Peter Norvig, Google's head of research, and Eric Horvitz, a distinguished scientist at Microsoft Research talk AI with MIT Technology Review
Horvitz: You don't need it to be completely labeled. An area known as semi-supervised learning is showing us that even if 1 percent or less of the data is tagged, you can use that to understand the rest.
But a lack of labels is a challenge. One solution is to actually pay people a small amount to help out a system with data it can't understand, by doing microtasks like labeling images or other small things. I think using human computation to augment AI is a really rich area.
Norvig: You don't have to tell a learning system everything. There's a type of learning called reinforcement learning where you just give a reward or punishment at the end of a task. For example, you lost a game of checkers and aren't told where you went wrong and have to learn what to do to get the reward next time.
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