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turchin comments on AIFoom Debate - conclusion? - Less Wrong Discussion

11 Post author: Bound_up 04 March 2016 08:33PM

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Comment author: torekp 08 March 2016 02:05:39AM *  1 point [-]

There are many cases where simple genetic algorithms outperform humans. Humans outperform GAs in other cases of course, but it shows we are far from perfect.

To riff on your theme a little bit, maybe one area where genetic algorithms (or other comparably "simplistic" approaches) could shine is in the design of computer algorithms, or some important features thereof.

Comment author: turchin 09 March 2016 09:02:02PM 0 points [-]

https://en.wikipedia.org/wiki/Neuroevolution "Neuroevolution, or neuro-evolution, is a form of machine learning that uses evolutionary algorithms to train artificial neural networks. It is most commonly applied in artificial life, computer games, and evolutionary robotics. A main benefit is that neuroevolution can be applied more widely than supervised learning algorithms, which require a syllabus of correct input-output pairs. In contrast, neuroevolution requires only a measure of a network's performance at a task. For example, the outcome of a game (i.e. whether one player won or lost) can be easily measured without providing labeled examples of desired strategies."