The most compelling article I came across relating to my research paper for artificial intelligence was an article produced by TheNewYorkTimes. The article talked about how a robot built by Google, has just defeated the human champion, Lee Se-dol, four games to one in the tournament of the strategy game of Go.” What makes this article compelling was that it talked about how human players of the game called Go,  “The top players, it turns out, can’t fully access their own knowledge about how they’re able to perform so well. This self-ignorance is common to many human abilities, from driving a car in traffic to recognizing a face.” Next, the article went into depth about how with computers, today with our understanding, they are tasked with managing flights, processing payrolls, and routing telephone numbers. These complex systems that help computers function and sort these types of data accordingly “requires painstaking precision to explain exactly what the computer is supposed to do.” What’s most frightening is that the people who created the system that had beat the guy in the game can now “learn winning strategies almost entirely on their own, by seeing examples of successes and failures.” and most striking “AlphaGo does use simulations and traditional search algorithms to help it decide on some moves, but its real breakthrough is its ability to overcome Polanyi’s Paradox. It did this by figuring out winning strategies for itself, both by example and from experience.”

From my findings on the article “A Computer Wins by Learning Like Humans” I can come to the understanding that AI is in fact progressing. Therefore, without a doubt, they are learning how to solve and beat humans in the game. The next question that I am pondering is, to what extent are the scientist trying to fix with AI? Like what problems do we need AI for?


Works Cited

Mcafee, Andrew, and Erik Brynjolfsson. “A Computer Wins by Learning Like Humans.” New York Times, 16 Mar. 2016, p. A23(L). Science in Context, Accessed 15 Feb. 2018.

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