AlphaGo Zero, the latest evolution of Google's elite algorithm, has no need for humans: The platform learns by playing against itself-with no intervention or historical data.
In a new study, the researchers report how that uncanny self-reliance sharpened Zero's intelligence to devastating effect: in 100 games against Zero, a previous AlphaGo incarnation - which cleaned the floor with us in 2016 - didn't pick up a single win.
What's really cool is how AlphaGo Zero did it.
Perhaps we should have seen this coming.
AlphaGo has come away way since it started beating humans. While AlphaGo's effectiveness in human games and against itself has shown that there's room for AI to surpass our capacity in tasks that we think are far too hard, the robot overlords aren't here yet.
Deep Mind said: "AlphaGo Zero uses a single neural network which is trained to predict the program's own move selection and the victor of its games - improving with each iteration of self-play". AlphaGo took what it learned from humans and did it better. Moreover, AlphaGo Zero requires much less computer resources to operate than its predecessors.
But there are some situations where an AI can train itself: rules-based systems in which the computer can evaluate its own actions and determine if they were good ones. That the team could build such an algorithm that surpassed previous versions using less training time and computer power "is nothing short of amazing", he adds.
The DeepMind team ran the AI against itself for three days, during which it completed almost five million games of Go. What's novel about AlphaGo Zero is that instead of just running the tree search and making a move, it remembers the outcome of the tree search - and eventually of the game. And that's why it was such a big deal when AlphaGo defeated Go champion Lee Sedol by 4-1 past year.
Since then, AlphaGo has continued to surprise and amaze. When it does this it actually produces an even more powerful neural network, which leads to another new iteration of the player.
AI programs like AlphaGo Zero that can gain mastery of various tasks without human input may be able to solve problems where human expertise falls short, says Satinder Singh, a computer scientist at the University of MI in Ann Arbor. AlphaGo Zero "figures out only for itself, only from self-play, and without any human knowledge, without any human data, without any human examples or features or intervention from humans". AlphaGo Zero was able to go from complete amateur to grandmaster in a matter of days. "This included not only fundamental elements of human Go knowledge, but also non-standard strategies beyond the scope of traditional Go knowledge". I don't see the principal theoretical reason why you should do that, why you should abandon lots of knowledge that we have about the world.
But DeepMind's developments go beyond just playing a board game exceedingly well. All in less than two months.
Computers long ago began outplaying human chess players, but there were doubts about whether a program could master Go's mix of strategy, intuition and mind-boggling complexity. AlphaGo Zero is likely to change that. But the game of Go represents a confined problem with rules and a clear definition of when the game ends-albeit there are a mind-numbing amount of game variations. Data sets of human knowledge could also potentially "impose a ceiling on the performance of systems trained in this manner". "But for Go players, that day is here".
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