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/4cJoY1SExplore Cosmos Lab: https://research.nvidia.com/labs/cosmos-lab/cosmos3/---TIMESTAMPS:00:00:00 A road that was never filmed00:02:28 Inside Cosmos 3: reasoning and generator towers00:05:02 World models: dynamics, policy and one clock00:08:59 Learning robot skills from human video00:11:06 Ambiguous tasks and system 2 planning00:12:53 Neural simulators for policy verification00:16:41 Cosmos as a starting point for robot policies00:19:00 Cosmos Dreams and robot safety00:22:04 Super, Nano and Edge model sizes00:24:24 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 repositoryhttps://github.com/NVIDIA/cosmos[00:22:05] Cosmos3-Edge model cardhttps://huggingface.co/nvidia/Cosmos3-Edge[00:22:15] Cosmos3-Super model cardhttps://huggingface.co/nvidia/Cosmos3-Super[00:22:16] Cosmos3-Nano model cardhttps://huggingface.co/nvidia/Cosmos3-Nano[00:22:50] NVIDIA Jetson Thorhttps://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-thor/[00:22:52] NVIDIA Jetson Orinhttps://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/[00:22:53] NVIDIA DGX Sparkhttps://www.nvidia.com/en-us/products/workstations/dgx-spark/[00:24:42] Cosmos 3 collection on Hugging Facehttps://huggingface.co/collections/nvidia/cosmos3other:[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 AIhttps://arxiv.org/abs/2606.02800[00:17:43] DROID: A Large-Scale In-The-Wild Robot Manipulation Datasethttps://arxiv.org/abs/2403.12945---RESCRIPT: https://app.rescript.info/share/e2385948cf465f0d6a2c0930150fc3ab
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 open00:00:46 Why Mistral moved into audio00:09:27 Inside Voxtral: trunk, encoder, dual streams00:20:22 Speech that works in real time00:30:52 How a voice becomes tokens00:39:59 Flow matching, FSQ and the new codec00:52:51 When speech models lose the speaker01:03:23 Correcting hallucinations with preferences01:12:12 Controlling synthetic speech01:20:06 Why cascades still win01:29:25 Speech in the wild01:33:46 Audio models as interfaces01:37:54 Why voice still needs a screen---REFERENCES:paper:[00:01:42] Mistral 7Bhttps://arxiv.org/abs/2310.06825[00:09:38] Voxtralhttps://arxiv.org/abs/2507.13264[00:14:41] Whisper: Robust Speech Recognitionhttps://arxiv.org/abs/2212.04356[00:19:11] Voxtral Realtimehttps://arxiv.org/abs/2602.11298[00:21:52] Delayed Streams Modeling (Kyutai)https://arxiv.org/abs/2509.08753[00:30:52] Voxtral TTShttps://arxiv.org/abs/2603.25551[00:32:38] SoundStream neural audio codechttps://arxiv.org/abs/2107.03312[00:34:59] Flow Matching for Generative Modelinghttps://arxiv.org/abs/2210.02747[00:37:03] EnCodec: High Fidelity Neural Audio Compressionhttps://arxiv.org/abs/2210.13438[00:37:42] Moshi and the Mimi codechttps://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/2305.18290dataset:[00:46:14] Mozilla Common Voicehttps://commonvoice.mozilla.org/en/datasetsorganization:[00:50:47] Hugging Facehttps://huggingface.co/
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 intelligence00:01:08 Sponsor: CyberFund00:01:46 Inherent's $50M raise and the road from string theory00:09:14 Three timescales of learning: weights, context, culture00:13:47 Move 37 was innovative, not creative: the field decides00:20:39 Creativity as satisficing: the urinal and evolution00:25:06 Exaptation and the Tristan chord: creativity in context00:30:56 Coherence for whom? Deutsch's hard-to-vary explanations00:35:53 Why copying is creative: Deutsch and the constraint engineer00:42:27 Societies of agents and the strong Moravec paradox00:45:51 Evaluate in hindsight: from Lean proofs to climate change00:51:56 Picbreeder, local goals and why discovery needs