David, that's a fantastic and succinct summarization. Tysvm!
On Jan 9, 2017 12:19 AM, "David Ongaro" <david.ong...@hamburg.de> wrote: > On Jan 5, 2017, at 10:49 PM, Robert Jasiek <jas...@snafu.de> wrote: > > > On 06.01.2017 03:36, David Ongaro wrote: > > Two amateur players where analyzing a Game and a professional player > happened to come by. > So they asked him how he would assess the position. After a quick look he > said “White is > > > leading by two points”. The two players where wondering: “You can count > that quickly?” > > Usually, accurate positional judgement (not only territory but all > aspects) takes between a few seconds and 3 minutes, depending on the > position and provided one is familiar with the theory. > > > Believe it or not, you also rely on “feelings” otherwise you wouldn’t be > able to survive. > > Some see DNNs as some kind of “cache” which has knowledge of the world in > compressed form. Because it's compressed it can’t always reproduce learned > facts with absolute accuracy but on the other hand it has the much more > desired feature to even yield reasonable results for states it never saw > before. > > Mathematically (the approach you seem yourself constrain into) there > doesn’t seem to be a good reason why this should work. But if you take the > physical structure of the world into account things change. In fact there > is a recent pretty interesting paper (not only for you, but surely also for > other readers in this list) about this topic: https://arxiv.org/abs/ > 1608.08225. > > I interpret the paper like this: the number of states we have to be > prepared for with our neural networks (either electronic or biological) may > be huge, but compared to all mathematically possible states it's almost > nothing. That is due to the fact that our observable universe is an > emergent result of relatively simple physical laws. That is also the reason > why deep networks (i.e. with many layers) work so well, even though > mathematically a one layer network is enough. If the emergent behaviours of > our universe can be understand in layers of abstractions, we can scale our > network linearly by the number of layers matching the number of > abstractions. That’s a huge win over the exponential growth required when > we need a mathematical correct solution for all possible states. > > The “physical laws” for Go are also relatively simple and the complexity > of Go is an emergent result of these. That is also the reason why the DNNs > are trained with real Go positions not just with random positions, which > make up the majority of all possible Go positions. Does that mean the DNNs > won’t perform well when evaluating random positions, or even just the > "arcane positions” you discussed with Jim? Absolutely! But it doesn’t have > to. That’s not its flaw but its genius. > > David O. > > > _______________________________________________ > Computer-go mailing list > Computer-go@computer-go.org > http://computer-go.org/mailman/listinfo/computer-go >
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