Slashdot Mirror
Men vs. Machines
FFriedel writes "In October classical chess world champion Vladimir Kramnik is scheduled to play Deep Fritz in Bahrain. Now Garry Kasparov, who lost his title to Kramnik in 2000, but is still ranked as the strongest player in the world, has announced that he will play the computer chess world champion Deep Junior in Jerusalem at almost exactly the same time. Both programs are distributed by ChessBase. In 1997 Kasparov lost his famous match against Deep Blue."
From what I understand, they can't make a program that can beat even a decent player at that game. GNU Go whips me consistently at the lowest lever, though.
BlackGriffen
It's an exciting time for the chess nuts out there. Anyone that follows chess should be fairly excited by this. Of course, the chess followers are rooting for the human, while the AI folks are rooting for the computer.
It's been noted for years that one benchmark of a machine's ability to think intelligently was to beat a grandmaster in chess. That goal has been significantly harder to achieve than beating the Turing test. Now just for a Go playing computer, a harder still benchmark.
"Kasparov would move Qe4 here, man."
"Whoa, deep blue, man."
"Hey guys, we need a name...for...hey!"
And thus it's perpetuated.
Human chess players never learn about their opponent's behaviors beforehand?
If you know the computer will know how you will play, you should play in a different way. But the computer will obviously know you will know it knows how you play, and thus expect this. As a result, you should alter your strategy back to your original. The computer will also realize you will do this though, so you should again try to alter your playing manner.
But I do remember quite a few people criticizing the Deep Blue stunt because IBM trained Deep Blue by examining every Kasparov match on record. Kasparov had no idea what to expect since Deep Blue never played anyone else. Did Deep Blue every play any other grandmasters?
all i can say is "GO BANANA!"
(simpson's reference)
"I would say that 99 per cent of what my father has written about his own life is false." - L. Ron Hubbard Jr.
My father has been a chess fanatic for years upon years. He's read books upon books and is really good. He can beat any mere mortal that he plays. There are a bunch of people on Yahoo! games and other online chess networks that he can play and can compete with, but they are a distinct minority... it comes down to the rankings. Point is, my dad is really good.
There was a chess program for the Vic 20 that could whip my dad's ass every time. Machines have been whipping general players asses for a very long time. My dad is really good but for all of that my dad is still an amateur and could never hope to make a showing in a real competition. It's only the great grandmasters that give the machines trouble... these grandmasters are several orders of magnitude better than the amateur players like my father and are far better than most pros. It says a *LOT* that a machine is able to beat someone like Kasparov... even knowing his moves ahead of time.
It's true that the machine was made just to beat kasparov, but that was probably from a lack of programmer time..... it could be programmed the same, and a Bobby Fisher module added, and a Karpov module and a Kramnik module and so on.
I know some people who believe that being able to play the perfect game of Chess will fundamentally change the world because they will be able to relate the strategy used in Chess to the real world. Unfortunetly this only would work should the rules of the game stay constant, in the real world rules will change and an adapative algorithm will be needed to properly evaluate a given situation.
Humans and computers don't play chess the same way. The grandmasters can forsee, what is it like 10 of every move into the game, while the computer can see every move forseeable. I've never been a big fan of playing computers in chess, and that goes way back to the old battle chess game.... remember that one, where your characters would duke it out when a player made a capture? Anyways, I was able to beat that one a couple times, but mostly it totally wooped my ass for the simple fact I was 10, didn't have much *game* and lacked the mental capacity to see 100 moves into the game. IMO, the computer should be limited to a set amount of moves and time, and should have to consider which moves it should concentrate on, instead of looking at every single move possible. I'd also like some randomization in the game.
Well, here's a heads up. That is exactly how human players prepare for matches against each other. They sit down and play through their opponents previous matches, and try to find weaknesses and holes to use against them.
The point of all this is equally questioned. People seem to think that creating large expert systems is a done deal, and no more research needs to be done into how to construct programs that use a set of variables to give advice, in this case which chess piece to move. Again, here's a clue:
This kind of stuff is fundamental, basic research. Absolutely vital and incredibly useful as we continue to learn about how to better realise and utilise computer technology.
Insert old saw about dogs walking here.
Who knows who the world champion is in chess? There is all that politcal garbage out there with FIDE and rankings. Bah!
And as far a computer beating a human? Its just not that interesting a problem anymore. Especially when Ken Thompson (of UNIX fame) showed 20 years ago that brute force searches was the way to create a winning system against a human. Not very sporting. A great book on this was "Chess Skill in Man and Machine" edited by Peter Frey.
