IBM has announced that it is developing a computer to compete on the game show Jeopardy! If the system can outwit human contestants--by comprehending and responding to a variety of questions more rapidly--artificial intelligence will have made a significant advance, the computer's developers say.

Back in 1997, IBM's chess-playing supercomputer Deep Blue famously beat then world champion Garry Kasparov in a highly publicized match. The event was symbolic in that it showed that computers could outsmart humans at a game once considered too intellectually challenging for a machine to master. Even so, chess is a game with well-defined rules and limits. Playing Jeopardy!, in contrast, requires a computer to deal with a variety of subject matters, from politics to pop culture, and to answer questions based on clues that involve analyzing subtle meanings, riddles, and puns. Jeopardy! might seem a lot simpler than chess, but for a machine, it's a far harder challenge.

According to the New York Times,

Under the rules of the match that the company has negotiated with the "Jeopardy!" producers, the computer will not have to emulate all human qualities. It will receive questions as electronic text. The human contestants will both see the text of each question and hear it spoken by the show's host, Alex Trebek.

The computer will respond with a synthesized voice to answer questions and to choose follow-up categories. I.B.M. researchers said they planned to move a Blue Gene supercomputer to Los Angeles for the contest. To approximate the dimensions of the challenge faced by the human contestants, the computer will not be connected to the Internet, but will make its answers based on text that it has "read," or processed and indexed, before the show . . .

I.B.M. will not reveal precisely how large the system's internal database would be. The actual amount of information could be a significant fraction of the Web now indexed by Google, but artificial intelligence researchers said that having access to more information would not be the most significant key to improving the system's performance.

IBM has already conducted some laboratory demonstrations of the program and still has some bugs to work out, such as getting the machine to understand the way that Jeopardy! clues are offered and what it should be searching for, leaving some experts skeptical that the computer program will vastly change the field.

According to the show's producers, an episode of Jeopardy! will be aired (at an unspecified date) pitting the IBM system--named Watson, after IBM founder Thomas J. Watson Sr.--against several human players.

Japanese, Canadian, and Stanford University researchers have designed a novel computer program that, through listening to samples of speech, was able to identify different categories of sounds without any human guidance. These findings shed light on how human infants learn language.

"In the past, there has been a strong tendency to think that language is very special and that the mechanisms involved are predetermined by evolutionary constraints, and are not very general," says James McClelland, a cognitive neuroscientist at Stanford University who worked on the project. "What we are saying is, Look, we can use a very general approach and do quite well learning aspects of language."

The group of researchers developed the program's software by incorporating features of machine learning into a neural network model. They then recorded the speech of mothers talking to their babies--Canadian mothers speaking English, and Japanese mothers speaking Japanese. The researchers extracted the parameters of the vowel sounds that the mothers were using in their speech and gave their program presentations of samples from the mothers' distribution of vowel sounds. The researchers tested four vowel sounds.

The program was able to bunch together the sounds it was hearing into only a few vowel categories, and it was able to gather the vowel sounds into four categories more than 80 percent of the time. The report appears in the current issue of the Proceedings of the National Academy of Sciences.

The next step is to determine if the program could deal with larger ensembles of sounds in a language, says McClelland. "That will definitely push the limits of the model, and from there we can gain even further insight into how the brain learns."