Chess Metaphors: Artificial Intelligence and the Human Mind by Diego Rasskin-Gutman, Deborah Klosky

By Diego Rasskin-Gutman, Deborah Klosky

Once we play the traditional and noble online game of chess, we grapple with principles approximately honesty, deceitfulness, bravery, worry, aggression, attractiveness, and creativity, which echo (or let us go away from) the attitudes we absorb our day-by-day lives. Chess is an job within which we set up just about all our to be had cognitive assets; hence, it makes a fantastic laboratory for research into the workings of the brain. certainly, examine into synthetic intelligence (AI) has used chess as a version for clever habit because the Fifties. In Chess Metaphors, Diego Rasskin-Gutman explores primary questions about reminiscence, inspiration, emotion, awareness, and different cognitive methods in the course of the video game of chess, utilizing the strikes of thirty-two items over sixty-four squares to map the structural and useful association of the mind.

Rasskin-Gutman makes a speciality of the cognitive job of challenge fixing, exploring it from the views of either biology and AI. reading AI researchers' efforts to software a working laptop or computer that may beat a flesh-and-blood grandmaster (and win a global chess championship), he reveals that the consequences fall brief compared to the really artistic nature of the human brain.

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5 shows some data for which both the outcome and the attributes are numeric. It concerns the relative performance of computer processing power on the basis of a number of relevant attributes; each row represents one of 209 different computer configurations. ) This is called a regression equation, and the process of determining the weights is called regression, a well-known procedure in statistics that we will review in Chapter 4. 4), and in Chapter 3 we will examine different representations that can be used for predicting numeric quantities.

The definition sounds circular, but it can be made to work. Search engines use PageRank (among other things) to sort web pages into order before displaying the results of your search. Another way in which search engines tackle the problem of how to rank web pages is to use machine learning based on a training set of example queries— documents that contain the terms in the query and human judgments about how relevant the documents are to that query. Then a learning algorithm analyzes this training data and comes up with a way to predict the relevance judgment for any 21 22 CHAPTER 1 What’s It All About?

A metric called PageRank, introduced by Google’s founders and also used in various guises by other search engine developers, attempts to measure the standing of a web page. The more pages that link to your web site, the higher its prestige, especially if the pages that link in have high prestige themselves. The definition sounds circular, but it can be made to work. Search engines use PageRank (among other things) to sort web pages into order before displaying the results of your search. Another way in which search engines tackle the problem of how to rank web pages is to use machine learning based on a training set of example queries— documents that contain the terms in the query and human judgments about how relevant the documents are to that query.

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