Its fun to watch humans race each other. Its boring to watch a human race a car. I think the same holds with humans, computers and chess competition.
I have not paid any attention to this, but does someone know whether it would be feasible to base a massively distributed chess engine on the Deep Junior basis? When we were thinking about continuation to the RC5-56 chall this was one thing which we considered. Could it be the time now, or is there already a lot of such projects - or maybe there is even already a category for such monsters :)
He is still going to make a ton of money!!!
Block Quote
"The six games will be played at the classical time control and the prize fund is that roundest of big round numbers, one million dollars. (Kasparov gets half a million up front and the other half is split 60/40 winner/loser. Ka-ching! Garry is definitely paying for dinner next time.)"
End Quote.
So Garry is getting at least 700,000 just for showing up. Man I wish I were that guy.
IBM is gonna look pretty good if both humans cream these beasts due to memories of Beep Blue.
Table-ized A.I.
If that fails, he plans to challenge his opponent to a "Double or Nothing" drinking contest at a local bar.
this was puglished yesterday in haaretzdaily.com. It has some interesting details like, for example, the track record of Junior, to this date, and that the competition will have a peace-builing slant to it, too.
Sigged!
Maybe if you were, say, a programmer or a computer professional, or maybe even had read an IT industry magazine at some point you would understand a little about the fundamentals of this discussion.
This has nothing to do with "computers counting faster" and everything to do with expert systems, that is programming computers to make "clever" decisions based on states. If this was just brute-force then why do you think it costs so much money to put together one of these systems? Because they have teams of programmers and serious hardware. More hardware than is needed for a brute-force approach, actually, so what's all the extra hardware doing? If you think this is all as easy as that one class you took in highschool where you typed:
10 PRINT "Hello!"
20 GOTO 10
and then laughed in that odd, shrieking way you have, then you really should get hit with the clue stick.
Not to debunk your entire post but....
If a human grandmaster is about to play Gary Kasaparov for instance, do you think he's likely to study as much about the way that Kasparov plays? Its just that the in this case the computer is able to forget the rest and _only_ focus on Kasparov's style. In any competition you would be foolish not to gain as much knowledge about your competition.
As for having multiple teams to win the Superbowl, ever heard of offense and defense and special teams. Its just an example of using the team that is most likely to win the play, or the game or whatever, it just so happens that these teams are all part of a larger team.
Its called specialisation and its pretty much what enabled us (humankind) to give up nomadic existence and focus on doing wonderful things like making chess playing computers and reading Slashdot instead of working.
"I'm tired of all this 'Aren't humanity great' bullshit. We're a virus with shoes" - Bill Hicks
I'm a chess fan, but I dont see any point in the computer vs. human matches. these "AI" chess playing computers simply look at threes and a database of good/common moves. real AI doesnt use trees (too many possibilities)
IBM even trained deep blue for kasparov, but kasparov never got a chance to play deep blue so could not have any idea of weaknesses in it's game (eg positions not in its database where it would have to waste time looking at the move tree.) which forced him to play very nonstandard games and use styles he is not used to using
to me, the fact that deep blue took kasparov does not mean anything except that kasparov is a truly amazing player (who else can compete against a super-comptuer programmed by computer scientists at a top corporation created soley to beat them?)
even more amazing is that kasparov only lost the series on a game where he was completely off
Only if the human really doesn't talk about anything in particular, and expects a meaningful response. ALICE cannot give meaningful responces.
ALICE would probably make a good CEO, rather than a conversation tool.
CEOBot: What would you like to know?
Interviewer: What were your profits this year?
CEOBot: What would you like to know about our profits this year?
Interviewer: How much were they?
CEOBot: How much do you think they were?
Interviewer: Well, you claimed 22billion.
CEOBot: I'm afraid I really don't know anything about that. Would you like me to sing you a song?
-Jayde
What's a sig?
That hasn't turned out to be the case. The search algorithms that the chess-playing programs use don't appear to be any great use for anything except playing chess (or closely related games like go or checkers).
Personally, I want to see a computer kick Kasparov's and Kramnik's ass (though I'm unconvinced it's going to happen this time around, it certainly will eventually) so that chess players shut up about defending the honor of humanity or some such rubbish. Knowing a little about how chess-playing programs work, I feel about as threatened by the prospect that the world chess champion can be trounced by a computer than the fact that in one second the PC I'm typing this at can do more arithmetic operations than I'll do in a lifetime.
