Computer Science podcasts

Sean Carroll talks with Caltech researcher Erik Winfree about programming DNA to process information and build microscopic machines. They discuss molecular computation, self-assembly, biological information processing and the prospects for DNA-based robots in biology and medicine.

Hosted by Sean Carroll

Spencer McIntyre of VulnCheck joins Daniel Whitenack and Chris Benson to discuss AI-assisted vulnerability research and exploit development. The episode examines why many proof-of-concept exploits are unreliable, how initial-access intelligence helps security teams prioritise threats, and where agents can assist research. The conversation also considers the limitations of automated exploitation, the effects of AI guardrails on legitimate security work, and implications for threat intelligence.

Hosted by Daniel Whitenack, Chris Benson

Ahmad Al-Dahle, Airbnb’s CTO, discusses Project Everest, an effort to capture institutional knowledge from employee workflows for use by AI agents. The conversation examines stale information, context poisoning and the costs of supplying excessive context, alongside evaluation of whether shared knowledge improves engineering work. It also considers productivity measures beyond token consumption and pull-request counts, limitations of chat interfaces, and the challenges of support agents mediating conflicting interests in a marketplace.

Daniel Litt discusses how he uses AI in mathematical research and why he changed his forecast about automated results. The conversation distinguishes technical proof work and literature coverage from choosing worthwhile questions and developing mathematical understanding. It examines checking machine-generated proofs, the guarantees and limits of Lean formalization, repeated discoveries by different models, and how research assessment might change. Further topics include publication incentives, competition between AI laboratories, teaching mathematics and questions about computational complexity. Statements about the future of mathematics are presented as the guest’s views.

Hosted by Ravid Shwartz Ziv, Allen Roush

Andy Hall of Stanford and Anthropic discusses how advanced AI could change work, wealth redistribution, public trust and democratic decision-making. The conversation considers open models, collective safety, legislative assistance, ideological bias, model governance and international competition. It distinguishes competing expectations about AI’s economic and political effects and examines the practical challenges of governing widely available agents.

Ignacio Cirac discusses quantum information and the path from theoretical ideas to useful quantum technologies. Topics include trapped-ion qubits, quantum simulation, Rydberg-atom approaches and the choices facing hardware developers. The conversation also considers expectations, industrial development and the European quantum research community.

Zoe Weil discusses how recommendation and ranking systems can use in-session behavioral signals to model intent and optimize purchasing outcomes. The episode examines event-to-feature models, low-latency ranking infrastructure, evaluation against business outcomes, and the distinction between click relevance and useful recommendations. It also considers privacy tradeoffs in personalization and the relationship between advertising and organic revenue.

Hosted by Sam Charrington

Rafael Rosengarten talks with NVIDIA’s Stacie Calad-Thomson about connecting AI-driven drug discovery to physical laboratory work. They examine why consistent experimental data is essential, how agents and robotics could coordinate experiments, and how scientists’ responsibilities may change as laboratories automate. The discussion distinguishes proposing molecules or experiments from executing them, validating results and deciding the next research step.

Hosted by Rafael Rosengarten

John Platt discusses how Google’s applied-science team chooses problems where AI can make a useful contribution. Topics include satellite-based detection and prediction of aircraft contrails, evaluation of rerouting trials, limitations of physical simulation, and the role of AI agents in scientific modelling and data work. The conversation also examines overfitting, action incentives, gradient-free optimisation, SMO and support-vector machines, the physical constraints on scaling, and opportunities for robotics and manufacturing to support climate technology. It concludes with FireSat’s approach to detecting small wildfires from orbit and the practical issues of coverage, cost and false positives.

Hosted by Ravid Shwartz Ziv, Allen Roush

Maxim Raginsky joins Paul Middlebrooks to examine brains, AI and biological autonomy through control theory. They discuss what behaviour reveals about a system's internal organisation, analog and digital descriptions, the relationship between control and cybernetics, active inference and the limits of computational accounts. The publisher offers a public episode and supporter access to its full archive.

