Tim Scarfe, Ph.D (https://www.linkedin.com/in/ecsquizor/) and features regular appearances from MIT Doctor of Philosophy Keith Duggar (https://www.linkedin.com/in/dr-keith-duggar/).

Information

  • Creator
    Machine Learning Street Talk (MLST)
  • Years Active
    2020 - 2026
  • Episodes
    265
  • Rating
    Clean
  • Copyright
    © Machine Learning Street Talk (MLST)
  • Show Website
    Machine Learning Street Talk (MLST)