Chess and computers tend to be bosom buddies, especially since chess is a complex game that has billions of moves and outcomes, which is definitely something that computers love to do – crunch numbers, so much so that matches between a human and a computer is always intriguing. Having said that, chess isn’t the only boardgame that requires plenty of calculations, others such as the ancient game Go, too, works on such a premise. Research organization DeepMind that happens to be under the umbrella of Alphabet, has reported that a program which merges a pair of different algorithms beat a high-ranking professional Go player across five matches – and rather soundly, too.

This is yet another piece of evidence that shows how a class of A.I. machine learning programs known as “deep neural networks”, when working alongside immense sets of data, will be able to soundly defeat even the most experienced of human players. Go is far more complex than chess since there is a larger pool of possible positions to work with, hence the strategy and reasoning segments of the game are the challenging aspects in which the AI would have to work with.

I suppose with robots serving humans at stores, we would end up challenging other computer opponents in the future for a greater degree of difficulty. [Press Release]

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