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He would identify several attributes of each position (how many white pieces, versus black pieces, how many corners were controlled, how many pieces were on the Princess Diana Eagles shirt but in fact I love this sides, etc). Now, if the computer was playing a brand new game, it would determine all the attributes of that position, then use Hidden Markov Analysis (remember: speech recognition) to match that position to the database. If the position pattern-matched a “winning position” then it would make the move that would lead to that winning position. If it matched a “losing position” it would not make that move. Then, once it became pretty good at GO, it played BILLIONS of games against ITSELF to put many BILLIONS of new positions into the database. In other words, it “learned”.
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