Author: Dan Ellwein
Date: 13:58:49 03/27/00
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On March 27, 2000 at 15:35:13, José Antônio Fabiano Mendes wrote: >http://arn.org/docs/odesign/od191/deeperblue191.htm JAFM good article... Jose... here are some excerpts from the article that i thought were particularly interesting... "Deeper Blue is running on a machine capable of evaluating 200 million nodes per second. A top grandmaster, at a very generous estimate, can visualize and evaluate perhaps as many as a hundred different possibilities in a minute of concentrated thought. This is a speed difference of eight orders of magnitude, greater than the relative speed gap between the most advanced tactical fighter jet and the average inchworm. Clearly, something is going on in the human grandmaster’s mind that is not only radically different from what Deeper Blue’s program does, but also inconceivably more efficient. In view of the incredible complexity of chess and the limited speed of the human mind, it is a kind of computational miracle that humans can play chess at all. The clear implication, backed up by de Groot’s reports of the grandmasters’ verbal protocols, is that human chess masters immediately dismiss as irrelevant almost all of the possible moves for both sides in a given position, focussing only on a few alternatives at each ply. But exactly how the grandmasters determine relevance remains a riddle. At one time, when the computational barrier presented by full-width searches seemed insuperable, programmers did try a selective search approach modeled (or more accurately, intended to be modeled) on human methods of play. But the resulting programs were so prone to oversights in complex positions that they were consistently defeated by programs designed to do a full-width search. For decades now the selective search method has been abandoned. The failure of selective search programs to produce decent chess play becomes even more puzzling when one considers that human beginners, properly taught, can rapidly learn to play better chess. It is no particular secret how this is done: the beginners are shown various combinations and motifs, then given the opportunity to apply them in exercise positions. With sufficient practice, they are able to produce similar combinations in their own games. The difficulty programmers have in emulating this learning process comes at the level of characterizing the various ideas that the beginners are being asked to absorb. Talented human students do not require a full abstract description of a type of situation in which a given motif might be useful; they rapidly see what is pertinent in the examples and, presented with exercises, detect relevant similarities without further prompting. Just where programmers would like to find clues, pedagogic practice defers to the human mind and leaves out the intermediate steps. The IBM team found human grandmasters sufficiently articulate that they were able to use their input to extend and refine Deeper Blue’s evaluation parameters. But that refinement, as we have already seen, comes nowhere near to the level necessary to enable a program to perform well with a selective search. Where such long-range planning is possible, a master may keep his sights fixed on one set of goals for many moves, with the consequence that he does not have to start from scratch in the assessment of a position at every turn. By contrast, a computer essentially starts over at each move, its search not guided by any overarching ideas. It is chiefly by this characteristic -- the readiness of the program to abandon the strategically indicated paths in the dubious pursuit of material gains -- that computers can be distinguished from human beings in blind tests. The importance of the human capacity to sort out patterns, detect relevance, and apply learned knowledge is widely recognized... In construing chess as a computational problem, computer scientists overlooked the extent to which mastery in chess, like expertise in more open-ended pursuits, requires that elusive quality known as intuition. Recognizing a grandmaster’s chess abilities as something extraordinary, outsiders are likely to impute to him improbable computational powers and to construe a man-versus-machine chess match as a contest between human and electronic computers. The truth, stranger than this popular fiction, is that chess grandmasters do not work like computers at all and their thought processes have thus far resisted computational simulation. ...currently programmers have no idea how to enable the machine to select the relevant features of the position or to form and follow plans. Barring a conceptual breakthrough in this direction, computer chess is and will remain detectably inhuman. More importantly, we can see why Deeper Blue fails the Turing test, why computer simulation doesn’t give us performance with a human feel to it. The only way for programs to achieve high performance levels is to exploit the remarkable speed of computers in a finessed, alpha-beta pruned version of the brute-force search. Perforce, this leads to an inhuman style of play. But the more modest goal of emulating at least the problem-solving capacities of the human brain still draws the artificial intelligence community like a vision of the Holy Grail. On that front, the computer chess saga is indeed a lesson in humility. Deeper Blue’s performance does not advance our understanding of human cognition." regards - PilgrimDan
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