Oct 13, 2026

Robots That Predict the Future: Dyna-2 & Physical AI — Jason Ma, Dyna Robotics

Dyna Robotics is pioneering physical AI with Dyna-2, a world-action model trained on over a million hours of human video, predicting future states and generating robot actions. Co-founder Jason Ma explains how this enables robots to learn new tasks from minimal demonstrations, tackles the robotics data bottleneck with synthetic data and self-improving models, and details their full-stack approach, including custom semi-humanoid hardware for real-world deployments at Din Tai Fung. The discussion delves into diffusion transformers, agentic harnesses for hour-long workflows, the "Bitter Lesson" of scaling data, and the distinction between world-action models and VLAs, all driven by a deployment-first research culture.

Jul 17, 2026

AI Can't Learn The Way Humans Do - This Could Fix That

This discussion explores world models as a promising path to solving sample efficiency in AI and achieving AGI. It contrasts deterministic control (Newtonian physics) with stochastic environments (RL), explaining the challenges posed by large action spaces in Go, self-driving, and robotics. The episode delves into how synthetic data, video diffusion models, and latent space architectures like JEPA are making world models practical, while also highlighting remaining open problems in physics modeling, real-time adaptation, and rich sensory integration.

Apr 29, 2026

How to Build the Future: Demis Hassabis

Demis Hassabis, CEO of Google DeepMind, outlines the remaining challenges on the path to AGI, including memory, continual learning, and true reasoning. He discusses how learnings from AlphaGo are shaping agent development, the strategic importance of powerful small models like Gemma, and his vision for AI as the ultimate tool for scientific discovery, offering a framework for identifying breakthrough opportunities and advice for founders building in the age of AI.

Mar 10, 2026

10 years of AlphaGo: The turning point for AI | Thore Graepel & Pushmeet Kohli

Ten years after the historic match between AlphaGo and Lee Sedol, Google DeepMind's Thore Graepel and Pushmeet Kohli reflect on its legacy. They discuss how AlphaGo's blend of deep learning and tree search conquered the game of Go, the significance of creative breakthroughs like 'Move 37', and how these foundational concepts evolved into systems like AlphaZero, which learns without human data. The conversation bridges the gap from game-playing to solving scientific grand challenges, detailing how the same principles are now used in tools like AlphaTensor to discover novel, more efficient algorithms for fundamental problems like matrix multiplication.