You'd be forgiven for assuming that DeepMind's artificial intelligence technology has already proven its chops. Back in 2016 the celebrated computer lab watched one of its AI programs do the unthinkable and win a game of Go against then world champion – and human being – Lee Sedol. Mastering the ancient Chinese board game was just one example of the machine learning DeepMind is hoping it can ultimately use to revolutionise sectors like science, healthcare, and energy. For the next step on that journey, DeepMind has turned its attention to StarCraft II. The seven-year-old RTS may still be an esports sensation, but it's not an obvious step up from Go.
StarCraft is a deep, complicated war strategy game. Google's AlphaStar AI crushed it.
Studies show that lots of Americans are worried that AI is coming for their jobs -- Uber and Lyft drivers, couriers, receptionists, even software engineers. A remarkable exhibition match today suggested that another group that should be worried is ... pro video gamers? In a stunning demonstration of how far AI capabilities have come, AlphaStar -- a new AI system from Google's DeepMind -- competed with pro players in a series of competitive StarCraft games. StarCraft is a complicated strategy game that requires players to consider hundreds of options at any given moment, to make strategic choices with payoffs a long way down the road, and to operate in a fast-changing environment with imperfect information. More than 200,000 games are played every day.
An interesting debate is emerging over Google's announcement last week of some details of its attempt to battle humans at the video game StarCraft II, a debate over what "AI" is and what "fairness" may mean for the field. ArsTechnica's Timothy B. Lee published an article yesterday analyzing the face-off between Google's DeepMind unit's AlphaStar computer program against one of the dominant human champs at StarCraft II, Poland's Grzegorz Komincz, known by his gamer handle MaNa. Lee argues the Google win over MaNa was "not a fair fight," citing the fact that AlphaStar's neural network configuration had an advantage in having access to "raw" game data during the course of play, data that human players do not have. When DeepMind researchers forced AlphaStar to play by only looking through a "camera," the way humans see the game, it lost. The use of extra information by AlphaStar raises two entwined questions: Should machine learning be constrained by human limitations, in order to be fair, and if it is not, can it really make any claims to be developing anything called intelligence, at least in the sense most people use that term? Also: Google's AI surfs the "gamescape" to conquer game theory Lee offers the view that AI should be bound by fair rules: "The ultimate way to level the playing field would be to make AlphaStar use the exact same user interface as human players," he writes.