THE VALUE OF DATA FROM AN ARTIFICIAL INTELLIGENCE PERSPECTIVE

Andrei Popescu ()

Annals of the University of Craiova for Journalism, Communication and Management, 2019, vol. 5, issue 1, 172-194

Abstract: Go is arguably the most complex board game in existence. Its goal is simple, to surround more territory on the board than your opponent. This game has been played by humans for the past 2,500 years and is thought to be the oldest board game still being played today. In 2016, Google DeepMind's AlphaGo beat 18-time world champion Lee Sedol in four out of five games. Now, normally a computer beating a human at a game like chess or checkers, wouldn't be that impressive, but Go is different. Go cannot be solved by brute force, Go cannot be predicted, there are over 10^170 moves possible in Go. To put that into perspective, there are only 10^80 atoms in the observable universe. AlphaGo was trained using data from real human Go games. It ran through millions of games and learned the techniques used and even made up new ones that no one had ever seen and this is very impressive alone. However, what many people don't know is that only a year after AlphaGo's victory over Lee Sedol, a brand-new AI called AlphaGo Zero, beat the original AlphaGo, not in four out of five games, but beat it 100 to 0, all games in a row. The most impressive part is that it learned how to play with zero human interaction. There was no data that needed to be input and this technique is more powerful than any previous version. It isn't restricted to human knowledge as no data was given. No historical figures were given and with just the bare-bones rules AlphaGo Zero surpassed the previous AlphaGo in only 40 days of learning. In only 40 days, it surpassed over 2,500 years of strategy and knowledge and it only played against itself. Now, is regarded as the best Go player in the world, even though it isn't human (Silver D., Hassabis D., 2017). This article will analyses several studies and researches on Artificial Intelligence (AI) and its other subsets, from a perspective of Data Input, focusing on a synthesis of several framework attributes, necessary to sustain trust.

Keywords: Artificial Intelligence (AI); Artificial Narrow Intelligence (ANI); Artificial General Intelligence (AGI); Artificial Super Intelligence (ASI); Machine Learning (ML); Deep Learning (DL); Data Science; Data Capital (search for similar items in EconPapers)
JEL-codes: Z0 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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