AlphaGo and Google DeepMind: (Un)Settling the Score between Human and Artificial Intelligence

By Katie L. Strong, PhD 

In a quiet room in a London office building, artificial intelligence history was made last October as reigning European Champion Fan Hui played Go, a strategy-based game he had played countless times before. This particular match was different from the others though – not only was Fan Hui losing, but he was losing against a machine.

The machine was a novel artificial intelligence system named AlphaGo developed by Google DeepMind. DeepMind, which was acquired by Google in 2014 for an alleged $617 million (their largest European acquisition to date), is a company focused on developing machines that are capable of learning new tasks for themselves. DeepMind is more interested in artificial “general” intelligence, or AI machines that are adaptive to the task at hand and can accomplish new goals with little or no preprogramming. DeepMind programs essentially have a kind of short-term working memory that allows them to manipulate and adapt information to make decisions. This is in contrast to AI that may be very adept at a specific job, but cannot translate these skills to a different task without human intervention. For the researchers at DeepMind, the perfect platform to test these types of sophisticated AI: computer and board games. 

Courtesy of Flickr user Alexandre Keledjian
DeepMind had set their sights high with Go; since IBM’s chess playing Deep Blue beat Garry Karparov in 1997, Go has been considered the holy grail of artificial intelligence, and many experts had predicted that humans would remain undefeated for at least another 10 years.

The views, opinions and positions expressed by these authors and blogs are theirs and do not necessarily represent that of the Bioethics Research Library and Kennedy Institute of Ethics or Georgetown University.