AlphaGo and Google DeepMind: (Un)Settling the Score between Human and Artificial Intelligence
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.
|Courtesy of Flickr user Alexandre Keledjian
|Cofounder of DeepMind Demis Hassabis
Whether or not AlphaGo is actually weaker when it plays first is difficult to know since Lee Sedol may be the only person that can attest to this. During the post-four game press conference, cofounder of DeepMind Demis Hassabis stated that Lee Sedol’s win was valuable to the algorithm and the researchers would take AlphaGo back to the UK to study what had happened, so this weakness could be confirmed (and presumably fixed). One important point of Go play that may have influenced the outcome though is that AlphaGo will play moves to maximize its chances of winning, irrespective of how this move influences the margin of victory. Whether or not this is a weakness is probably up for debate as well, but in this sense AlphaGo is not playing like a professional human player. Go has a long history of being respected for its elegance and simplicity, but AlphaGo is not concerned with the sophistication or complexity of the game – it just wants to win.
|Courtesy of Flickr user Little Book
Deep neural networks are loosely based on how neural connections in our brains work, and neural networks have been utilized for years to optimize our searches in Google and to increase the performance of voice recognition in smartphones. Analogous to synaptic plasticity, where synaptic strength increases or decreases over a lifetime, computer neural networks change and strengthen when presented with many examples. In this type of processing, neural networks are organized into layers, and each layer is responsible for constructing only a single piece of information. For example, in facial recognition software, the first layer of the network may only pick up on pixels and the second layer will only be able to reconstruct simple shapes, while a more sophisticated layer may be able to recognize difficult shapes (i.e, eyes and mouths). These layers will continue to become more complex until the software can recognize faces.