anand iyer

@hausman_k is the co-founder and CEO of @physical_int, a robotics company building a general-purpose “AI brain for the physical world.” The company has raised more than $1 billion in funding to develop foundation models that allow robots to operate across many machines, environments, and tasks rather than being programmed for a single purpose. In our conversation, we explore: • The moment a lecture from Sergey Levine convinced him to abandon his PhD research direction and pivot fully to deep learning • The case for building a general “AI brain” for the physical world rather than a single specialized robot • The role of real-world data in training robots, the limits of simulation, and how deployment could create a powerful data flywheel • The unique challenges of physical intelligence and why robots must operate with far higher reliability than language models Thank you to the partners who make this possible - @brexHQ: The intelligent finance platform: brex.com/mario - @meetgranola: The app that might actually make you love meetings: granola.ai/mario Timestamps (00:00) Intro (04:05) Karol’s early fascination with robots (18:21) Karol’s entry point to robotics and PhD program (25:49) Combining robotics with LLMs: The Taylor Swift demo (30:48) The 1970s SHRDLU AI experiment (39:40) How research shapes what Physical Intelligence builds (49:07) The return of reinforcement learning in robotics (1:00:00) NVIDIA’s simulation engines (1:07:31) Compensating for missing senses

Companies that now regularly use artificial intelligence are starting to track their workers’ use of tokens, AI’s unit of measurement on.wsj.com/473nAnZ

Agents are the future. Inspired by autoresearch and arbos we released our deepfake research training toolkit: DFResearch: github.com/BitMind-AI/dfr… Experiment autonomously to train the best deepfake detection models. Integrated to download data, output submission ready-results, and full guide to adding custom models, datasets.

Went to @DvijKalaria's lab @berkeley_ai and played ping pong against his robot, Oreo. I'd played a ton of ping pong as a kid. This felt appropriately surreal and one of those "I wish I could tell my highschool self about this" moments. Table tennis is one of the harder sports for robots to play. The ball can move up to 30+ mph with heavy spin, the human opponent's intent is hidden, and the whole body has to coordinate. Oreo is a full humanoid holding a real paddle, and it learned key motions like swings by watching Dvij demonstrate. No robot-collected training data. One person shows the motion, the policy generalizes. The way it works, as I understood it: - A smart system (a hierarchical planner) first figures out where the ball is going to fly and picks the best type of hit, like a forehand or backhand swing. - This plan then helps train the robot's "brain" (an RL policy) in a virtual simulation. The brain learns by trial and error, getting rewards when it mimics a few example moves - Once trained in the sim, the whole setup gets applied to the actual physical robot so it can play for real. The human demonstrations are essentially the reference motions. They are building a robot that has watched more human table tennis than any human has, and uses that to develop its own game. I still won. (Barely. But that won't last)

Meet the DEEP Robotics Year of the Horse Special Edition robot — a playful fusion of robotics, culture, and imagination. #Robotics #EmbodiedAI #DEEPRobotics #PhysicalAI #Robothorse