Machine Learning Street Talk
A highly technical podcast exploring advanced AI research, from LLMs to cognitive science, with the researchers building the field.
This is a documentary-style production, not a simple interview show. The host, Tim Scarfe, narrates a story, providing extensive technical and historical context before weaving in interview clips with world-class experts. The show is defined by its intellectual skepticism, consistently challenging the reliability of new models, the economics of the tech industry, and the legal and ethical frameworks surrounding AI. It deliberately covers a broad and diverse set of ideas, including less-hyped but important areas like formal verification, thermodynamic computing, and symbolic AI.
“MLST's documentary format, where interview clips are used to illustrate a larger narrative, is unique in the space. Its commitment to intellectual diversity and skepticism, covering topics from chip design economics to the philosophy of mind, sets it apart from podcasts focused solely on mainstream deep learning.”
Who hosts this show
Machine Learning Street Talk (MLST) is a leading technical AI podcast hosted by Dr. Tim Scarfe, with Dr. Keith Duggar as a regular co-host. The show features in-depth, documentary-style episodes that go beyond mainstream hype to explore a wide range of topics including advanced AI research, cognitive science, neuroscience, and philosophy of mind. MLST is known for its intellectual rigor and for featuring discussions with preeminent, world-class experts in the field.
Credentials & credits
- Dr. Tim Scarfe: Ph.D. in Machine Learning, former Principal Engineer at Microsoft, former Chief Data Scientist at bp.
- Dr. Keith Duggar: Ph.D. from MIT, former researcher at IBM Research, experience on Wall Street and at Microsoft.
Other ventures
- Dr. Tim Scarfe: Founder of several tech startups, including Dot Net Solutions and an augmented reality startup.
- Dr. Keith Duggar: Co-founder of MLST, CTO at XRAI Glass, Data Strategy Consultant.
- MLST Substack publication.
What kind of podcast
- Country
- United Kingdom
- Region
- uk
When new episodes drop
- 01The Thermodynamic AI Chip · Thomas AhleJun 28, 2026 · 1h 03m
- 02He won a Nobel here for AlphaFold. Then he left. - John JumperJun 22, 2026 · 53 min
- 03The Ex-Pentagon Chief Sounding the Alarm on AI Weapons — Brad CarsonMay 31, 2026 · 1h 21m
- 04
- 05The AI Progress Chart Everyone Is Misreading — Beth Barnes & David ReinMay 4, 2026 · 1h 53m
- 06When AI Discovers the Next Transformer — Robert LangeMar 13, 2026 · 1h 18m
- 07The Dangerous Illusion of AI Coding? - Jeremy HowardMar 3, 2026 · 1h 27m
- 08
Notable episodes
- 01He won a Nobel here for AlphaFold. Then he left. - John Jumper
A documentary on the creation of AlphaFold featuring its Nobel-winning creator, who discusses the breakthrough and his subsequent move from DeepMind to Anthropic.
- 02The Ex-Pentagon Chief Sounding the Alarm on AI Weapons — Brad Carson
Explores the complex legal, political, and ethical issues of AI in warfare with a former high-level government official, demonstrating the show's breadth beyond pure technology.
- 03The Thermodynamic AI Chip · Thomas Ahle
A deep dive into the intersection of AI and hardware, covering the economics of chip design, formal verification, and novel computing paradigms like thermodynamic computing.
What you'll be asked on this show
Tim Scarfe's interview style is more akin to a journalist building a documentary than a conversational talk show host. He often opens by setting a large conceptual frame, using historical context or a philosophical question. He probes by asking for quantification ('Do simulators really cost $10,000?'), challenging the reliability of claims ('could it be deceptive?'), and exploring legal or systemic consequences ('How is AI different in a tort analysis?'). Rather than rapid-fire questions, he allows guests long, narrative answers and uses his own narration to connect disparate clips into a cohesive argument.
The primary host, Tim Scarfe, employs a narrative, documentary style, often explaining complex concepts himself and using guest clips as supporting evidence. He frequently asks clarifying questions to break down jargon for the audience and uses rhetorical questions to frame topics. Co-host Keith Duggar often plays the role of a friendly adversary, pushing back on guests and probing for weaknesses in their arguments.
Questions the host keeps coming back to
7 cataloguedIf you're going on this show as a guest, expect some version of each of these. Each note explains when the host reaches for it.
process
3- Q.01
“So, is this concept basically like [simpler analogy] for the audience?”
Asks for confirmation of a simplified definition to ensure the audience can follow a complex technical point.
- Q.02
“What actually happens, legally, to someone who posts a deepfake?”