deception00:57:21 Spaghetti proofs, translation layers and superhuman Go01:00:37 Does nature compress? Naturalness and real patterns01:07:36 Why replicate? Replica's redacted figures and Faraday01:12:31 Faraday beats Codex, Claude and GLM 5.2 on held-out tasks01:15:31 Replication to innovation: how the Transformer happened01:18:26 Deep replication: what Faraday learns from Voyager and GNoME01:23:37 Can the AI scientist cheat? Goodharting the judge01:29:09 Inside Replica: scale-down, 8xB300 runs, per-task rubrics01:34:11 The RL crisis: getting GRPO to work with per-turn credit01:39:43 Weights vs harnesses: AlphaEvolve, DGM and EvoTune01:45:45 The recursive company: agents cross a phase transition01:50:35 Collective intelligence and the electric dynamo01:55:46 What replaces OKRs? Incumbents and the burden of knowledge---REFERENCES:MLST Creativity Article:https://archive.mlst.ai/read/why-creativity-cannot-be-interpolatedorganization:[00:01:47] Inherenthttps://inherentlabs.ai/other:[00:20:51] Marcel Duchamp, Fountainhttps://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 Scientisthttps://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 Learninghttps://people.idsia.ch/~juergen/diploma.html[01:59:33] Are Ideas Getting Harder to Find?https://www.nber.org/papers/w23782book:[00:16:04] Creativity: Flowhttps://search.worldcat.org/title/254487436[00:26:22] Why Greatness Cannot Be Plannedhttps://link.springer.com/book/10.1007/978-3-319-15524-1[00:33:03] The Beginning of Infinityhttps://www.penguinrandomhouse.com/books/293575/the-beginning-of-infinity-by-david-deutsch/[01:55:47] Laws of Knowledgehttps://www.penguin.co.nz/books/the-infinite-alphabet-9780241655672(Full list refs on YT/rescript)---RESCRIPT:https://app.rescript.info/session/670296ba913761d0?share=6281911cac9bdbff637f10819d4d1e5c
Could slowing AI development make superintelligence safer? Daniel Kokotajlo and Thomas Larsen of the AI Futures Project join Tim Scarfe to examine AI 2040: Plan A, a proposal to buy time before AI exceeds human control.SPONSOR:---Cyber Fund built the Monastery to help founders ship products that were impossible a year ago.Apply now: https://cyber.fund---After revisiting AI 2027 and the limits of forecasting, they ask what happens when AI can automate research and sustain an economy without human workers. Tim challenges the case for general models and asks whether intelligence alone explains power. Plan A proposes an initial pause to build safety infrastructure, then cautious development up to the strongest AI that can still be reliably controlled. The discussion tests the distinction between control and alignment, the case for public AI research, and whether the US and China could enforce a slowdown. It ends with the evidence that would change their forecasts.---TIMESTAMPS:00:00:00 AI 2040: a slower route to superintelligence00:01:34 Sponsor: Cyber Fund00:02:12 From OpenAI to AI 202700:06:58 Forecasts, war games and self-fulfilling prophecies00:17:44 Why AI sceptics are changing their minds00:23:04 When AI can replace its own researchers00:28:45 Could an AI economy grow without human workers?00:37:32 One general model or a society of specialists?00:47:43 Brains, machines and collective intelligence00:56:12 Plan A: buy time at the controllable frontier01:00:02 Why control buys time but cannot replace alignment01:06:36 Why AI research should be public01:10:32 Can the US and China enforce an AI slowdown?01:19:04 Why AI policy debates miss the technology01:21:56 Is AI normal technology? The remaining disagreementMany thanks to James Wilken-Smith for helping with show research. ---REFERENCES:other:[00:00:01] AI 2040: Plan Ahttps://ai-2040.com/[00:03:27] AI 