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I fear this is an overly mystical misinterpretation of neural nets. (Standard feed-forward) neural nets are layers of non-linear feature transformations. They t
by argonaut 11y ago
I fear this is an overly mystical misinterpretation of neural nets. (Standard feed-forward) neural nets are layers of non-linear feature transformations. They take inputs and at each step transform those inputs into a more compact representation, distilling the inputs into their most important factors (and throwing away unimportant factors).
So the most likely explanation is that policy/value nets in AlphaGo have learned to extract - with cold logic - the key factors that make up what humans believe to be "good" board positions.
It has little to do with voodoo about neural connections and magic emerging from the weights. AlphaGo has most likely managed to identify the important factors of good board positions (by seeing tons of examples of good and bad moves/positions). It only appears to be magical because these factors are most likely very complex and inter-dependent.
This is supported by the AlphaGo paper - they report that AlphaGo without tree search is about as good as the best tree search programs (amateur pro level). So AlphaGo has taken amateur-pro-level board analysis ability, and combined it with tree search, to achieve top-player performance..
- versteegen 11y ago> So AlphaGo has taken amateur-pro-level board analysis ability, and combined it with tree search I don't think that's an entirely accurate way of putting it, because a player who reaches that level is also doing a little tree searching. Maybe if you found some human who managed to reach "amateur pro" level by playing purely on snap, instinctual decisions without any logic or exploration of variants at all, yes, then you could say their ability to evaluate positions is as good as AlphaGo's. But I would guess that you are right anyway that we can deduce that its ability to evaluate a board position really is below that of better professionals, and its huge strength is due to the tree search (which of course involves a second "policy" net to pick moves to explore).
- reacweb 11y agoIn go, the tree searching is named "reading". Even beginners need to read very deep trees (more than 7 moves) to anticipate the result of a Semeai (capturing race).
- Veedrac 11y agoIs that really different to what humans do, though?
- taneq 11y ago> distilling the inputs into their most important factors (and throwing away unimportant factors). Isn't that pretty much the definition of intuition? Combining a bunch of things in some unknown and nonlinear way to result in a 'feeling' about the situation?
- goldenkey 11y agoPretty much. The nature of evaluating a Go position, is that it has some notions of similarity like islands, "aliveness", and local features that can be looked at liberty-wise. But in order to fully understand how it all pieces together, one must have seen other positions to intuit the kind of future playstyle that will result in the game. Anyhow, it's perfect for a stochaistic system that just recognizes patterns. As long as you can provide the tree search (ie. the partial position evaluation) you can basically just let it rip.
- argonaut 11y agoYes. But the person I was responding to was definitely up in the clouds describing things as "magic" (and implicitly equating intuition to magic).
- mehwoot 11y agoI fear this is an overly mystical misinterpretation of neural nets. Isn't most human experience an overly mystical interpretation of physics/chemistry?
- hasenj 11y agoWell obviously it's reduced to actual algorithms. The point is, the programmers don't quite understand what exactly are those features that the neural net is seeing.