So, Watson won at Jeopardy!… by a pretty significant lead, too. Inevitably, lots of folk are keen to downplay this victory, and for a variety of reasons. Commonest complaint would have to be regarding Watson’s speed-to-buzzer advantage, but its minders designers say that it’s not really that big a deal:

Though Watson seemed to be running the round and beating Jennings and Rutter to the punch with its answers many times, Welty insisted that Watson had no particular advantage in terms of buzzer speed. Players can’t buzz in to give their questions until a light turns on after the answer is read, but Welty says that humans have the advantage of timing and rhythm.

“They’re not waiting for the light to come on,” Welty said; rather, the human players try to time their buzzer presses so that they’re coming in as close as possible to the light. Though Watson’s reaction times are faster than a human, Welty noted that Watson has to wait for the light. Dr. Adam Lally, another member of Watson’s team, noted that “Ken and Brad are really fast. They have to be.”

A re-run with some sort of handicap might prove this one way or the other, but I suspect the doubters will find new advantages to pin on the machine… which , to my mind, rather misses the point of the exercise, which was to demonstrate whether or not a machine could outperform humans at a particular task. Quod erat demonstrandum, y’know?

A more interesting point is that even Watson’s creators aren’t entirely sure how Watson achieves what it achieves. George Dvorsky:

Great quote from David Ferrucci, the Lead Researcher of IBM’s Watson Project:

“Watson absolutely surprises me. People say: ‘Why did it get that one wrong?’ I don’t know. ‘Why did it get that one right?’ I don’t know.”Essentially, the IBM team came up with a whole whack of fancy algorithms and shoved them into Watson. But they didn’t know how these formulas would work in concert with each other and result in emergent effects (i.e. computational cognitive complexity). The result is the seemingly intangible, and not always coherent, way in which Watson gets questions right—and the ways in which it gets questions wrong.

As Watson has revealed, when it errs it errs really badly.

This kind of freaks me out a little. When asking computers questions that we don’t know the answers to, we aren’t going to know beyond a shadow of a doubt when a system like Watson is right or wrong. Because we don’t know the answer ourselves, and because we don’t necessarily know how the computer got the answer, we are going to have to take a tremendous leap of faith that it got it right when the answer seems even remotely plausible.

Dvorsky’s underlying point here is that we shouldn’t be too cocky about our ability to ensure artificial intelligences think in the ways we want them to. They’re just as inscrutable as another human mind. Perhaps even more so… which is why he and Anders Sandberg (among others) believe we should foster a healthy fear of powerful AI systems.

But the most interesting point I’ve seen made about Watson’s victory is a skeptical stance over at Memesteading:

When Alex Trebek walked by the 10 racks of 9 servers each, said to include 2880 computing cores and 15 terabytes (15,000 gigabytes) of high-speed RAM main-memory, I couldn’t shake the feeling: this seems like too much hardware… at least if any of the software includes new breakthroughs of actual understanding. As parts of the show took on the character of an IBM infomercial, the feeling only grew.

[…]

An offline copy of all of Wikipedia’s articles, as of the last full data-dump, is about 6.5GB compressed, 30GB uncompressed – that’s 1/500th Watson’s RAM. Furthermore, chopping this data up for rapid access – such as creating an inverted index, and replacing named/linked entities with ordinal numbers – tends to result in even smaller representations. So with fast lookup and a modicum of understanding, one server, with 64GB of RAM, could be more than enough to contain everything a language-savvy agent would need to dominate at Jeopardy.

But what if you’re not language savvy, and only have brute-force text-lookup? We can simulate the kinds of answers even a naive text-search approach against a Wikipedia snapshot might produce, by performing site-specific queries on Google.

For many of the questions Watson got right, a naive Google query of the ‘en.wikipedia.org’ domain, using the key words in the clue, will return as the first result the exact Wikipedia article whose title is the correct answer.

[…]

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.

All of which isn’t to demean Watson’s achievement so much as to suggest that perhaps the same results could be reached with a much smaller hardware outlay… though there is an undercurrent of “Big Iron infomercial” in there, too.