In 2016 for the first time ever, a “grand master” of the game Go was defeated by a Google created program named AlphaGo. The periodical I read was published in Nature before AlphaGo had even played  Lee Sedol. But the program had already beaten the best European player and all other commercial Go programs to date. AlphaGo was employed a deep neural net. Deep neural nets consist of nodes that are all connected together. Each node is designed to analyze the input and give an output that can help the program decide where to go “get the pun?” . One node might determine all the legal moves, one might be for immediate attack, immediate defenses, or analyzing where a position is being contested or held. The AlphaGo programmers were also successful in making the program capable of learning, which it did so magnificently. The program employed tactics into its game play that few, if any, professional players have seen before. The algorithms used are very complex and are different in each node, but when all the nodes communicate together the program is better than any human player can be.


CC BY-SA 4.0 Gale Research on AlphaGo and Machine learning. by Andrew is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

1 Comment
  1. Miguel 2 years ago

    This is a very interesting post Andrew, I really enjoy how concise the paragraph was. I must also say this adds on to my research of technology, this is just proof that technology is expanding rapidly. I also enjoyed how you interacted with your readers when you paused and tried to make a humorous comment in your paragraph. I hope to read more about your research, I find your topic very interesting.

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