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Discussion and Debate
Discussion and Debate
Physical & Life Sciences
Preventing artificial intelligence from taking on negative human traits.
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<blockquote data-quote="sjastro" data-source="post: 75949912" data-attributes="member: 352921"><p>Using chess as an example, supervised learning which uses human chess games as the data results in human bias as the AI program plays like a human.</p><p></p><p>In <a href="https://en.wikipedia.org/wiki/Game_theory#:~:text=Game%20theory%20is%20the%20study,systems%20science%20and%20computer%20science." target="_blank">Game Theory</a> humans and computers play games to win in order to obtain some payoff.</p><p>For humans it could be satisfaction of winning or monetary gain; in the case of computers it is to maximize some numerical value.</p><p></p><p>In reinforced machine learning the chess moves and the rules of chess are the only parameters given to the program.</p><p>The program plays itself but under a reward system; if it wins it gives itself a positive value, if it loses a negative value.</p><p>Since the payoff is to maximize this value, the program recognizes it needs to win games while discarding the games it loses.</p><p>The program initially starts off playing purely random moves but through reinforced machine learning the win/loss ratio increases with time.</p><p>Since there is no human involvement in the training except for the chess moves and rules, the reward system produces a concept of chess strategy unique to AI.</p><p>There is a reverse bias now with the top human chess players being influenced by AI strategies.</p><p></p><p>A more in depth view of reinforced learning can be found in this video.</p><p></p><p>[MEDIA=youtube]0MNVhXEX9to[/MEDIA]</p></blockquote><p></p>
[QUOTE="sjastro, post: 75949912, member: 352921"] Using chess as an example, supervised learning which uses human chess games as the data results in human bias as the AI program plays like a human. In [URL='https://en.wikipedia.org/wiki/Game_theory#:~:text=Game%20theory%20is%20the%20study,systems%20science%20and%20computer%20science.']Game Theory[/URL] humans and computers play games to win in order to obtain some payoff. For humans it could be satisfaction of winning or monetary gain; in the case of computers it is to maximize some numerical value. In reinforced machine learning the chess moves and the rules of chess are the only parameters given to the program. The program plays itself but under a reward system; if it wins it gives itself a positive value, if it loses a negative value. Since the payoff is to maximize this value, the program recognizes it needs to win games while discarding the games it loses. The program initially starts off playing purely random moves but through reinforced machine learning the win/loss ratio increases with time. Since there is no human involvement in the training except for the chess moves and rules, the reward system produces a concept of chess strategy unique to AI. There is a reverse bias now with the top human chess players being influenced by AI strategies. A more in depth view of reinforced learning can be found in this video. [MEDIA=youtube]0MNVhXEX9to[/MEDIA] [/QUOTE]
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Preventing artificial intelligence from taking on negative human traits.
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