2027https://ai-2027.com/[00:13:47] Scenario Scrutiny for AI Policyhttps://blog.aifutures.org/p/scenario-scrutiny-for-ai-policy[00:33:11] The 2028 Global Intelligence Crisishttps://www.citriniresearch.com/p/2028gic[01:00:40] Brief independent investigation of agents' behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incidenthttps://www.redwoodresearch.org/research/hugging-face-incident[01:09:21] The Hugging Face incident and the road aheadhttps://openai.com/index/hugging-face-incident-and-the-road-ahead/[01:22:01] AI as Normal Technologyhttps://www.normaltech.ai/p/ai-as-normal-technology[01:22:51] Common Ground between AI 2027 & AI as Normal Technologyhttps://asteriskmag.substack.com/p/common-ground-between-ai-2027-andperson:[00:19:43] Geoffrey Hintonhttps://www.cs.toronto.edu/~hinton/[00:20:07] Ryan Greenblatthttps://www.lesswrong.com/users/ryan_greenblatt[00:26:06] Elon Muskhttps://www.tesla.com/elon-musktool:[00:21:46] ARC-AGI-3https://arcprize.org/arc-agi/3[00:21:53] AlphaGo and Move 37https://deepmind.google/research/alphago/[00:39:41] Claudehttps://claude.com/product/overview[00:39:58] NVIDIA H100 GPUhttps://www.nvidia.com/en-us/data-center/h100/paper:[00:24:42] Training AI Scientists to Replicate Researchhttps://arxiv.org/abs/2608.13331v1[01:27:19] Validity of the single processor approach to achieving large scale computing capabilitieshttps://www.cs.cmu.edu/~18742/papers/Amdahl1967.pdfbook:[00:28:52] Bullshit Jobs: A Theoryhttps://www.simonandschuster.com/books/Bullshit-Jobs/David-Graeber/9781501143335organization:[01:05:09] Redwood Researchhttps://www.redwoodresearch.org/---RESCRIPT: https://app.rescript.info/public/share/33d1a58fa8f307ae7dfd504d4fdaa9d5
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 grader00:06:51 What AlphaZero learned from the world00:12:24 Interpretability as a control loop00:21:54 The forbidden method and safer interventions00:37:36 Why models catch hallucinations too late00:46:19 Debug the dataset before training00:50:44 Why neural networks become modular00:55:57 Finding the geometry inside a network01:02:55 Why steering falls off the manifold01:12:10 A reusable calculator inside Llama01:17:19 From abstractions to goals01:25:28 Reward hacking, oversight and collusion01:37:23 Are sparse autoencoders dead?---REFERENCES:paper:[00:05:45] Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMshttps://arxiv.org/abs/2502.17424v7[00:11:05] Acquisition of Chess Knowledge in AlphaZerohttps://arxiv.org/abs/2111.09259[00:25:30] Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuninghttps://arxiv.org/abs/2507.16795[00:29:30] Persona Vectors: Monitoring and Controlling Character Traits in Language Modelshttps://arxiv.org/abs/2507.21509[00:41:14] Features as Rewards: Scalable Supervision for Open-Ended Tasks via Interpretabilityhttps://arxiv.org/abs/2602.10067[00:47:03] Anatomy of Post-Training: Using Interpretability to Characterize Data and Shape the Learning Signalhttps://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 Behaviorhttps://arxiv.org/abs/2605.05115[01:14:20] Arithmetic in the Wild: Llama uses Base-10 Addition to Reason About Cyclic Conceptshttps://arxiv.org/abs/2605.01148[01:29:35] Measuring Reward-Seeking via Contrastive Belief Updateshttps://arxiv.org/abs/2607.18966v1other:[00:15:44] Intentional Designhttps://www.goodfire.com/blog/intentional-design[00:56:12] The World Inside Neural Networkshttps://www.goodfire.com/research/the-world-inside-neural-networks[01:37:28] A Pragmatic Vision for Interpretabilityhttps://www.alignmentforum.org/posts/StENzDcD3kpfGJssR/a-pragmatic-vision-for-interpretability---RESCRIPT:https://app.rescript.info/share/846cfee4131b664fd09209cc3b98018e