MORAVEC'S PARADOX AND THE ROBOT OLYMPICS

Summary from the TLDR newsletter

Many of the things we struggle to program into computers can be learned from data, but only when that data is available. If we can't learn from data, we are forced to program it in at lower performance. Computers can learn any skill, but only with enough data, and we don't want to have to need huge amounts of data for every single task. As models get better, they will need less data to learn new tasks, and when this happens, we'll finally be able to build truly general methods that combine physical understanding and cognition.