The data black hole at the center of AI

So one definition of intelligence is sample efficiency. That is to say how much data do you need in a given domain to operate fluently and competently. And it's actually not clear that we've made that much progress in training sample efficiency over the last few years. It seems like more so we've just dramatically widened and improved the data distribution. The main way that AIs have been getting better is from adding more and better data and scaling the compute required to develop that data in the first place. Obviously, RL is the main way that this has happened. You can think of RL as basica