AlphaGo handily beat 18-time world Go champion Lee Sedol 4-1, and in doing so taught us several interesting lessons about where AI research is today, and where it is headed.
There’s life in old AI approaches
One fascinating thing about AlphaGo is the unusual way it was designed. The software combined deep learning—the hottest AI technique out there today—with a much older, and far less fashionable, approach. Deep learning involves using very large simulated neural networks, and usually it eschews logic or symbol manipulation of the kind pioneered by the likes of Marvin Minksy and John McCarthy. But AlphaGo combines deep learning with something called tree-search, a technique invented by one of Minksy’s contemporaries and colleagues, Claude Shannon. Perhaps, then, we will increasingly see the connectionist and symbolic AI coming together in the future.
Polanyi’s paradox isn’t a problem
The game of Go, in which players try to surround and capture each other’s pieces across a large board, is a neat example of Polanyi’s famous paradox: “We know more than we can tell.”
Unlike with chess, there aren’t straightforward guidelines for playing the game or measuring progress, which is one reason why Go has historically been so difficult for computers to play. Machine learning, where a computer isn’t programmed (in the conventional sense) but rather generates its own algorithm for learning from examples, offers a way for computers to navigate Polanyi’s paradox. Plenty of things we do, like driving a car or recognizing a face, are similar. Some economists have highlighted this as an important point. And, as an article in the New York Times shows, some even see AlphaGo’s triumph as compelling evidence that computers will take over more tasks (and jobs) as machine learning is used ever more widely.
AlphaGo isn’t really AI
Not so fast, though. Amazing as AlphaGo is, it’s still a long way from truly intelligent. As AI expert and robotics entrepreneur Jean-Christophe Baillie points out, real intelligence will require not just more sophisticated learning but things like embodiment and the ability to communicate. Indeed, driving a car on a busy city street or interacting with someone you recognize is a lot more complex than we might realize. So while machine learning might let computers take on more tasks, it’s going to be a long time before they can replace everything people do.
AlphaGo is pretty inefficient
Compared with a human, AlphaGo learns quickly, consuming data on previous games and playing against itself at silicon speed. But it’s much less efficient than a person at learning, in that it requires far more examples of Go games in order to pick up effective techniques. This is one of the key problems with deep learning, which many people are trying to solve, by finding ways to learn from either from new kinds of data or from less data altogether.
Commercialization isn’t obvious
The skills demonstrated by AlphaGo—subtle pattern recognition, planning, and decision making—are obviously important. But it’s less obvious how they might be turned into a commercially viable product. Demis Hassabis, the founder of Google DeepMind, has said that the techniques developed for AlphaGo could be used to build a personal assistant that learns its master’s preferences and habits more effectively. But human language is a lot more complex than a board game, and a lot harder to learn from. In other words, it might be tricky to apply AlphaGo’s specific skill set in the messy real world.
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