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DOI: 10.1145/1400181.1400205

Leah Hoffmann

Q&a
a complex thinker

Daphne Koller discusses probabilistic relational modeling, artificial intelligence, and her new work with biologists.

IN APRIL, DAPHNE Koller, a professor of computer science at Stanford University, received the first-ever ACM-Infosys Foundation Award in Computing Sciences for her groundbreaking approach to artificial intelligence.

How did your interest in relational
logic and probability first develop?

What really sparked my interest was when I went to UC-Berkeley for my post-doc, and I realized how useful probabilistic modeling methods are— techniques like Bayesian networks— but, on the other hand, how brittle they are.

Brittle?

[Probability is] a language that’s very restricted in its expressive power because it can only refer to specific, concrete entities.

At Berkeley, I remember speaking with a graduate student who was building a model for vehicle traffic on a freeway. He had this wonderful model that was getting great results, but it only applied to a three-lane freeway. If he wanted to add another lane, it would take him two weeks to do the model.

And that’s where logic entered the
picture.

The language of logic allows you to come up with much more general rules for describing the properties of objects and their interactions with each other. It’s a good way of extending the power of probability to something that’s more expressive and forceful.

That would be the synthesis known
as probabilistic relational modeling.

What we did is take this language of

objects and relations and add the ability to make probabilistic statements, which enables you to represent probability distributions over networks of interrelated individuals.

It also enables you to create mod-
els of considerably more complex sys-
tems.

Yes, and that’s been a focus of my work ever since. The world is very complex: people interact with other people as well as with objects and places. If you want to describe what’s going on, you have to think about networks of things that interact with one another. We’ve found that by opening the lens a little wider and thinking not just about a single object but about everything to which it’s tied, you can reach much more informed conclusions.

Which was an insight you brought
to the field of artificial intelligence…

Well, I wasn’t the only one involved. There had been two almost opposing threads of work in artificial intelligence: there were the traditional AI folks, who grew up on the idea of logic as the most expressive language for representing the complexities of our world. On the other side were people who came in from the cognitive reasoning and machine learning side, who said, “Look, the world is noisy and messy, and we need to somehow deal with the fact that we don’t know things with certainty.” And they were both right, and they both had important points to make, and that’s why they kept arguing with each other.