Follows up a philosophical question about blame with a practical probe into the current state of legal enforcement.
- Q.03
“How is AI different from other technologies within a traditional legal framework like tort analysis?”
Asks the guest to evaluate whether existing legal or social structures are sufficient for new AI capabilities.
money
1- Q.01
“Do these commercial tools really cost something like [very high number]?”
Used to quantify the financial or economic barriers within a specific technical domain, grounding abstract costs in concrete numbers.
controversy
2- Q.01
“When a new model seems to perform better, could that be deceptive?”
Introduces skepticism to challenge the surface-level performance of AI systems and probe for hidden failures.
- Q.02
“When it comes to AI harms like deepfakes, should we blame the tool or the person misusing it?”
Pivots a technical discussion toward legal and ethical liability, exploring where responsibility should lie.
future
1- Q.01
“Given the opacity of these systems, what do you see as the solution for their use in warfare?”
Presses the guest for forward-looking policy or technical solutions to a complex, high-stakes problem.
Topics covered repeatedly
Who gets booked here
MLST books world-class experts at the absolute peak of their fields, including Nobel laureates, heads of major research labs, influential academics, and former high-ranking government officials. The show explicitly states it does not work with booking agents, indicating a preference for guests who are directly and deeply involved in their work.
- Dr. Thomas Ahleon The Thermodynamic AI Chip · Thomas Ahle
- Dr. John Jumperon He won a Nobel here for AlphaFold. Then he left. - John Jumper
- Brad Carsonon The Ex-Pentagon Chief Sounding the Alarm on AI Weapons — Brad Carson
- Prof. Michael I. Jordanon Intelligence is collective, not artificial — Prof. Michael I. Jordan (UC Berkeley / Inria)
- Beth Barneson The AI Progress Chart Everyone Is Misreading — Beth Barnes & David Rein
- David Reinon The AI Progress Chart Everyone Is Misreading — Beth Barnes & David Rein
Where to find this show
Audience & reach
Sponsors are high-end, technically-focused companies like Notion, Cyber Fund, and Prolific, targeting an audience of sophisticated engineers, researchers, and founders. The show also has a Patreon for direct listener support and has disclosed production cost sponsorships, suggesting a flexible approach to funding that aligns with its high-production-value, in-depth content.
Subscriber and view counts are pulled live from YouTube and re-verified on a 30-day cycle. Listener estimates for the RSS feed aren't published here unless they're host-verified.
Pitch this show
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People also ask
- Who are the hosts of Machine Learning Street Talk?
- The primary host and creator is Dr. Tim Scarfe, who has a Ph.D. in Machine Learning. Dr. Keith Duggar, who holds a Ph.D. from MIT, is a regular co-host and interviewer.
- What is the format of the show?
- MLST uses a documentary-style format. Episodes are typically narrated by the host, who provides context and analysis, weaving in clips from interviews with expert guests.
- Is the podcast still running?
- Yes, the podcast is active and typically releases new episodes on a weekly basis.
- Who is the intended audience for MLST?
- The show is aimed at a highly technical audience of engineers, research scientists, and others with a strong interest in the deep, technical details of AI and related fields.
- How can I support the podcast?
- You can support the show through their Patreon, which offers early access and exclusive content, or by donating via PayPal. The links are in the show's description.
- Does the show only cover Large Language Models (LLMs)?
- No, the show prides itself on its intellectual diversity, covering a wide range of topics including CogSci, Neuroscience, Mathematics, Philosophy, and alternative paths to AGI beyond just LLMs.
Built from the show's public RSS feed, YouTube, the host's own websites, and the cited sources below. Computed and AI-extracted fields are labelled. Facts only — no private info, no fabrication, no transcripts republished.
Sources & how this page was built
This page is AI-assisted, grounded in the public sources cited below, and host-verifiable. We publish facts only; we do not republish transcripts. If anything here is wrong, the host can claim and correct the page above.Model: gemini-2.5-pro · high confidence
- [01]About - Machine Learning Street Talk (MLST)mlst.ai
- [02]Dr. Keith Duggar - Chief Technology Officer at XRAI Glass | The Orgtheorg.com
- [03]Machine Learning Street Talk - YouTubeyoutube.com
- [04]Tim Scarfe of Machine Learning Street Talk on Agency and the Future of AI - YouTubeyoutube.com
- [05]Machine Learning Street Talk (MLST) - Apple Podcastspodcasts.apple.com
- [06]Dr. Keith Duggar (Machine Learning Street Talk) vs. Liron Shapira — AI Doom Debateyoutube.com
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