A conversation between Vlad Firoiu, an AI grad student at MIT, and Brian Hamrick. Vlad believes Alphago will win, Brian disagrees.
Tim Vlad, how much do you know about computer go AI? In particular, i want to place online bets on Lee Sedol vs Alphago. Vlad Firoiu I know a fair bit about it people in my lab at MIT are also placing bets I think alphago will win _____________________________ Brian Hamrick I think I'd take lee sedol's side at 1:1 maybe like 70% Tim so Vlad Firoiu is betting on Alphago Brian the pro's opinions is that alphago has weaknesses in macro play I think the weakness is that alphago is probably trained on most pro games, as opposed to mainly with top pro games so it will have some weaknesses that come from lower tier pros there straight up aren't that many top pro games Tim so the claim is that the learning algorithms that deepmind uses doesn't have enough data in top pro games to train on? Brian I think so so there's also potential weaknesses in how alphago learned because there's a lot of relatively set sequences of moves, and some of those aren't optimal depending on the rest of the board but those situations almost never arise, so it's likely that alphago has never seen the reasons for the patterns so potentially it will lose out by not deviating, or by sedol deviating _____________________ Vlad: ok, so basically it is learning to be a good but not best human Vlad: so 1. I think that even if that were the case it could still win by looking further ahead which is something humans are terrible at but computers are amazing at due to memory, parallelization, and modern tree search Vlad: 2. alphago will bootstrap by playing itself having reached pro level play, it can generate infinite high quality data this has been used in other games to exceed human performance I wouldn't be surprised if alphago reaches a level way past lee sedol or that any human will reach Vlad: again, all these things can leverage massive parallelization and I suspect google will go all-out on this in terms of computing resources and engineering not to mention having go experts and AI experts improving the algorithms Vlad: 3. I think that alphago pretty much has the fundamentally "right" algorithm this is obviously not something I can prove but I think that the combination of heuristic learners with tree searchers is pretty darn good __________________________ Brian: hmm I don't think that bootstrapping really works the way vlad says it does it seems like it should just make alphago more confident in something arbitrary, which may or may not be correct depending on the specifics it may or may not be true that alphago can read further than sedol it's probably comparable at this point Tim read further? Brian read = look ahead Tim hm, how could people possibly look farther ahead than computer? Brian well so traditionally in go it's been because humans only actually consider ~5 moves max, whereas computers don't know which 5 moves those are so they have to search a lot more the neural net part of alphago is probably attempting to mimic that intuition of humans so alphago may not have the same limitations as older go ai ______________________________________ Vlad: well brian's right about the alphago being better than older ai's by having more human-like intuition also there are good ways to leverage these heuristics to guide the tree search Vlad: it is true that bootstrapping isn't quite as straightforward as it seems but deepmind is well aware of this and has several ideas on how to make it work which they've demonstrated on simpler games Vlad: re lookahead: brian is trying to account for lee sedol's better heuristics which allow him to look at a much smaller branching tree which is certainly valid in comparing to older go bots which had bad heuristics but I suspect that alphago's heuristics are already good enough to allow super-human tree search to kick in ________ Brian: hm I should mention that even the top go players make multiple mistakes per game (as determined by post-game analysis) so this means two things one is that alphago may have inherited some of those mistakes from the learning set the other is that alphago may be able to avoid that because of the consistency of the tree-search part of the algorithm it's very difficult to tell which side is advantaged from that Brian: one of the reasons that it's unclear is that most of the time the reason that a move is a mistake isn't revealed until many many moves later, and there's a lot of variations in the meanwhile so it's not clear that the tree search will be enough to recognize it _______ Vlad Firoiu works on deep learning at MIT, in the lab of world famous AI researcher Josh Tenenbaum. The opinions he expresses are entirely his own, and are not affiliated with MIT or Professor Tenenbaum. Brian Hamrick is a graduate from MIT, IOI gold medalist, and all around very intelligent person. He blogs about a wide range of topics including Haskell, Go, programming, mathematics, Pokemon speedrunning. Check out his blog at Brianhamrick.com/blog _______ Postscript: chat with Jacob Steinhardt Tim: do you think Alphago will beat Lee Sedol? Jacob: i doubt it Lee Sedol is way better than the last guy i think it would have had to improve more in the last 6 months than the improvement that it was relative to the previous state of the art to be better than Lee Sedol Tim: is this informed by your knowledge of go, by your knowledge of AI, or both? Jacob: lol probably neither i'm just looking at the elo ratings and projecting Reposted from https://docs.google.com/document/d/1sdBnDVenK7YwuQGZpKsbb0pxpB-ghl4uhn0-NbBHsw0/edit?usp=sharing
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The moment I put my pen to the paper (metaphorically, for this blog), I'm out of content to write.
This is an interesting phenomenon -- I'm pretty sure I have a stack of ridiculous ideas somewhere in my brain, but once I told myself "time to spin up the good ol' writing machine, tim chu!" these thoughts escaped me, and I find that my brain is totally blank. This is the "decide-to-write first" way of tackling things, where one detrmines what they should do and then tries to do it. In contrast, there's the "ideas first" school of thought, were thoughts and ideas float around in the brain and then coalesce into art. This last item is a guess based on conversations with talented undergrad mathematicians, and how they come up with good ideas. I would estimate up to 40 to 90 percent of MIT mathematics students believe that the best undergraduate mathematicians "force" themselves to read math books, or tell themselves to do it and have a reserve of love or willpower that let them power through the books. From a brief conversation with the illustrious Amol Aggarwal (one of the top undergrads from MIT), this is a misconception. He takes me through the mental process he uses: (1) Questions and ideas that flit in and out of the mind. (2) Sometimes, it becomes clear that more knoweldge is required to think about the question more deeply .When this occurs, it appears reasonable to read a section of a book, or learn a particular branch of mathematics. Where do the questions and ideas in (1) come from? According to Amol, his questions came from previous querstions, which came from previous questions, which .... eventually went down to a "seed idea" that his advisor, Alexei Borodin, proposed to him. Thus the picture looks much less like "time to do math" and more like.... starting out with a seed idea from Borodin, which blossoms into a tree of questions, and books and references are accessed accordingly when they appear relevant to the question at hand. |
Author"All we ever want is indecision, all we really like is what we know." - Alexander Pope. ArchivesCategories |