Podcast
Machine Learning Street Talk (MLST)
Technical conversations on machine learning, AI research, cognition, and the philosophy of intelligence.
Original source
AI Is Learning at the Wrong Level of Abstraction — Matthieu Wyart
Matthieu Wyart argues that deep networks learn abstractions by discovering hidden hierarchies in data, which can make learning polynomial in effective dimension rather than impossible in raw input space. He also makes a case for predicting latent representations instead of tokens, claiming it is more sample-efficient and may better support future machine creativity and scientific reasoning.
How Researchers Test AI for Hidden Goals — Apollo Research
Apollo Research walks through a new way to measure whether frontier models are reward-seeking by changing what they believe graders reward and then observing how their behavior shifts. The episode uses OpenAI checkpoint data, synthetic document fine-tuning, and contrastive belief updates to argue that reward-seeking and scheming are distinct, measurable failure modes that may grow with scale.
Why a Nation Can't Outsource Its Frontier AI - Alistair Pullen (Cosine AI)
Alistair Pullen says Cosine is building a UK sovereign frontier model because export controls and compute constraints forced the company to pursue its own stack. He argues the real bottlenecks are inference economics, active parameters, trajectory data, and better RL reward shaping for coding agents.
The Benchmark With No Instructions — ARC-AGI-3 (winning team!)
The episode dissects ARC-AGI-3 as a benchmark for interactive goal inference, action efficiency, and abstraction under tight constraints. The Tufa Labs team explains how its winning system evolved from brute-force search toward language-mediated, harnessed reasoning, while arguing that the benchmark tests performance, not true competence.
The Thermodynamic AI Computing Chip - Thomas Ahle
Thomas Ahle argues that hardware design is becoming an agentic, AI-assisted workflow from intent to tape-out, but correctness and verification remain the hard bottlenecks. The episode also explores thermodynamic computing, where chip noise is harnessed as computation, and the limits of LLMs for formal proof, benchmarking, and engineering trust.
He won a Nobel here for AlphaFold. Then he left. - John Jumper
John Jumper explains how AlphaFold turned protein structure prediction from a slow, expensive experimental bottleneck into a fast, highly accurate computational tool, while stressing that it is still a narrow predictor rather than a model of the cell. The episode also covers AlphaFold2’s architecture, AlphaFold3’s move to biomolecular interactions, and how these tools are changing structural biology globally, including in Africa.
When AI Decides You're a Threat — Brad Carson
Brad Carson argues frontier AI should be regulated like a high-risk product, with mandatory testing, transparency, and liability rather than personhood or broad First Amendment protection. The conversation also digs into autonomous weapons, chip chokepoints, U.S.-China dialogue, and why public distrust may become AI's biggest political risk.
Intelligence is collective, not artificial — Prof. Michael I. Jordan (UC Berkeley / Inria)
Michael I. Jordan argues that intelligence should be treated as a collective economic system rather than an isolated artificial mind, and that AGI talk is mostly misleading branding. He emphasizes machine learning as a practical engineering discipline, then extends the same systems-and-incentives lens to data markets, drug discovery, creator monetization, uncertainty quantification, and human-in-the-loop automation.