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Babies take a while to learn how to grasp things, it's believable that the number of attempts they make is in the 10,000s. 1 grasp attempt a minute, 8 hours a day for 3 months is 43,000 attempts.

Of course they are learning a lot of other stuff at the same time so it's not really comparable.

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This is something that I think people often miss when they compare machine learning to human performance. Humans spend a LOT of time in their early learning and calibrating phase. Like, it's our full time job, 365 days a year, for several years. One interaction every ~5sec, for 12 hours a day, seems a modest estimate. That's over 3 million training examples per year.

Well, it's a hard problem. Looking at the data, could you come up with a way to learn control more quickly?

Ie, given a series of pixel intensities, could you send commands to each motor to get them to grasp something?

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