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Google DeepMind's AI Beats Humans At Even More Computer Games
An anonymous reader writes: Google DeepMind's learning algorithm has trumped human performance in an even greater range of games from the Atari 2600. The system's performance in classic games for the 80's games console has improved steadily since it was revealed in April last year (video) and a paper released yesterday shows it besting people in 31 titles.
I don't want my computer to play classic atari games better than me I want it to make my work easier so I can have more time to play classic atari games, just saying.
But that's not at all the point of this article. The point of this article is that a computer program learned--in a manner SOMEWHAT analogous to human learning--through practicing how to play certain video games without having any game-specific special programming. AI opponents have existed as long as there have been video games (or close to it) and you're right, if that's what this article was about, it would be be boring. Neural net learning by examining visual output--now that's pretty cool.
Well, sortof. From TFA:
"However, the system's continued poor performance in Ms Pacman exposes a weakness that DeepMind discussed earlier this year. The limitation stems from the DeepMind system only looking at the last four frames of gameplay, about one fifteenth of a second of the game, to learn what actions secure the best results." (my emphasis)
GP misunderstands the ML aspect of this, but it does come down to reflexes and precision in this specific project. It is nevertheless interesting to investigate which games the net performs badly on and which ones it doesn't.
In a way, this is also a manner of 'ranking' games: the harder it is for such a system to perform well at it, the more cerebral and less primitive/physical it probably is (although I don't want to imply that one type is better than the other)