‘Rich Interaction’ May Make Computers A Partner, Not A Product

In the movie “2010,” while trying to salvage the mission to Jupiter, the Hal 9000 computer noted, “I enjoy working with human beings, and have stimulating relationships with them.”

Well, 2010 is just around the corner, and as usual Hollywood was a little ahead of its time – but in this case, not by much. Oregon State University researchers are pioneering the concept of “rich interaction” – computers that do, in fact, want to communicate with, learn from and get to know you better as a person.

The idea behind this “meaningful” interaction is one of the latest advances in machine learning and artificial intelligence, in which a computer doesn’t just try to learn from its own experiences, it listens to the user, tries to combine what it “hears” with its internal reasoning, and changes its program as a result. When ordinary users spot the machine’s errors they should be able to step in and explain directly to the machine the logic it should be using.

“There are limits to what the computer can do just by its own observations and efforts to learn from experiences,” said Margaret Burnett, an associate professor of computer science at OSU. “It needs to understand not just what it did right or wrong, but why. And for that, it has to continue interacting with human beings and make constant changes in its own programming, based on their feedback.”

OSU researchers say that many advanced learning systems begin learning the moment they are delivered to an end user’s desktop in an effort to customize themselves to the end user. Systems like this are the basis of spam filters on personal computers, e-mail sorting, product recommendation – “If you liked this book, here’s another one you might find interesting.”

A lot of these systems are based on word statistics, set rules, similarities, and other such approaches. But even the most advanced systems only allow a user to tell the computer something is right or wrong. The user is never asked to explain what the real problem is.

Consider the Nigerian money scam, a form of spam so common that almost everyone who ever used email has gotten a plaintive query from someone who just lost their rich uncle and has several million dollars tucked away, needing only a helpful and discreet friend to get it to a safe bank. Help me out and we’ll split the money, your new “friend” implores.

Now, this scam is so pervasive that most computer spam filters will immediately spot it and send it to junk mail, perhaps because it saw the words “Nigeria” and “money” in the same message. But what if the recipient regularly received email from a friend in Nigeria who is a legitimate banker? That’s not spam, but how do you explain that to the computer?

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