Re:I agree... no time soon.. (Score 1)

As for the math.. I am curious to see how he explains the following statement: "Put it all together and you should be able to build a machine that searches more than 100 trillion positions per second--easily a million times as fast as Deep Blue. That would be enough to build a tree of analysis for Go as big as Deep Blue's was for chess and to evaluate all its end positions properly. If we assume the top Go players calculate about as deeply as the top chess players do, the result should be a machine that plays Go as well as Deep Blue played chess. Well enough, that is, to beat any human player." According to http://en.wikipedia.org/wiki/Go_complexity (I know.. not the end all of sources but fairly reliable), the game-tree complexity of go is 10^360. According to wikipedia John Tromp and Gunnar Farnebäck showed that of those moves.. only 10^172 are legal. These estimates seem to be on the lower end of other views of its complexity. So anyway.. the paper states two methods to trim this huge tree... 1) If you find a move that is not as good as another, don't go further down that path. 2) I don't really understand the details (he doesn't give them) but he would cache results already decided and wouldn't have to recalculate those paths until something interfered with it. So for (1) It seems really hard to decide which move is better than another as mentioned earlier (2) Things are jumping around quite a lot on a board and I think the caching would lose its effectiveness. If we isolate only the right third of the board and there is a corner group on each side and a group in the middle... as these groups take form they will influence how the other groups form and will need to be recalculated often. A human does this with visualization and intuition. So let's say there are 10^172 moves that may need to be traversed. Let's say that by a miracle of branching tactics mentioned in (1) the computer decides that one trillionth of a percent of these moves are viable. That leaves us with 10^158. Okay.. still have a huge number. Since (2) above seems pretty fuzzy... lets just go ahead and cut the exponent into a fourth(thats a big feat I think). That leaves us with about 10^40 board positions to evaluate. So 10^40 boards * 1 seconds/10^14 boards = 10^26 seconds. So how long is this? 10^26 seconds / 60 / 60 / 24 / 365 = about 10^18 years. Now I know that there are complications that could make this math not exactly accurate. I don't really know how to guestimate how many trees his two methods could cut off. I do feel however that my guesses were not on the light side.. they were on the heavy side. I don't know much about complexity theory.. but it seems to me that his statement shown above seems very optimistic. He says that a computer like this would have a depth tree as deep as Deep Blue's was for chess. Does he mean depth of moves? We already know that there is a much greater depth of moves in go than chess.. what does he mean by that? Anyway.. it seems very optimistic and he might be a very bright guy.. but his success with Deep Blue may have clouded his logic.