The TWIML AI Podcast

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

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

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

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

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

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

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

Damian Borth describes learning from trained neural networks themselves as a new source of training data. The conversation explores representations of model weights, transferring knowledge across architectures and tasks, and the possibility of specializing models with less computation by reusing what existing networks have learned.

Hosted by Sam Charrington

Alex Wiltschko explains efforts to predict smell from molecular structure and to give computers a useful representation of odors. He discusses graph neural networks, the difficulty of collecting olfactory data, and how learned embeddings connect chemistry to perception and potential practical applications.

Hosted by Sam Charrington

Devvret Rishi discusses how agents with access to tools and external systems change the security requirements of generative AI applications. He examines the limitations of static controls and the need for runtime policies, observability and recovery mechanisms as agent workflows become more capable.

Hosted by Sam Charrington

Alex Bowcut draws lessons from building an AI system for tax-law research. He explains why retrieval still matters with long-context models, how chunking and combined retrieval methods affect results, and why reliable citations and feedback from domain experts are central to evaluating a specialized application.

Hosted by Sam Charrington