technology
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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/).
Required Pod Score for this podcast. PitchCentric checks your profile against host openness, topical fit, and audience signals before you generate a pitch.
Contact path
Public web form
Booking probability
35%
Guest openness
Selective
Sign up to generate a grounded pitch for Machine Learning Street Talk (MLST).
Signup to Generate a PitchWelcome! 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.
Recent episodes include "Mathematics Just Changed Forever, Kevin Hartnett" (26 October 2026, 94 minutes), "How a Voice Agent Learns the Rhythm of Conversation, Shawn Wen" (1 October 2026, 70 minutes), and "Who Checks a Proof No Human Can Read?, Leo de Moura" (30 September 2026, 74 minutes).
Machine Learning Street Talk (MLST) publishes roughly a few times a week, with episodes averaging 80 minutes across 271 episodes.
Booking probability for Machine Learning Street Talk (MLST) is 35%, and the host is rated Selective on guests. Machine Learning Street Talk (MLST) requires a Pod Score of 85 or higher. Machine Learning Street Talk (MLST)'s contact route on record: public web form.
The strongest fits for Machine Learning Street Talk (MLST) are Scientific Research & Development. Machine Learning Street Talk (MLST) has a technology audience.
Machine Learning Street Talk (MLST) is a technology podcast with 271 episodes published.
Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.
Machine Learning Street Talk (MLST)
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...
Machine Learning Street Talk (MLST)
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...
Machine Learning Street Talk (MLST)
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...
Machine Learning Street Talk (MLST)
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...
Machine Learning Street Talk (MLST)
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...
Machine Learning Street Talk (MLST)
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...
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...
Machine Learning Street Talk (MLST)
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...
Machine Learning Street Talk (MLST)
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...
Machine Learning Street Talk (MLST)
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...
Machine Learning Street Talk (MLST)
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...
Machine Learning Street Talk (MLST)
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...
Machine Learning Street Talk (MLST)
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...
Machine Learning Street Talk (MLST)
Can an AI do the right thing for the wrong reason? Tim Scarfe speaks with Apollo Research’s Alexander Meinke, Axel Højmark and Jérémy Scheurer about Measuring Reward-Seeking via Contrastive Belief Updates, their new...
Machine Learning Street Talk (MLST)
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...
Machine Learning Street Talk (MLST)
Tim Scarfe travels to Zurich to sit down with the Tufa Labs ARC-AGI-3 team — founder Benjamin Crouzier, with Jeroen Cottaar, Dries Smit, Stefano Viel and Michal Tesnar — to work out what their leaderboard-topping system...
Machine Learning Street Talk (MLST)
Thomas Ahle wants Normal Computing to be the Lovable for chip design: type your intent, and a swarm of agents carries it from design through optimisation, formalisation and verification to tape-out. To get there, his...
Machine Learning Street Talk (MLST)
This episode is sponsored by Notion. Learn more about Notion's Developer Platform today at notion.com/mlst Protein folding stalled biology for fifty years. A sequence of amino acids dictates a three-dimensional shape...
Machine Learning Street Talk (MLST)
Brad Carson was the Army's General Counsel, served two terms in Congress and was Acting Under Secretary of Defense for Personnel and Readiness. He now heads Americans for Responsible Innovation, the AI-policy advocacy...
Machine Learning Street Talk (MLST)
Michael I. Jordan, described by Science magazine as the most influential computer scientist alive, has never thought of himself as an AI researcher. In this conversation he explains why that distinction matters...
Recent guests on Machine Learning Street Talk (MLST). Study who booked and why before you pitch.
Professor of Artificial Intelligence, Universität Freiburg
former OpenAI researcher and executive director of the AI Futures Project, AI Futures Project
political journalist, Eloro Resources Ltd.
Deputy Director, Clark County Department of Job and Family Services
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