Any sufficiently advanced technology is indistinguishable from a rigged demo
--Andy Finkel (J. Klass?)
Best world players are still slightly better than best programs (two best backgammon programs around are Snowie and JellyFish).
Slightly OT, but...
:)
I'm more interested in seeing someone write a strong Go opponent. It's pretty obvious that chess is rather simple for a powerful computer to brute force, but even the most sophisticated hardware and software can be beaten by an amateur Go player. The strongest Go programs rate at around the 8-kyu level (Go ratings start at 30-kyu for complete beginners, on up to 1-kyu, then from 1-dan to 9-dan for pro players).
There have been cash awards (on the order of a million dollars in at least one instance) put out on the table for developers who could write a Go program capable of beating a certain level player. So far, nobody's succeeded. MindZine has a nice (albeit a bit dated) article explaining why this is.
When a computer can play a really strong game of Go, I'll be impressed.
"Wow, you're like some kind of superhero able to ward off happiness and success at every turn."
-- Ryan Stiles
Here is an interesting NY Times article that ChessBase linked to.
(For those who say "fuck that registration shit")
***************
Early in the film "A Beautiful Mind," the mathematician John Nash is seen sitting in a Princeton courtyard, hunched over a playing board covered with small black and white pieces that look like pebbles. He was playing Go, an ancient Asian game. Frustration at losing that game inspired the real Mr. Nash to pursue the mathematics of game theory, research for which he eventually won a Nobel Prize.
In recent years, computer experts, particularly those specializing in artificial intelligence, have felt the same fascination -- and frustration.
Programming other board games has been a relative snap. Even chess has succumbed to the power of the processor. Five years ago, a chess-playing computer called Deep Blue not only beat but thoroughly humbled Garry Kasparov, the world champion at the time. That is because chess, while highly complex, can be reduced to a matter of brute force computation.
Go is different. Deceptively easy to learn, either for a computer or a human, it is a game of such depth and complexity that it can take years for a person to become a strong player. To date, no computer has been able to achieve a skill level beyond that of the casual player.
The game is played on a board divided into a grid of 19 horizontal and 19 vertical lines. Black and white pieces called stones are placed one at a time on the grid's intersections. The object is to acquire and defend territory by surrounding it with stones.
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.
"A good Go player could make a move and other players say, `Yes, that's a good move,' but they can't explain to you why it's a good move, or how they even know it's a good move," said Dr. John McCarthy, a professor emeritus at Stanford University and a pioneer in artificial intelligence.
Dr. Danny Hillis, a computer designer and chairman of the technology company Applied Minds, said that the depth of Go made it ripe for the kind of scientific progress that comes from studying one example in great detail. "We want the equivalent of a fruit fly to study," Dr. Hillis said. "Chess was the fruit fly for studying logic. Go may be the fruit fly for studying intuition."
Along with intuition, pattern recognition is a large part of the game. While computers are good at crunching numbers, people are naturally good at matching patterns. Humans can recognize an acquaintance at a glance, even from the back. "Every Go book is filled with advice on patterns of different kinds," Dr. McCarthy said.
Dr. Daniel Bump, a mathematics professor at Stanford, works on a program called GNU Go in his spare time. "You can very quickly look at a chess game and see if there's some major issue," he said. But to make a decision in Go, he said, players must learn to combine their pattern-matching abilities with the logic and knowledge they have accrued in years of playing.
"If you watch really strong players," Dr. Bump said, "some seem to make fairly mundane moves, but at the end of the game they're ahead. Others do spectacular things."
One measure of the challenge the game poses is the performance of Go computer programs. The last five years have yielded incremental improvements but no breakthroughs, said David Fotland, a programmer and chip designer in San Jose, Calif., who created and sells The Many Faces of Go, one of the few commercial Go programs.
Mr. Fotland's program was the winner of a tournament last weekend in Edmonton, Alberta, that pitted 14 Go-playing programs -- including several from Japan -- against one another. But even The Many Faces of Go is weak enough that most strong players could beat it handily.
Part of the challenge has to do with processing speed. The typical chess program can evaluate about 300,000 positions per second, and Deep Blue was able to evaluate some 200 million positions per second. By midgame, most Go programs can evaluate only a couple of dozen positions each second, said Anders Kierulf, who wrote a program called SmartGo.