Hosted by Paul Middlebrooks

Diogo Almeida joins Sam Charrington to discuss TypeSafe's Jev model and the use of calibrated decisions as a component of AI software. The conversation examines reinforcement learning from calibrated decisions, differences from classifiers and text-generating models, and the implications for agents, tool use and system architecture.

Hosted by Sam Charrington

Anna Truzzi and Luca Pellegrino discuss experiments on a historical antibacterial preparation and the sources of bias in AI systems. An external segment with Ian Scott and Ilaria Zanardi explores elephant communication, including low-frequency sound, long-distance signalling and the evidence for individually distinctive calls.

Hosted by Anna Truzzi, Luca Pellegrino

Yuandong Tian discusses his research on computer Go, from DarkForest to OpenGo, and the role of action-space design in applying reinforcement learning. The conversation covers gradient-free optimization, neural architecture search, Coconut’s approach to reasoning in latent space, representation learning and grokking. It then examines AI-assisted research, recursive self-improvement, coding-agent limitations, and whether alternative architectures can outperform transformers. The closing discussion considers data efficiency, robotics, restrictions on self-improving systems, and open-source models.

Hosted by Ravid Shwartz Ziv, Allen Roush

Ming-Yu Liu, NVIDIA vice president of research and head of the Cosmos Lab, joins Practical AI hosts Daniel Whitenack and Chris Benson to discuss AI systems that interact with the physical world. The conversation examines open models, world models and simulation as foundations for robotics and autonomous vehicles, and how research and deployment can advance physical AI. Published 1 October 2026; publisher runtime 47 minutes 19 seconds.

Hosted by Daniel Whitenack, Chris Benson

Andrew Glester explores how drones, robotics and machine learning support wildlife research and conservation. Blair Costelloe discusses the WildDrone and WildBotics networks and the Mara Elephant Project; Camille Rondeau Saint-Jean describes real-time animal monitoring; William Njoroge explains how Ol Pejeta Conservancy combines drones with established conservation methods. The episode considers population data, field constraints, regulation and collaboration across disciplines.

Hosted by Andrew Glester

Sam Charrington speaks with Greg Burnham, who leads AI capabilities research at Epoch AI, about the progression from elementary mathematical tasks to difficult research problems, including Navier–Stokes. They examine how advanced systems solve mathematical problems, the roles of persistence and existing human work, and the evidence for new ideas. The conversation also considers how to measure progress when traditional benchmarks become less informative, why improvements appear steady across successive model generations, and the remaining weaknesses in open-ended research, learning from experience and choosing productive scientific questions.

Hosted by Sam Charrington

Stanford linguist and computer scientist Chris Manning discusses what linguistics contributed to machine learning and why pragmatics and dialogue remain open problems for language models. The conversation examines early abstraction of verb categories in small transformers, how distributed representations shape learning, and whether language alone can support meaningful representations. It also considers language in world models, diffusion language models, and representation finetuning (ReFT), which steers frozen models through their hidden states. The final discussion asks where knowledge resides and whether concepts occupy linear subspaces.

Hosted by Ravid Shwartz Ziv, Allen Roush

Nick Kuhn joins Daniel Whitenack and Chris Benson to discuss deploying AI agents alongside conventional enterprise applications. Topics include agent build packs, MCP gateways, shared memory, identity, sandboxing and lessons from platform engineering.

Hosted by Daniel Whitenack, Chris Benson

Andrew Dai joins The Information Bottleneck to discuss language-model pre-training, next-token prediction, training-data quality and visual reasoning. The conversation examines counting and image understanding, world models and JEPA. Hosted by Ravid Shwartz-Ziv and Allen Roush.

Gaute Einevoll and Mihai Petrovici discuss how neuromorphic hardware can support artificial intelligence and efficient brain simulation. They contrast high-frequency arithmetic on conventional silicon with the rich dynamics and millisecond spikes of biological neurons, and explore brain-inspired systems for emulating neural dynamics and studying computation in spiking networks. Petrovici is a senior researcher at the University of Bern. The conversation was recorded on 18 June 2026 and released on 23 September 2026; the publisher’s audio runs 1 hour, 41 minutes and 46 seconds.

Hosted by Gaute Einevoll