Machine Learning Street Talk (MLST)

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/

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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

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/

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

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 superintelligence
00:01:34 Sponsor: Cyber Fund
00:02:12 From OpenAI to AI 2027
00:06:58 Forecasts, war games and self-fulfilling prophecies
00:17:44 Why AI sceptics are changing their minds
00:23:04 When AI can replace its own researchers
00:28:45 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 intelligence
00:56:12 Plan A: buy time at the controllable frontier
01:00:02 Why control buys time but cannot replace alignment
01:06:36 Why AI research should be public
01:10:32 Can the US and China enforce an AI slowdown?
01:19:04 Why AI policy debates miss the technology
01:21:56 Is AI normal technology? The remaining disagreement

Many thanks to James Wilken-Smith for helping with show research.

---
REFERENCES:
other:
[00:00:01] AI 2040: Plan A
https://ai-2040.com/
[00:03:27] AI 2027
https://ai-2027.com/
[00:13:47] Scenario Scrutiny for AI Policy
https://blog.aifutures.org/p/scenario-scrutiny-for-ai-policy
[00:33:11] The 2028 Global Intelligence Crisis
https://www.citriniresearch.com/p/2028gic
[01:00:40] Brief independent investigation of agents' behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident
https://www.redwoodresearch.org/research/hugging-face-incident
[01:09:21] The Hugging Face incident and the road ahead
https://openai.com/index/hugging-face-incident-and-the-road-ahead/
[01:22:01] AI as Normal Technology
https://www.normaltech.ai/p/ai-as-normal-technology
[01:22:51] Common Ground between AI 2027 & AI as Normal Technology
https://asteriskmag.substack.com/p/common-ground-between-ai-2027-and
person:
[00:19:43] Geoffrey Hinton
https://www.cs.toronto.edu/~hinton/
[00:20:07] Ryan Greenblatt
https://www.lesswrong.com/users/ryan_greenblatt
[00:26:06] Elon Musk
https://www.tesla.com/elon-musk
tool:
[00:21:46] ARC-AGI-3
https://arcprize.org/arc-agi/3
[00:21:53] AlphaGo and Move 37
https://deepmind.google/research/alphago/
[00:39:41] Claude
https://claude.com/product/overview
[00:39:58] NVIDIA H100 GPU
https://www.nvidia.com/en-us/data-center/h100/
paper:
[00:24:42] Training AI Scientists to Replicate Research
https://arxiv.org/abs/2608.13331v1
[01:27:19] Validity of the single processor approach to achieving large scale computing capabilities
https://www.cs.cmu.edu/~18742/papers/Amdahl1967.pdf
book:
[00:28:52] Bullshit Jobs: A Theory
https://www.simonandschuster.com/books/Bullshit-Jobs/David-Graeber/9781501143335
organization:
[01:05:09] Redwood Research
https://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 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

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RESCRIPT:
https://app.rescript.info/share/846cfee4131b664fd09209cc3b98018e