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Twenty years after Deep Blue, what can AI do for us?

Peter Sayer | May 12, 2017
IBM built Deep Blue to win at chess -- but since then has taken a collaborative, rather than competitive, approach to artificial intelligence.

IDGNS: Since that match, we've seen DeepMind's Alpha Go take on some of the world's strongest go players and we've seen IBM's Watson take on the Jeopardy champions. What do you think are the next big challenges that AI is ready for?

MC: Board games have served AI very well, both chess and go, but I think board games have more or less had their day, and it's time to move on to more real-world problems, problems that have more complexity to them. Games like chess are very well-defined: Everything is there right in front of you, you've got all the information, you know exactly what moves are possible, you know what checkmate looks like and so on.

The real world isn't like that: There's complexity every which way you turn. I think we should add some additional complexity to the challenges and problems that we look at.

There are still interesting challenges in computer games. For example, I saw just recently that a program had beaten a group of human professionals at poker, and that's interesting because it adds this imperfect information as we call it, hidden information where your opponents know their cards but you don't. So that's one way of adding complexity. There are others.

But in the long run, we want to be tackling problems not where we're trying to create a system that can do as well or better than people, what we really want are systems that complement people in an interesting way and help people make decisions.

In chess, obviously, our goal at least initially with Deep Blue was to prove that it was possible to build a system that could play as well as the best players in the world. On the way, we built this system that played chess in a completely different way than the human way of playing chess. It was apparent that the human approach had its strengths and its weaknesses, and the computer approach that we used had its strengths and weaknesses. Combining the two together, in fact, was shown fairly quickly to produce a player that could be better than either a human alone or a computer alone.

Twenty years later that's still true, so I think that lesson we learned is applicable to practically every real-world problem we can think about.

For example, in health care, a physician can look at a patient and make a diagnosis and come up with a treatment. But what if they have an assistant that thinks about the problem differently than they do, has different skills, can look at all the recent medical literature and all the ongoing drug trials, and produce alternative diagnoses or alternative treatments that the human expert, the physician, can consider and accept or reject? It allows them to broaden their thinking and with that, get a higher level of performance than with either one alone.

 

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