Arend Hintze, Michigan State University
Researcher David Silver and colleagues designed a computer program capable of beating a top-level Go player – a marvelous technological feat and important threshold in the development of artificial intelligence, or AI. It stresses once more that humans aren’t at the center of the universe, and that human cognition isn’t the pinnacle of intelligence.
I remember well when IBM’s computer Deep Blue beat chess master Garry Kasparov. Where I’d played – and lost to – chess-playing computers myself, the Kasparov defeat solidified my personal belief that artificial intelligence will become reality, probably even in my lifetime. I might one day be able to talk to things similar to my childhood heroes C-3PO and R2-D2. My future house could be controlled by a program like HAL from Kubrick’s “2001” movie.
Not the best automated-home controller: HAL.
As a researcher in artificial intelligence, I realize how impressive it is to have a computer beat a top Go player, a much tougher technical challenge than winning at chess. Yet it’s still not a big step toward the type of artificial intelligence used by the thinking machines we see in the movies. For that, we need new approaches to developing AI.
Intelligence is evolved, not engineered
To understand the limitations of the Go milestone, we need to think about what artificial intelligence is – and how the research community makes progress in the field.
Typically, AI is part of the domain of engineering and computer science, a field in which progress is measured not by how much we learned about nature or humans, but by achieving a well-defined goal: if the bridge can carry a 120-ton truck, it succeeds. Beating a human at Go falls into exactly that category.
I take a different approach. When I talk about AI, I typically don’t talk about a well-defined matter. Rather, I describe the AI that I would like to have as “a machine that has cognitive abilities comparable to that of a human.”
Admittedly, that is a very fuzzy goal, but that is the whole point. We can’t engineer what we can’t define, which is why I think the engineering approach to “human level cognition” – that is, writing smart algorithms to solve a particularly well-defined problem – isn’t going to get us where we want to go. But then what is?
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