I saw this story this morning, about IBM looking for more markets for its Watson information-sifting system (the one that performed so publicly on “Jeopardy”. And this caught my eye for sure:
John Baldoni, senior vice president for technology and science at GlaxoSmithKline, got in touch with I.B.M. shortly after watching Watson’s “Jeopardy” triumph. He was struck that Watson frequently had the right answer, he said, “but what really impressed me was that it so quickly sifted out so many wrong answers.”
That is a huge challenge in drug discovery, which amounts to making a high-stakes bet, over years of testing, on the success of a chemical compound. The failure rate is high. Improving the odds, Mr. Baldoni said, could have a huge payoff economically and medically.
Glaxo and I.B.M. researchers put Watson through a test run. They fed it all the literature on malaria, known anti-malarial drugs and other chemical compounds. Watson correctly identified known anti-malarial drugs, and suggested 15 other compounds as potential drugs to combat malaria. The two companies are now discussing other projects.
“It doesn’t just answer questions, it encourages you to think more widely,” said Catherine E. Peishoff, vice president for computational and structural chemistry at Glaxo. “It essentially says, ‘Look over here, think about this.’ That’s one of the exciting things about this technology.”
Now, without seeing some structures and naming some names, it’s completely impossible to say how valuable the Watson suggestions were. But I would very much like to know on what basis these other compounds were suggested: structural similarity? Mechanisms in common? Mechanisms that are in the same pathway, but hadn’t been specifically looked at for malaria? Something else entirely? Unfortunately, we’re probably not going to be able to find out, unless GSK is forthcoming with more details.
Eventually, there’s coing to be another, somewhat more disturbing answer to that “what basis?” question. As this Slate article says, we could well get to the point where such systems make discoveries or correlations that are correct, but beyond our ability to figure out. Watson is most certainly not there yet. I don’t think anything is, or is really all that close. But that doesn’t mean it won’t happen.
For a look at what this might be like, see Ted Chiang’s story “Catching Crumbs From the Table”, which appeared first in Nature, and then in his collection Stories of Your Life and Others, which I highly recommend, as “The Evolution of Human Science”.