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Comment by bwang29 | Hacker News Reader

I'm not an expert on generalized A.I. training but it seems like the problem of training with audio and visual data input from games suffer the exact scale problem with Alpha Go at specific tasks. I could imagine training an A.I. to play a complex RPG game like Witcher, you would first probably need to train on a horse riding game, a running game, a weather reaction game, a free fighting game, a trading game and maybe a couple hundreds of thousands of games, each for a couple million times of trial and error? However, it also seems that human doesn't need to take this amount of reinforcement training data to quickly understand the complex mechanics in life. Wonder if there is any comparison between the amount of trials and errors a baby need to go through V.S. an A.I. need to go through using reinforcement learning to stand up and walk under similar gravity and muscle group setup?

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Well, babies don't learn to stand and walk (and many other skills) from scratch by trial and error. The hard part of constructing a walking machine has already been solved by millions of years of evolution.

Indeed, a calf can walk within hours of being born. A human infant might need more time than a calf in part because walking on two legs is harder than walking on four, but much of the difference simply comes down to the fact that humans are born so early that a lot of predetermined brain development has not yet occurred (the muscles and bones are also too weak). The study [1] found that across varying mammalian species, walking is learned a predictable amount of time after conception (as opposed to time after birth).

There's a surely a continuum between brain functions that are completely hard wired and completely learned from scratch. I think it's accurate to say that for many functions, learning is used as a form of adaptive refinement to finalize specific predetermined neural programs. But the exact interaction between learning and pre-programming isn't well understood in most cases.

[1] http://www.pnas.org/content/106/51/21889.abstract

There's a 3rd factor: memory. I.e. a somewhat static NN can still make input/output to more dynamic memory.

E.g. you could have basic feature detection (edges or maybe even something relating to facial features) prebaked into your visual system. General object categorization gets learned by the visual system while recognizing locations where you have been before needs to access memory.

Of course in the human brain memory is just another big web of neurons with different tradeoffs, but in software you can use other things.

It takes new humans 9 - 12 months to walk; it takes new humans 3 - 4 years to talk in complex sentences that are mostly grammatically correct and say things most people can understand.

The usual argument in favour of A.I goes something like this: each new human has to learn these things for themselves, whereas A.I. only has to learn to do it once, ever.

I'm curious about the total number of image recognition neutral networks that have ever been trained...

Each instance of an AI only has to do it once, which is an important distinction, but still relevant. E.g. once we get to the point where a human-level housekeeping robot is possible, you don't have to wait 10 years after purchasing it for it to do useful things in your house :)

If an AI is duplicated, so that a new instance is created (pro-creation), then surely the new instance already knows what the parent instance learnt?

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