With some irony, I expect, Gizmodo gave the following headline to a story this week about a rudimentary sprinting robot: “Someday, this robot will run faster than us all.” This week also brings the news that in a couple of months we will have a chance to see if IBM has made a champion artificially-intelligent Jeopardy player. I for one do not doubt that eventually, robots — maybe even the same robot — will be able to run faster than us all and win at Jeopardy and cook my dinner or at least provide me with a recipe that will use all the stray leftovers in my refrigerator. And then what will AI and robotics researchers do?

A hint to answering this question can be found by going to the IBM Research home page and putting in the search term “Deep Blue,” the name of the company’s chess-playing computer that famously beat World Chess Champion Garry Kasparov. The first results take you to what seem to be orphaned Web pages from 1997. Eventually you reach a page that acknowledges that the team has moved on to other projects. So too with the MIT Media Lab Personal Robotics Group which abounds in aspirational descriptions and videos, but seems short on actual results that conform to those aspirations. Has the teddy-bear robot called “Huggable” in fact been turned, as its makers expected, into a communication avatar, an early education companion, or a therapeutic companion? One would be hard-pressed to know.

My guess is that graduate students graduate and funding opportunities change. And some questions get answered, or perhaps not; in either case researchers move on, maybe building on what they have done, maybe moving in a new direction entirely. Doubtless, as in any other kind of research, there are times when the results have a nearly immediate impact in the wider world, or eventually get filtered into products and processes that we come to take for granted. But in these academic fields, as in all others, it looks to me like a good deal of what gets done amounts to lines, sometimes very expensive lines, on a C.V.

For those of us who observe this world from the outside, knowing it works this way provides two cautionary lessons. First, there is not necessarily a great idea or accomplishment behind every great-sounding press release or polished website. No surprise there, I hope. Second, it usually takes some time to judge the full impact of the new knowledge and abilities that we gain in these kinds of research programs. If IBM’s “Watson” program wins its Jeopardy match, we will doubtless be treated to a good deal of speculation about what it means — I might be tempted to engage in some myself. But the best response will still probably be that we can only wait and see. That’s good, because time is a useful thing for us slow-thinking humans. But it is also problematic, as the frog in the slowly warming pan of water eventually finds out.

[Photo via MGM Television via Curt Alliaume.]


  1. Deep Blue was a milestone; there was a day when people argued that computers would never beat grand masters, because chess was too complicated a game and required a human level of insight and intuition. Deep Blue showed that brute force can be a substitute – and that technology had reached the level where it could defeat the best human player of this "thinking man's game."

    If Watson is crowned as the world's champion Jeopardy player, we will have passed another milestone. The game was chosen both because it is so astonishing that a computer could play it, and because it has direct implications for practical AI query systems processing natural language in unrestricted domains, where the system must formulate hypotheses about what information is being sought and assess and select among them on general criteria of plausibility and fitness.

    Unlike Deep Blue, Watson will not be a dead end for IBM. The market for chess players is small; the market for information oracles, problem solvers and natural language processors and interfaces is huge.

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