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AlphaGo Zero

The folks at Deep Mind are at it again. From their website:

Artificial intelligence research has made rapid progress in a wide variety of domains from speech recognition and image classification to genomics and drug discovery. In many cases, these are specialist systems that leverage enormous amounts of human expertise and data.

However, for some problems this human knowledge may be too expensive, too unreliable or simply unavailable. As a result, a long-standing ambition of AI research is to bypass this step, creating algorithms that achieve superhuman performance in the most challenging domains with no human input.

They have come up with a new version of AlphaGo but this one learned to play Go entirely by playing against itself beginning with random play. In 40 days of self-training, it became stronger than the previous version of AlphaGo, the one that beat up on Ke Jie. This is kind of scary, don’t you think?

While it is still early days, AlphaGo Zero constitutes a critical step towards this goal. If similar techniques can be applied to other structured problems, such as protein folding, reducing energy consumption or searching for revolutionary new materials, the resulting breakthroughs have the potential to positively impact society.

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