In the course of a chess game, a player has an average of 25 to 35 moves available. In Go, on the other hand, a player can choose from an average of 240 moves. A Go-playing computer would take about 30,000 years to look as far ahead as Deep Blue can with chess in three seconds, said Michael Reiss, a computer scientist in London.
If processing power were all there was to it, the solution would be simply a matter of time, since computers are growing ever faster. But the obstacles go much deeper. Not only do Go programs have trouble evaluating positions quickly, they have trouble evaluating them correctly.
Nonetheless, the allure of computer Go increases as the difficulties it poses encourage programmers to advance basic work in artificial intelligence. Graduate students produce dissertations on the topic, and a handful of researchers around the world devote much or all of their attention to it.
The game attracts people from all fields. For example, Chen Zhixing, a retired chemistry professor in Guangzhou, China, wrote a program called Handtalk, which dominated the computer Go field for several years. Dr. Bump, 50, whose field is number theory, has been playing Go for 35 years and taught himself the C programming language four years ago so he could write Go software. Mr. Fotland, 44, the creator of The Many Faces of Go has been working on computer Go for 20 years and is chief technology officer at Ubicom, a small semiconductor company in Silicon Valley.
All are very strong Go players, and it takes a strong Go player to write even a weak Go program. Mr. Fotland, for instance, said he had written programs for checkers, Othello and chess. The algorithms are all very similar, and it is not difficult to write a reasonably strong program, he said. Each of the games took him a year or two to finish. "But when I started on Go," he said, "there was no end to it."
Mr. Fotland said that his Go programming was especially weak when he was a beginning player. "A lot of the stuff I wrote was just plain wrong because I didn't understand the game well enough," he said.
Even when skill develops, however, translating it into a program is not an obvious task. "There's a certain stream of consciousness when you're looking at positions," Dr. Bump said. "You might look at 10 variations, but you don't really know what's going on in the back of your mind. Even a strong player doesn't know how his mind works when he looks at a position."
"We think we have the basics of what we do as humans down pat," Dr. Bump said. "We get up in the morning and make breakfast, but if you tried to program a computer to do that, you'd quickly find that what's simple to you is incredibly difficult for a computer."
The same is true for Go. "When you're deciding what variations to consider, your subconscious mind is pruning," he said. "It's hard to say how much is going on in your mind to accomplish this pruning, but in a position on the board where I'd look at 10 variations, the computer has to look at thousands, maybe a million positions to come to the same conclusions, or to wrong conclusions."
Dr. Reiss, who is the author of Go4++, a previous champion that placed second in last weekend's playoff, agrees with Dr. Bump. Dr. Reiss, who is an expert in neural networks, compares a human being's ability to recognize a strong or weak position in Go with the ability to distinguish between an image of a chair and one of a bicycle. Both tasks, he said, are hugely difficult for a computer.
For that reason, Mr. Fotland said, "writing a strong Go program will teach us more about making computers think like people than writing a strong chess program."
Dr. Reiss, who works on Go full time, said he would not think of devoting his time to any other problem. "It's a fundamentally interesting problem, but also it's just the right level of difficulty," he said. "If it was too easy it would have been solved already. If it was fantastically difficult, people might give up in frustration."
"I think in the long run the only way to write a strong Go program is to have it learn from its own mistakes, which is classic A.I., and no one knows how to do that yet," Mr. Fotland said. A few programs have some learning capabilities built into them.
Mr. Fotland's program, for instance, refers to a database of games played by strong players in deciding its moves, and Dr. Reiss's program employs a learning scheme for deciding which moves are interesting to look at.
Dr. Reiss said he had come up with an idea for a new Go program that would learn by analyzing professional games. But to pursue his idea would require too much work, he said, depriving him of time to continue making updates to his current program.
It seems unlikely that a computer will be programmed to drub a strong human player any time soon, Dr. Reiss said. "But it's possible to make an interesting amount of progress, and the problem stays interesting," he said. "I imagine it will be a juicy problem that people talk about for many decades to come."
Umm, there is a plethora of programs out there which manage to beat all but the "super grand masters". Programs such as Crafty are able to beat practically any human in lightening and blitz games, and hold their own very well in standard games.
Take for example the reports of "Fischer" on the Internet, beating Nigel Short after giving away what amounts to a 10 move advantage. There is no way a human can give such a highly ranked player such an advantage in a blitz game and win so convincinly, it was obviously a bot running on the Crafty engine (or something similar), beating the crap out of Short.
