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We have to have, for many decades to come, I would suggest, humans having the final word on those decisions.
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If you decide to install a system that recommends a movie or a book to somebody, if you make a mistake it's not the end of the world, whereas some kinds of decisions are really important. MC: Sometimes the problems are not life or death. IDGNS: Is one advantage of this augmented intelligence system, where it's ultimately the physician making the decision, that it makes it clear for legal purposes where the responsibility lies? 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. 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.įor example, in health care, a physician can look at a patient and make a diagnosis and come up with a treatment. 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. 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. On the way, we built this system that played chess in a completely different way than the human way of playing chess. 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. There are others.īut 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. 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. There are still interesting challenges in computer games. I think we should add some additional complexity to the challenges and problems that we look at.
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The real world isn't like that: There's complexity every which way you turn. 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. 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. What do you think are the next big challenges that AI is ready for? 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. So, of course, on a world stage, it popped up again. I think we'd fixed four of the five ways it could happen but we missed one of them. We had seen that bug a few months earlier and thought we'd fixed it. It only appeared under certain circumstances: Deep Blue was given an allotment of time to calculate a move and if it ran out of time in a certain way, it could cause it to play a random move. Of course, it didn't happen then: It was very rare. MC: Yes, we did figure it out and fixed it, although we didn't fix it until after the second game, so it was in there for game two as well. IDGNS: How did the bug come about? Were you able to figure out what caused it? There was some speculation at some point that this caused Kasparov to not have a good picture of what Deep Blue could and could not do in the game of chess. But Deep Blue, due to a bug, played a random move, and the random move was a particularly bad move, and so as soon as Kasparov responded we resigned for Deep Blue.
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Kasparov would have had to prove that he knew how to win the position, which of course, I'm sure he was capable of doing. It had a losing position but the game could have gone on for quite some time. What happened was that, at the end of the first game of the match, Deep Blue was destined to lose. MC: I'm not sure if that's a valid theory or not.