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Is There a Three to Five Year Path for AIGA?
Jeff Yang: In the near term I think we're going to see more and more sample efficient reinforcement learning for two reasons. The algorithms are better optimized to learn quickly but also that you're not always starting from scratch people complain about the sample efficiency on like DOTA or Atari or chess or whatever compared to a human. He says in three to five years he'll be seeing increasingly capable sample efficient learning agents that can generalize.Yang: It's kind of a quiet revolution that is basically AI is no longer being as sample inefficient because we are starting not from scratch but starting from a good foundation so it's probably what the next 3 to 5 years look like.