So yes, there are general engines out there which are very highly rated and can beat 85% to 90% of grand masters.
Or maybe more of a "why would you want to":
Chessbase has several chess programs for sale on their website. While quite inexpensive (~$45-$80 USD) they are advertised as being damn near impossible to beat. In fact, Chessbase's front page highlights one of the programs for sale kicking the ass of the entire Swiss Chess Team!
So why would you want to actually buy one of these programs? They aren't teaching programs. They aren't for a friendly game against the computer. They aren't open sourced (that I could see) so you can't study the algorithms. They are meant to destroy every human they come in contact with.
Does anyone outside of chess grand masters use these things? (How many grand masters are there, anyway?) I'm a very mediocre chess player myself, and if I want my ass handed to me in chess I'll go down to the local high school club and call them all smelly virgins before starting a game. At least I'll have some face-to-face interaction.
So what's the point?
obviously no deficiencies vs. no obvious deficiencies
This threat to the supremacy of mankind will soon be quelled when we uncover the talented chess-playing midget secretly hidden inside Deep Fritz's supposed workings.
in any event- im reminded of the checkers champion computer players... they always win. the real question is- how do they win? the answer is: by storing a set number of move in lookup tables. in other words- once a game gets to a certain point, the computer opponent looks up, in a database, a winning set of moves from the given point in the game. how is this ai? how is this 'machine bettering man' on a level playing field? the answer is that it isn't.
programming a computer to play until it reaches a point where the number of moves left in the game are finite, and the computer has a database of moves that guarantee wins from this position is not artificial intelligence- it's loading the deck.
if you really want to impress people, build a machine that has no idea what the rules are, but rather is taught the rules as it plays the game. if that machine can beat the best players in the world, then we have an argument for a machine intelligence that is both strategic and insightful.
until that point, we have nothing but technical deception; technical deception in the same sense that Eliza was programmed as an 'ai'. what it appears to be on the surface is not, in fact, what it actually is.
That's ok, Jesus likes me anyway.
When Kramnik offered to play Fritz, he said "Fine, give me a copy of the program and let me play with it before hand." The creators of Fritz freaked out and everybody said "But then you'll be able to find the weaknesses and just exploit those!" Well, that's not Kramnik's fault -- if he found a human player that always made the same mistake, he'd certainly take advantage of it every time, right?
The list of fairness questions goes on and on...since a computer can memorize openings, can't a human player be allowed to have his books with him? Since a computer doesn't need rest breaks, can't they be as short as possible? Are the programmers allowed to tweak the computer between every match, every move? Why?
So what I'm wondering is, what has to happen in these matches in order for both sides to consider them fair fights?
www.HearMySoulSpeak.com
In the modern version of the turing test, ie IRCbots, most people are very easy to fool when they are not expecting it. However, fooling a discering judge who is trying to tell human thought from canned waffle is still impossibly hard, in the 'We still have very little idea of how to do it' category.
My Karma: ran over your Dogma
StrawberryFrog
Didn't Kasparov beat Deep Blue but got beaten by DeepER Blue?
>The problem is that these machines are being
/., and everybody keeps moaning it, but it's just patently false.
>programmed to play against 1 opponent, and are
>being fed data about that player's past games,
>habits, techniques...
This gets posted at least 10 times a story like this hits
Programs like Fritz, Junior and even Deep Blue are tuned to perform as well as possible against a wide range of testing opponents or known testpositions.
You can't just feed the computer some games from it's future opponent and have it magically adapt itself to this. About the only thing that is done in reality is to have the computer play into an opening that it is know to play well. For a human vs computer match, this just means trying to get an open position where tactics become predominant over longtime strategy.
And that is independent of whether you're playing Kramnik, Kasparov, Anand. It's the same for any human opponent.
--
GCP
Deep(er) Blue used some special-purpose hardware, but Deep Fritz and Deep Junior don't. Multiprocessors are a commodity nowadays.
Deep(er) Blue's custom ASICs were basically there to make the brute-force approach go faster. They didn't implement some sort of expert system or neural net, they had little to do with sophisticated position evaluation, they were mostly just there to speed up the nuts-and-bolts operations of walking extremely large decision trees.
The scorn you heap upon this post's grandparent seems just a trifle misplaced, since you yourself seem to know little about the programs being discussed. They're a combination of chess-specific knowledge and fast implementations of fairly ancient algorithms, so they're pretty formidable opponents, but in terms of AI research they've progressed little beyond an early-to-mid-80s level. Nobody that I know who actually works in AI would say any different, either.
