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Len Yabloko's List: IBM Watson
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With a full, inverse-indexed, cross-linked, de-duplicated version of Wikipedia all in RAM, even a single server, with a few cores, can run hundreds of iteratively-refined probe queries, and scan the full-text of articles for sentences that correlate with the clue, in the seconds it takes Trebek to read the clue.
That makes me think that if you gave a leaner, younger, hungrier team millions of dollars and years to mine the entire history of Jeopardy answers-and-questions for workable heuristics, they could match Watson’s performance with a tiny fraction of Watson’s hardware.
Unfortunately, Jeopardy didn’t open this as a general challenge to all, like the DARPA Grand Challenges, with a large prize to motivate creative entries. Jeopardy seems to have simply followed IBM’s lead – and perhaps even received promotional payments from IBM for doing so. (I can’t find a definitive statement either way.)
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Computers may be able to solve problems and produce usable search results using complicated algorithms, but I’ve yet to see one that can come up with fresh ideas.
That’s why a continued push for innovation and creativity may not only be important for the U.S. economy, it may also be what saves us in the soon-to-come “Robot Wars of 2069″!
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There is much debate over the significance of this achievement, even within the AI community. I personally feel it is very significant, that a task we once thought reserved only to human thought is now shown possible by machines. It’s easy to forget that when Deep Blue defeated Kasparov in 1996, that task was also thought to be impossible, and now chess programs running on cell phones can beat grandmasters. Before we know it, deep QA software will be running on cell phones as well.
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Wolfram|Alpha is a completely different kind of thing—something much more radical, based on a quite different paradigm. The key point is that Wolfram|Alpha is not dealing with documents, or anything derived from them. Instead, it is dealing directly with raw, precise, computable knowledge. And what’s inside it is not statistical representations of text, but actual representations of knowledge.
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Here’s an example of a clear Watson win and Google Search fail, a 1976 entrant in the “modern” this was kicked out for wiring his epee to score points without touching his foe. The correct response is, “what is the pentathlon?” The Google Search results are miserable and no shortening of the query brought up any relevant results on page one. Of course if I “translated” the answer to “1976 cheating epee” or even “1976 cheating olympics” (the category was olympic oddities) Google Search finds the correct answer in the first result.
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So after day 1 I’m nonplussed, hasn’t Google been doing for years, on a larger scale, what Watson is just now doing on TV?
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However, in truth, Watson does not really think at all. He merely goes through a series of algorithms to come up with the most likely answers and then weighs the probably of each of these answers against each other to determine which one he should choose. A great information gathering machine, yes, but a thinker Watson is not.
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