Machine Learning podcasts

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

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.

Alex Smola discusses the development of voice agents, audiovisual systems and AI avatars. Topics include audio tokenization, inference cost, conversational latency, visual context and how artificial systems might learn from interaction while responding appropriately to human emotion.

Hosted by Sam Charrington

Sara Hooker of Adaptation Lab explores AI systems that change with their users, tasks and environments. The discussion connects continual learning, efficient adaptation and automated science with the difficulty of evaluating tasks that lack easily checked answers. It also covers interfaces, distillation, open models, safety and regulation, multilingual tokenization, and possible successors to current Transformer systems. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Hosted by Ravid Shwartz Ziv, Allen Roush

Jonathan Webb and ABC AI reporter Cam Wilson discuss how AI systems are changing mathematical research and the controversy around claims concerning the Navier–Stokes equations and the Millennium Prize. Mathematician Tristan Buckmaster of New York University contributes to the discussion.

Hosted by Jonathan Webb

Microsoft researcher Alexia Jolicoeur-Martineau explains the Tiny Recursive Model and the reasoning behind her work on small networks that repeatedly refine an internal state and proposed answer. The conversation examines truncated gradients, puzzle-solving benchmarks and the differences from autoregressive language generation. It also considers molecular modelling, limits of data scaling and which research directions she would prioritize. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Hosted by Ravid Shwartz Ziv, Allen Roush

Demetrios Brinkmann and Chris Benson discuss agents that operate computers and interact with software. They examine MCP, agent harnesses, interactions between agents, emerging commerce workflows and the practical challenges of using these tools in enterprise environments.

Hosted by Chris Benson

Rohan Anil of Core Automation discusses the relationship between pretraining, reinforcement learning and learning after deployment. Drawing on his optimization work at Google and Anthropic, he examines why additional post-training, distillation and longer contexts may not solve continual learning. Other topics include second-order optimizers, distributed training, low-level coding agents and the difficulty of coordinating very large GPU clusters. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Hosted by Ravid Shwartz Ziv, Allen Roush

Christopher Potts examines whether increasing token consumption translates into useful improvements in AI performance. The discussion covers evaluation beyond benchmarks, inference-time scaling, DSPy, interpretability, user expertise and architectural changes that could improve the economics of capable AI systems.

Hosted by Sam Charrington

John Langford of Microsoft AI Labs discusses whether compact implicit models of the world can improve data efficiency. The conversation examines belief-state compression, Transformer caches, JEPA-style objectives and his Next Latent research. It also covers the value of algorithmic research alongside scaling, agent-assisted experiments, open models, the history of CAPTCHA and the behavior of modern optimizers. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Hosted by Ravid Shwartz Ziv, Allen Roush

Chetan Gupta joins Daniel Whitenack and Chris Benson to discuss the importance of system architecture when moving AI into enterprise use. The conversation connects industrial and physical AI with deployment, governance and sovereignty, considering how organizations can make practical design choices.

Hosted by Daniel Whitenack, Chris Benson

INRIA researcher David Holzmüller explains how to choose among tabular foundation models, boosted trees and multilayer perceptrons. He discusses TabArena, TabICL and RealMLP, benchmark weaknesses, small-data performance, calibration and class imbalance. The conversation also examines text columns, explainability, time-series distinctions, synthetic pretraining and the trade-offs among computation, inference speed and predictive performance. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Hosted by Ravid Shwartz Ziv, Allen Roush

Justin Johnson joins Sam Charrington to discuss world models and spatial artificial intelligence. Topics include representations of three-dimensional environments, simulation and the role of such models in enabling systems to reason about and act within the physical world.

Hosted by Sam Charrington

Angie Jones discusses the shared infrastructure needed for practical AI agents, including interoperability and open standards. The conversation covers MCP, agent-to-agent communication and Goose, asking how common foundations can make tools easier to connect and reduce fragmentation as organizations adopt agent systems.

Hosted by Chris Benson

Max Welling discusses how ideas from physics may inform the next generation of artificial intelligence. The conversation examines physical structure, generative modelling and the relationship between learning systems and scientific discovery.

Hosted by Sam Charrington

University of Freiburg professor and Prior Labs co-founder Frank Hutter explains how TabPFN changed deep learning for tabular data. The episode examines in-context learning from synthetic pretraining, forecasting, architectural approaches to larger tables and the continuing roles of boosted trees and language models. Hutter also discusses moving research into a company, team autonomy, model licensing and building research organizations outside established technology hubs. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Hosted by Ravid Shwartz Ziv, Allen Roush

Google DeepMind research director Brendan O'Donoghue examines discrete diffusion as an alternative to autoregressive text generation. He discusses sample diversity, reinforcement learning, latency and potential on-device or robotics applications. The episode also addresses the costs of serving diffusion models, hardware trade-offs between computation and memory bandwidth, and practical limits on progress toward more capable AI. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Hosted by Ravid Shwartz Ziv, Allen Roush

Fatih Porikli examines why scaling image models alone does not guarantee visually correct results. He discusses separating scene planning from rendering, improving controllability and editing, and bringing high-resolution generation to edge devices, including the role of training objectives and reinforcement learning.

Hosted by Sam Charrington

Nathan Lambert discusses post-training and the prospects for open AI models, drawing on his work on OLMo at Ai2 and his writing on reinforcement learning from human feedback. Topics include capability gaps, the economics of the open ecosystem, training environments, continual learning and skepticism about recursive self-improvement. The conversation also examines research culture, concentrated talent and whether increased model spending translates into better products. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Hosted by Ravid Shwartz Ziv, Allen Roush