By David B. Fogel

ISBN-10: 1558607838

ISBN-13: 9781558607835

Blondie24 tells the tale of a working laptop or computer that taught itself to play checkers much better than its creators ever may perhaps through the use of a application that emulated the fundamental rules of Darwinian evolution--random edition and typical selection-- to find by itself the right way to excel on the online game. in contrast to Deep Blue, the distinguished chess computing device that beat Garry Kasparov, the previous international champion chess participant, this evolutionary software did not have entry to concepts hired through 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 purely the main rudimentary info programmed into its "brain," Blondie24 (the program's net username) created its personal technique of comparing the advanced, altering styles of items that make up a checkers online game by means of evolving synthetic neural networks---mathematical versions that loosely describe how a mind works.It's becoming that Blondie24 may still look in 2001, the 12 months once we consider Arthur C. Clarke's prediction that at some point we'd reach making a considering laptop. during this compelling narrative, David Fogel, writer and co-creator of Blondie24, describes in convincing element how evolutionary computation can help to carry us towards Clarke's imaginative and prescient of HAL. alongside the best way, he offers readers an within investigate the interesting historical past of AI and poses provocative questions on its destiny. * Brings some of the most interesting parts of AI learn to existence via following the tale of Blondie24's improvement within the lab via her evolution into an expert-rated checkers participant, in response to her remarkable luck in web competition.* Explains the principles of evolutionary computation, easily and clearly.* offers complicated fabric in a fascinating kind for readers with out history in machine technological know-how or man made intelligence.* Examines foundational concerns surrounding the construction of a considering machine.* Debates even if the well-known Turing try particularly checks 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 stick to.

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Extra resources for Blondie24: Playing at the Edge of AI

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But even if it has the right number of neurons, with the wrong weights, the neural network would play very poorly. Finding the right weights becomes the critical and vexing issue. A Sinister Task OR is a simple function. We only need three neurons, and we can figure out the right values for the weights and thresholds simply by examining the neural network and doing a little scratch work with paper and pencil. Fogel,Blondie24 8/28/01 9:32 AM Page 46 To compute more interesting complicated functions (like assessing the worth of a position in a game of checkers), we need more neurons.

HAL isn’t a mere collection of facts, but rather a learning machine. We’ve programmed thousands of facts into the common scientific calculator, but it doesn’t learn. We’ve programmed thousands of facts into “expert systems” for medical diagnosis and other applications, and yet these programs often fail to handle simple situations,and more important, fail to learn how to avoid such failures. Fogel,Blondie24 8/28/01 9:32 AM Page 17 We’ve programmed knowledge into Deep Blue,and yet Deep Blue can’t do anything but play chess.

11 Using faster hardware to process more boards in the same amount of time meant that chess programs could look farther ahead and anticipate a greater possible range of moves and countermoves. Fogel,Blondie24 8/28/01 9:32 AM Page 27 direct route to making better chess machines came from optimizing their hardware rather than their software. Deep Blue, the epitome of this optimization, emerged from the battle of two competing teams of computer experts at Carnegie Mellon University. Each team sought to take full advantage of tailoring computer circuitry to evaluate chess positions.

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