Machine Learning Street Talk (MLST)

In-depth discussions on AI, cognitive science, neuroscience and philosophy AI summary

Machine Learning Street Talk (MLST) Visit website › Technology · EN · 273 episodes · latest 2026-11-02

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What it is 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/).

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Machine Learning Street Talk (MLST)

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Latest episodes

Why Leibniz Couldn't Find the Mind Inside the Mill (Joscha Bach)

2026-11-02 · 1 hr 37 min

Joscha Bach is a cognitive scientist and the founder of the California Institute for Machine Consciousness.SPONSOR:---Parallel is a single platform for all your agentic research: web search built for AI agents, with…
Mathematics Just Changed Forever — Kevin Hartnett

2026-10-26 · 1 hr 34 min

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:…
What Most People Get Wrong About Evolution | Akarsh Kumar

2026-10-10 · 48 min

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

2026-10-01 · 1 hr 10 min

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

2026-09-30 · 1 hr 14 min

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…
When AI Research Starts Moving Faster Than Human Research - Zhengyao Jiang

2026-09-26 · 44 min

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…
How Deep Learning Finally Cracked Messy Tables - Frank Hutter

2026-09-23 · 1 hr 53 min

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.TabPFN is pre-trained on synthetic datasets drawn from a…
Why Scaling Prediction Cannot Create Intelligence - Alexander Mattick

2026-09-21 · 2 hr 14 min

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

2026-09-15 · 26 min

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…
Speech Recognition Is Not a Solved Problem — Pavan Kumar Reddy

2026-09-14 · 1 hr 42 min

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…

Episode details from the publisher’s feed. Playback happens at the publisher.

Publisher details

Oldest episode in feed: 04/2020 · over 6 years
Typical length: 1 hr 32 min
Format: Episodic Newest first, episodes stand alone

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