Woman power for AI and Information Extraction

A mathematical theoretician, aged 39  she has made contributions in areas like robotics and biology and is creating a set of computational tools for artificial intelligence that can be used by scientists and engineers to do things like predict traffic jams, improve machine vision and understand the way cancer spreads.

Koller’s work, build son an 18th-century theorem about probability, has already had an important commercial impact, and her colleagues say that will grow in the coming decade. Her techniques have been used to improve computer vision systems and in understanding natural language, and in the future they are expected to lead to an improved generation of Web search.

Described as “On the bleeding edge of leading edge” by machine vision researcher  Gary Bradski, she was honoured in 2004, receiving a $500,000 MacArthur Fellowship.

Koller is seen as part of a current revival of interest in artificial intelligence. After three decades of disappointments, AI  researchers are now making progress. Recent developments have made possible spam filters, Microsoft’s new ClearFlow traffic maps and the driverless robotic cars that Stanford teams have built in the  competitions sponsored by the Defense Advanced Research Projects Agency.

Since arriving at Stanford as a professor in 1995, Koller has led a group of researchers who have reinvented the discipline of artificial intelligence. Pioneered during the 1960s, the field was originally dominated by efforts to build reasoning systems from logic and rules. Judea Pearl, a computer scientist at the University of California, Los Angeles, had a decade earlier advanced statistical techniques that relied on repeated measurements of real-world phenomena.

The Bayesian approach, this centers on a formula for updating the probabilities of events ,based on repeated observations and was named for the 18th-century mathematician Thomas Bayes, describing how to transform a current assumption of an event into a revised, more accurate assumption after observing more  evidence.

Koller has led research that has greatly increased the scope of existing Bayesian-related software. “When I started in the mid- to late 1980s, there was a sense that numbers didn’t belong in AI. People didn’t think in numbers, so why should computers use numbers?”

She is beginning to apply algorithms more generally to help scientists discern patterns in vast collections of data. “The world is noisy and messy,” she said. “You need to deal with the noise and uncertainty.” Her philosophy has led her to do research in game theory and AI, and more recently in molecular biology.

Her tools led to a new type of cancer gene map based on examining the behavior of a large number of genes that are active in a variety of tumors. From research, scientists were able to develop a new explanation of how breast tumors spread into bone.

One potentially promising area to apply Ms. Koller’s theoretical work will be the emerging field of information extraction, which could be applied to Web searches. Web pages would be read by software systems that could organise the information and effectively understand unstructured text.

Koller, is the daughter of a botanist and an English professor. While her father spent a year at Stanford in 1981 when she was 12, she began programming on a Radio Shack PC that she shared with another student. By 15 she had entered Hebrew University, where she studied computer science and mathematics.
By 17, she was teaching a database course at the university.

She didn’t spend her time looking at a computer monitor. “I find it distressing that the view of the field is that you sit in your office by yourself surrounded by old pizza boxes and cans of Coke, hacking away at the bowels of the Windows operating system,” she said. “I spend most of my time thinking about things like how does a cell work or how do we understand images in the world around us?”

Source: http://www.nytimes.com/
Web: http://ai.stanford.edu/~koller/