Except I think this is an example of economists not understanding other areas of science and mathematics. A misunderstanding thinking that "learn from zero" means having learned from NO data from the "real" world (in this case the limited definition of the game go). Other information about the real world, vast amounts of information, is indeed irrelevant to learning how to solve THIS problem << learn to play go very well >>djhbrown wrote:[i]The Economist[/i]: $ in AI wrote:...The most important is whether AI will always depend on vast amounts of data... A competing vision of AI stresses simulations, in which machines teach themselves using synthetic data or in virtual environments........... [so she must be really intelligent and will take over the outside real world without needing any real-world data as well as triumphing over the inside world of a board game of limited depth].
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Yes, it turns out that the set of information needed (by alpaha go zero) turns out to be small, but in problems like this, discovering the minimalist set, the necessary and sufficient set, is one of the difficulties. The reason I said "blindness" on the part of the economists is that they don't relate this to THEIR problem. They imagine that they NEED vast amounts of information about the real world (to make their predictions) without seeing that the reason for that is that they don't know which bits of information from the real world are relevant and which are not. And that appearances can be deceptive.
Thus before being shown, we would have thought that alpha go zero would have needed more information << information we cannot see is redundant to the minimal set of information that is sufficient >>
They also do not see what the (generalized) process might be able to do. Given a problem and a PROPOSED minimalist set of information, can the neural net learn to solve the problem "from zero" --- with "from zero" meaning "from just THIS minimalist set of information". In which case that set of information IS a sufficient set.
