By David B. Fogel
Blondie24 tells the tale of a working laptop or computer that taught itself to play checkers much better than its creators ever might through the use of a application that emulated the elemental ideas of Darwinian evolution--random edition and common selection-- to find by itself the right way to excel on the video game. not like Deep Blue, the prestigious chess computing device that beat Garry Kasparov, the previous global champion chess participant, this evolutionary application did not have entry to innovations hired by means of human grand masters, or to databases of strikes for the endgame strikes, or to different human services in regards to the video game of chekers. With merely the main rudimentary details programmed into its "brain," Blondie24 (the program's web username) created its personal technique of comparing the complicated, altering styles of items that make up a checkers online game through evolving synthetic neural networks---mathematical versions that loosely describe how a mind works.It's becoming that Blondie24 should still look in 2001, the yr after we take note Arthur C. Clarke's prediction that in the future we'd achieve making a considering computer. during this compelling narrative, David Fogel, writer and co-creator of Blondie24, describes in convincing element how evolutionary computation may also help to carry us toward Clarke's imaginative and prescient of HAL. alongside the best way, he supplies readers an inside of look at the attention-grabbing heritage of AI and poses provocative questions about its destiny. * Brings essentially the most fascinating parts of AI examine to lifestyles via following the tale of Blondie24's improvement within the lab via her evolution into an expert-rated checkers participant, in response to her notable luck in web competition.* Explains the principles of evolutionary computation, easily and clearly.* provides complicated fabric in an enticing variety for readers without heritage in machine technological know-how or man made intelligence.* Examines foundational matters surrounding the production of a pondering machine.* Debates even if the recognized Turing try particularly assessments for intelligence.* demanding situations deeply entrenched myths concerning the successes and implication of a few famous AI experiments * indicates Blondie's strikes with checkerboard diagrams that readers can simply persist with.
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Extra info for Blondie24: Playing at the Edge of AI
The WOPR’s conclusion: “Strange game . . ” The movie audiences of the 1980s had no problem envisioning a machine that was smart enough to convince itself that nuclear war wasn’t winnable. That seemed plausible enough. The mechanisms underlying the WOPR were never hinted at in the movie. It was simply “intelligent,” and the audience was left to accept that this machine could play games, understand the outcome, try out alternative strategies, compare the results, and iteratively improve its performance.
That’s not what we want. 4 By changing the weights and the thresholds of the neurons,we can make a neural network that performs this task or fails miserably. The same principle holds for designing a neural network that assesses positions on a checkerboard. If a neural network has the right number of neurons and the right weights between those neurons, that network might do very well. But even if it has the right number of neurons, with the wrong weights, the neural network would play very poorly.
That’s what this match was about. Machine Learning in Deep Blue: Close but Not Quite Let’s dig a little deeper into how Deep Blue worked. 20 Material referred to the point value assigned to each chess piece. Conventional wisdom starts by assigning one point for pawns, three points for knights and bishops, ﬁve points for rooks, and nine points for a queen. Fogel,Blondie24 8/28/01 9:32 AM Page 31 Of course, these are just heuristics, and in certain cases it makes sense to give up more material for better position.