“I want to create a medical version of this,” he adds. “A Watson M.D., if you will.” He imagines a hospital feeding Watson every new medical paper in existence, then having it answer questions during split-second emergency-room crises. “The problem right now is the procedures, the new procedures, the new medicines, the new capability is being generated faster than physicians can absorb on the front lines and it can be deployed.” He also envisions using Watson to produce virtual call centers, where the computer would talk directly to the customer and generally be the first line of defense, because, “as you’ve seen, this thing can answer a question faster and more accurately than most human beings.”

“I want to create something that I can take into every other retail industry, in the transportation industry, you name it, the banking industry,” Kelly goes on to say. “Any place where time is critical and you need to get advanced state-of-the-art information to the front of decision-makers. Computers need to go from just being back-office calculating machines to improving the intelligence of people making decisions.” At first, a Watson system could cost several million dollars, because it needs to run on at least one $1 million I.B.M. server. But Kelly predicts that within 10 years an artificial brain like Watson could run on a much cheaper server, affordable by any small firm, and a few years after that, on a laptop.

Ted Senator, a vice president of SAIC — a high-tech firm that frequently helps design government systems — is a former “Jeopardy!” champion and has followed Watson’s development closely; in October he visited I.B.M. and played against Watson himself. (He lost.) He says that Watson-level artificial intelligence could make it significantly easier for citizens to get answers quickly from massive, ponderous bureaucracies. He points to the recent “cash for clunkers” program. He tried to participate, but when he went to the government site to see if his car qualified, he couldn’t figure it out: his model, a 1995 Saab 9000, was listed twice, each time with different mileage-per-gallon statistics. What he needed was probably buried deep inside some government database, but the bureaucrats hadn’t presented the information clearly enough. “So I gave up,” he says. This is precisely the sort of task a Watson-like artificial intelligence can assist in, he says. “You can imagine if I’m applying for health insurance, having to explain the details of my personal situation, or if I’m trying to figure out if I’m eligible for a particular tax deduction. Any place there’s massive data that surpasses the human’s ability to sort through it, and there’s a time constraint on getting an answer.”

Many experts imagine even quirkier ways that everyday life might be transformed as question-answering technology becomes more powerful and widespread. Andrew Hickl, the C.E.O. of Language Computer Corporation, which makes question-answering systems, among other things, for businesses, was recently asked by a client to make a “contradiction engine”: if you tell it a statement, it tries to find evidence on the Web that contradicts it. “It’s like, ‘I believe that Dallas is the most beautiful city in the United States,’ and I want to find all the evidence on the Web that contradicts that.” (It produced results that were only 70 percent relevant, which satisfied his client.) Hickl imagines people using this sort of tool to read through the daily news. “We could take something that Harry Reid says and immediately figure out what contradicts it. Or somebody tweets something that’s wrong, and we could automatically post a tweet saying, ‘No, actually, that’s wrong, and here’s proof.’ ”

CULTURALLY, OF COURSE, advances like Watson are bound to provoke nervous concerns too. High-tech critics have begun to wonder about the wisdom of relying on artificial-intelligence systems in the face of complex reality. Many Wall Street firms, for example, now rely on “millisecond trading” computers, which detect deviations in prices and order trades far faster than humans ever could; but these are now regarded as a possible culprit in the seemingly irrational hourlong stock-market plunge of the spring. Would doctors in an E.R. feel comfortable taking action based on a split-second factual answer from a Watson M.D.? And while service companies can clearly save money by relying more on question-answering systems, they are precisely the sort of labor-saving advance deplored by unions — and customers who crave the ability to talk to a real, intelligent human on the phone.

Some scientists, moreover, argue that Watson has serious limitations that could hamper its ability to grapple with the real world. It can analyze texts and draw basic conclusions from the facts it finds, like figuring out if one event happened later than another. But many questions we want answered require more complex forms of analysis. Last year, the computer scientist Stephen Wolfram released “Wolfram Alpha,” a question-answering engine that can do mathematical calculations about the real world. Ask it to “compare the populations of New York City and Cincinnati,” for example, and it will not only give you their populations — 8.4 million versus 333,336 — it will also create a bar graph comparing them visually and calculate their ratio (25.09 to 1) and the percentage relationship between them (New York is 2,409 percent larger). But this sort of automated calculation is only possible because Wolfram and his team spent years painstakingly hand-crafting databases in a fashion that enables a computer to perform this sort of analysis — by typing in the populations of New York and Cincinnati, for example, and tagging them both as “cities” so that the engine can compare them. This, Wolfram says, is the deep challenge of artificial intelligence: a lot of human knowledge isn’t represented in words alone, and a computer won’t learn that stuff just by encoding English language texts, as Watson does. The only way to program a computer to do this type of mathematical reasoning might be to do precisely what Ferrucci doesn’t want to do — sit down and slowly teach it about the world, one fact at a time.

“Not to take anything away from this ‘Jeopardy!’ thing, but I don’t think Watson really is answering questions — it’s not like the ‘Star Trek’ computer,” Wolfram says. (Of course, Wolfram Alpha cannot answer the sort of broad-ranging trivia questions that Watson can, either, because Wolfram didn’t design it for that purpose.) What’s more, Watson can answer only questions asking for an objectively knowable fact. It cannot produce an answer that requires judgment. It cannot offer a new, unique answer to questions like “What’s the best high-tech company to invest in?” or “When will there be peace in the Middle East?” All it will do is look for source material in its database that appears to have addressed those issues and then collate and compose a string of text that seems to be a statistically likely answer. Neither Watson nor Wolfram Alpha, in other words, comes close to replicating human wisdom.