Dwarkesh Patel: evaluation

Transcript context

…Well, we’ve been working on humanoid robots now for a while. I guess it’s been five or six years or something. A bunch of the things that were done for the car are applicable to the robot. We’ll use the same Tesla AI chips in the robot as in the car. We’ll use the same basic principles. It’s very much the same AI. You’ve got many more degrees of freedom for a robot than you do for a car. If you just think of it as a bitstream, AI is mostly compression and correlation of two bitstreams. For video, you’ve got to do a tremendous amount of compression and you’ve got to do the compression just right. You’ve got to ignore the things that don’t matter. You don’t care about the details of the leaves on the tree on the side of the road, but you care a lot about the road signs and the traffic lights, the pedestrians, and even whether someone in another car is looking at you or not looking at you. Some of these details matter a lot. The car is going to turn that one and a half gigabytes a second ultimately into two kilobytes a second of control outputs. So you’ve got many stages of compression. You’ve got to get all those stages right and then correlate those to the correct control outputs. The robot has to do essentially the same thing. This is what happens with humans. We really are photons in, controls out. That is the vast majority of your life: vision, photons in, and then motor controls out. Naively, it seems that between humanoid robots and cars… The fundamental actuators in a car are how you turn, how you accelerate. In a robot, especially with maneuverable arms, there’s dozens and dozens of these degrees of freedom. Then especially with Tesla, you had this advantage of millions and millions of hours of human demo data collected from the car being out there. You can’t equivalently deploy Optimuses that don’t work and then get the data that way. So between the increased degrees of freedom and the far sparser data... Yes. That’s a good point.…

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