Why Real-World Robotics Data Matters

In this episode, we discuss various topics in AI, including the challenges of the conference review process, the capabilities of Kimi K2 thinking, the advancements in TPU technology, the significance of real-world data in robotics, and recent innovations in AI research. We also talk about the cool "Chain of Thought Hijacking" paper, how to use simple ideas to scale RL, and the implications of the Cosmos project, which aims to enable autonomous scientific discovery through AI.

Papers and links:

• Chain-of-Thought Hijacking - https://arxiv.org/pdf/2510.26418
• Kosmos: An AI Scientist for Autonomous Discovery - https://t.co/9pCr6AUXAe
• JustRL: Scaling a 1.5B LLM with a Simple RL Recipe - https://relieved-cafe-fe1.notion.site/JustRL-Scaling-a-1-5B-LLM-with-a-Simple-RL-Recipe-24f6198b0b6b80e48e74f519bfdaf0a8

Chapters

00:00 Navigating the Peer Review Process

04:17 Kimi K2 Thinking: A New Era in AI

12:27 The Future of Tool Calls in AI

17:12 Exploring Google's New TPUs

22:04 The Importance of Real-World Data in Robotics

28:10 World Models: The Next Frontier in AI

31:36 Nvidia's Dominance in AI Partnerships

32:08 Exploring Recent AI Research Papers

37:46 Chain of Thought Hijacking: A New Threat

43:05 Simplifying Reinforcement Learning Training

54:03 Cosmos: AI for Autonomous Scientific Discovery

Music:

"Kid Kodi" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

"Palms Down" — Blue Dot Sessions — via Free Music Archive — CC BY-NC 4.0.

Changes: trimmed