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The state of the art in machine conversation: HAL’s still pure Hollywood

Human-machine interactions have mostly been a one-way street: the computer …

Anyone who has followed science fiction will remember the soothing voice of HAL, the computer that methodically killed all but one of the crew of the space ship it controlled. Perhaps the most compelling scene in the movie is the conversation that HAL had with the lone survivor as he inactivated the computer. The scene was notable largely because the conversation seemed so normal; HAL's end of it seemed positively human. A perspective in today's issue of Science takes a look at the prospects of creating computers that can mange this feat, and engage in natural-sounding conversations.

The perspective is authored by Yorick Wilks, a professor of computer science who directs the Institute for Language, Speech and Hearing at the University of Sheffield. Wilks provides a bit of the history of human-computer conversation, mentioning the chatterbot ELIZA, which restructured input sentences and shot them back to the user, coming across as a mildly-deranged therapist in the process. Wilks gives higher praise, however, to an amusing contemporary of ELIZA's called PARRY: "PARRY ran on the early ARPAnet at Stanford in the late 1960s and was designed by Kenneth Colby, a psychiatrist who wanted to model paranoiacs and their beliefs. PARRY was paranoid about the Mafia, horse races, track betting, and Italian-Americans; if anything you typed could be linked to them, it would spew out a paragraph of invective."

Wilks himself reserves much of his invective (couched moderately in academic language) for Noam Chomsky. He blames Chomsky's focus on identifying the basic rules and logic of language for dominating the attention of those working in the field. From this perspective, the excessive focus on core logic distracted linguists and computer scientists from following up on the ideas behind PARRY, which Wilks presents as "big data + small theory."

Instead of attempting to base models of human conversation on theories regarding the basic rules of language, successful models are now focusing on a statistical analysis of large samples of spoken and written conversations. This is essentially the same approach used by Google's new translation service, with reasonably good results. Using this method, computers can "teach" themselves language usage by performing a statistical analysis of language in action; a few simple rules act to focus the results, instead of serving as the intellectual foundation of the exercise.

While the statistical models are improving, Wilks also points out two further limitations on human-computer interactions. The first is voice recognition on the computer's part, which, as Nate Anderson will attest to, still suffers from annoying glitches. The second is that neither of statistical models nor speech recognition manage the biggest challenge of natural human conversation: a recognition of personal and general history. ELIZA, at its best, would only parrot the most recent inputs from its conversation partners. Real humans recognize things like the content of prior conversation, current events, and cultural context when engaging in conversation. In Wilks' example, humans can recognize when a person makes a statement that contradicts an earlier one, representing a change of opinion—or possibly a joke.

The perspective suggests that advances are being made on all three fronts, and the first fruits of the work may appear soon, possibly as a result of DARPA's CALO project. These first products will focus on recognizing the state and history of individual users. It's a far cry from HAL's general conversation abilities, but at least we'll have more time to work on avoiding endowing these assistants with HAL's homicidal tendencies.

Science, 2007. DOI: 10.1126/science.1148895

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