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?.

It goes beyond the title with direct discussion of these, data, models, including: That is to say, how much data do you need in a given domain to operate fluently and competently?.

1. 01And then you train your model to predict these correct rollouts, much in the same way that you might train that model to predict the next word in internet text
2. 02For this process to work, the model must have at least some prior probability to anticipate the correct solution in the first place, which is why you need mind-stretching amounts o
3. 03Even the task count difference here understates the gap because the models have to grind their far more numerous tasks, each far harder