Saltar al contenido

Ep 87: Gemini Co-Lead on World Models, RL's Next Domains & Continual Learning

22 de mayo de 2026 · 60 min

Notas del episodio

Oriol Vinyals, VP of Research at Google DeepMind and co-lead of the Gemini program, joins Jacob the day after Google I/O to unpack the research underpinning Google's latest announcements and where frontier AI is heading. The conversation moves from world models (why Google has uniquely bet on them as a path to AGI, what the "GPT moment" for video and images would look like, and how they connect to robotics and simulation) to agents (the Spark release, why the system and model need to be optimized jointly, and why scaffolding will eventually be written by models themselves). Oriol gets into the mechanics of memory in models, drawing on his cognitive neuroscience background to argue that file-system-style non-parametric memory is more practical than baking memory into weights at serving scale. He shares his views on the limits of RL today (LLMs are data-limited in a way that game-playing RL never was), why training on narrow domains like math and code generalizes surprisingly well, and what a true "Move 37" moment for science or ML research would look like. Throughout, he reflects on the unique advantages of being inside Google (TPU co-design, end-to-end revenue stability, the merger of Brain and DeepMind), the trade-offs between focus and exploration in research orgs, and why he believes AGI in some meaningful sense may already be here, even if the goalposts keep moving.

(0:00) Intro

(1:36) Why World Models

(4:21) The GPT Moment for Video

(7:51) What Makes Omni a World Model

(10:04) World Models & Robotics

(12:37) Evaluating Physics in AI

(14:51) Consumer Agents & Spark

(18:39) Scaffolding & the Bitter Lesson

(22:06) Memory & Continual Learning

(26:54) Research Bets Inside Big Labs

(32:30) Post-Training RL is Greenfield

(35:57) What Real Intelligence Looks Like

(39:11) RL Generalization

(43:00) Advice for Founders

(46:40) Can AI Truly Innovate?

(49:48) Recursive Self-Improvement

(52:14) Quickfire

Jacob is an AI investor at Redpoint Ventures. He's led Redpoint's investments in companies like Abridge, Physical Intelligence & Legora. Follow Jacob on Twitter (@jacobeffron).

On Unsupervised Learning we probe the sharpest minds in AI in search for the truth about what's real today, what will be real in the future and what it all means for businesses and the world. If you're a builder, researcher or investor navigating the AI world, this podcast will help you deconstruct and understand the most important breakthroughs and see a clearer picture of reality. Subscribe to this show to stay up to date on our latest episodes.

Todos los episodios de Unsupervised Learning with Jacob Effron

Ajustes

Visualización

Idioma

Determina qué emisoras ves primero en la página de inicio.

Historial

Las emisoras y episodios escuchados recientemente y tus últimas búsquedas solo se guardan en este navegador.

Avisos

Un breve aviso cuando un pódcast que sigues tiene un episodio nuevo. Puedes desactivar pódcast concretos con la campana de su página.

Cuenta

Guarda tus favoritos: en la app y en la web.

Atajos de teclado

/
Abrir búsqueda
Espacio
Reproducir o pausar
← →
En el reproductor: 15 s atrás, 30 s adelante (episodios) · en una fila: siguiente tarjeta
↑ ↓
En una fila: destino dentro de la tarjeta
Inicio / Fin
En una fila: primera o última tarjeta
Esc
Cerrar ventana o menú
?
Este resumen
Reproduciendo ahora

Ir a la emisora

A continuación

    Añade episodios con «A continuación»: sonarán después del episodio actual.