NYT Story On Go Programs And AI 246
mykej writes: "The NYT (registration required, blah blah) has a story on Go, the hardest game for computers to play. From the article: 'Programmers working on Go see it as more accurate than chess in reflecting the ineffable ways in which the human mind works. The challenge of programming a computer to mimic that process goes to the core of artificial intelligence, which involves the study of learning and decision-making, strategic thinking, knowledge representation, pattern recognition and, perhaps most intriguingly, intuition.' There are a few throwaway lines about Nash from 'A Beautiful Mind,' although they don't mention the game he invented after getting frustrated with the inconsistencies of go."
Go is harder or.....? (Score:2, Insightful)
It will take a general-purpose AI to play go (Score:5, Insightful)
I think that the minute you start to write a game-playing program, you're trapped by the very natural structures you have to use to make the program even play a legal game. You can't help but start to use minimax search. With go, you add modules for life & death evaluation, influence generation functions, the list goes on and on.
But all these things are just hard-coded approximations of some of the ways people think about go when they play, ripped out of their essential representational context. Real people have rich conceptual networks linking all of these skills together, which multiplies their power enormously. Give a beginning human player a perfect black-box life and death evaluator, like go programs ideally have, and he will never become a strong player. Only by solving life and death problems yourself (to take just one example) can you integrate that kind of knowledge into your total go knowledge. I maintain that this integration is essential.
Will computers ever beat people at go? Sure. But I'll bet the first program to do so will be a general-purpose near-human level AI, that thinks of board positions in terms of physical metaphors. It will have a rich mental landscape.
Bob Hearn
Re:Kasparov and IBM (Score:2, Insightful)
Deep Blue was prepared for the match the same way any other chess player would have prepared for the match - by studying the opponent's games. This is not some trick, it's standard preparation. This is always done in teams, and during the match players always have seconds (as in assistants) to help them analyze and study the games played so far. And if a game is adjourned for the night the seconds stay up analyzing the position and in the morning go over their findings with the player. That's the way matches are played.
Kasparov's complaint was not that the program was prepared in this way, but rather that he did not have sufficient opportunity to prepare for the program.
Standing on the shoulders of giants (Score:5, Insightful)
I respect what you are saying here and understand your reasons for not working on a go-playing program yourself, but I would challenge you with this: Even though you will probably not be the person to write the go ai program that is "near-human level", the person who does eventually write it (X number of years/decades in the future) will most definitely only be able to do it because he learned from people who came before him and attempted the endeavor. In short, it takes Newton to formulate the basic laws for physics and the calculus before Einstein can go further and discover relativity and quantum physics. And as Newton said, the only reason he could accomplish what he did was because he "stood on the shoulders of giants" that had come before him.
I am sure that this is not a new idea to you, but I present it again because I think it is very valid. We are at a very primitive state when it comes to computer ai, as anyone who has done any ai knows, both because of our lack of understanding of how our own intelligence/consciousness works, and because of our lack of good programming tools that allow us to work at a high abstraction of thought (i.e. most of the code we write is very tedious, and even though it is necesssary for our ai programs, it has little to do with actual ai). It is similar to knowing that you need a modern race-car when oil refineries, engines, and smelting have not even been invented yet. It is up to us to create those go-carts, pardon the pun, and start exploring how we might create a smelter, looking forward to the day when the infrastructure will be in place for others to continue the progress.
I know what you mean when you say that when you begin work on an ai problem like go, you are immediately trapped into things you have been taught, common procedures that you know others have used for similar types of problems, etc. However, this does not mean that you cannot be the one to think up the next innovation with respect to ai, taking the next step in creating a "rich mental landscape" that will lead to the integration you believe is essential to true ai.
I am quite positive that you are more knowledgeable about ai than me, since I have only dabbled in it here and there, but I hope you take my encouragement in the spirit I have intended to give it.
Peace to you,
Devin
Download a Go program for OS X (Score:3, Insightful)