Slashdot - News for Herds. Stuff that Splatters.
This was played in the qualifying event for the Kramnik match. It's now more than a year ago I believe and Fritz won by half a point in a 24 game match or so.
In the latest World Computer Chess Championship (July 2002) Junior won in a tiebreak over Shredder.
Fritz did not even make the top 3. (They participated with the name Quest to limit their damages)
--
GCP
Archon was released by Electronic Arts and I loved playing it on the Commodore 64.
Here's a site with a review and screen shots.
...and that is precisely the opportunity that was denied Kasparov. Deeper Blue and its handlers -especially Joel Benjamin - had years to dissect Kasparov's games, but Kasparov had no access to DB's oeuvre. That's not a level playing field.
Another aspect you've overlooked is that human preparation to play a particular opponent is usually on the order of weeks or months, and does not significantly sacrifice the preparer's ability to play other opponents. Even in the middle of preparing to play Kramnik or Anand, Kasparov could go to a tournament and beat just about anyone else. By contrast, DB was in preparation for years and the result was so finely tuned toward playing Kasparov that DB would have fared very poorly in any top-level tournament involving anyone other than Kasparov. That kind of inflexibility is not a hallmark of a intelligence, artificial of otherwise. What it indicates is that the basic methods were so old and so well understood that people have been able to spend years just tuning the implementation.
Making a computer beat the world champion is a respectable feat. However, it's not even the highest goal in computer chess. Making a computer that could beat a series of opponents, without fundamental changes equivalent to a brain transplant between matches, would be more impressive. Making a computer that could win a 16-player round robin tournament against a whole field of top grandmasters - something Kasparov still does regularly, to this day - would be more impressive still. Making a computer that could play speed chess better than Anand or Hawkeye would be another worthwhile challenge in a different direction. Then there's Go, and then a bunch of other challenges, and then there's the real world. Spending years to create a program that can beat one player in one chess match under less-than-fair conditions is really a pretty low goal.
Slashdot - News for Herds. Stuff that Splatters.
Deep Blue, Deep Fritz, Deep Junior, Deep Thought. When are we going to get Deep Frink, so I can find out who's going to win the Superbowl?
--
"Outlook not so good." That magic 8-ball knows everything! I'll ask about Exchange Server next.
If the computer's preparation were comparable in duration and resources to the human's preparation, that would be fine. My point, though, is that in the particular case of Deeper Blue vs. Kasparov that was not the case. The computer had much more time to prepare for Kasparov than vice versa (he was kinda busy winning tournaments and such). Similarly, DB had many more of Kasparov's games to study than vice versa. It's not a problem that DB was allowed to prepare; it's a problem that Kasparov effectively was not.
Slashdot - News for Herds. Stuff that Splatters.
Then I stand corrected. Nonetheless, I think even that still falls into the category of "making the brute-force approach go faster". The type of evaluation function involved, no matter how "sophisticated" it is in a certain context, is not truly sophisticated in the same manner as something that actually performs planning or learning functions. It's basically a calculator for a complex mathematical function, and is still driven more by the intelligence of the person who assigned weights to all of the positional factors than by any actual sophistication as an AI researcher would use the term.
Slashdot - News for Herds. Stuff that Splatters.
That is exactly how human players prepare for matches against each other.
False, players also need to talk, walk, remember other things, and a bunch of other non-specialized activities. They need to be humans. On the other hand, a computer is "anything, but not a human". Why? Because there is NO limit to how much memory they can use, how much processing power they might use. Perfect memory, perfect calculus.
So if you could build a infinite memory/ infinit processing power computer, you could just precalculate all the possible outcomes of matches. Say a ply of 60 or more.
That computer is smart?
Vision 1: To call a computer chess program "inteligent" you need to draw a line and state: "this memory and this CPU should give you enough resources to beat any human". Anything else is just plain unfair and it's no longer inteligent.
Vision 2: A chess program should be an entityand not a bunch tuned of knowledge / rules / algoritms. It should be able to learn from experience without human intervention (ie: no specialized learning program, this one should be tuned by the computer itself), it should be able to plan it's own strategy, and autotrain itself. Ie: you teach them the chess rules, let it comunicate (gather more data, a database if requested, etc) and then the computer must do everything without interference.
Level 1 is acceptable, but we'd like to see a computer beat a human under the much more fair Vision 2.
unfinished: (adj.)