Comment by tschellenbach
6 years ago
On the other hand at some point we will want AI to learn based on a small number of interactions. IE an AI that beats a human after playing 10 games of chess/starcraft etc. Right now it takes millions of training matches. Many real world situations don't happen that often so this fundamentally limits applications of the current generation of AI.
Humans require few or many examples depending on the situation. For example my toddler got some candy from the hospital gift shop six months ago. Walking past the same place today he took off on his own accord and went directly to the candy. A single example was enough to train his candy finding algorithm. On the other hand he has had hundreds of examples of putting on his shoes and still can not manage this on his own.
Show me a human that can win a Starcraft championship after only playing ten games. If you find any, they learned the mechanics and strategy somewhere else. That’s transfer learning, appears to be in its infancy in the ML community but making progress.
The scale matters here. I think a better metric for your parent comment would be the delta in skill per game played.
A human is significantly better on game 11 than game 1 (I recently got into Starcraft). Current ML systems are not. It's up for discussion how to take the human's previous experience into account, but the total amount of experience is significantly less that the computer's.
I guess, then, the next step in AI research should be to develop a deep learning network for automatic training examples generation ~~ An AI Machine Learning Trainer of some kind.