Feminism takes many forms. The picture on the left was published as a cover of Redbook. The picture on the right is the "raw picture" -- the pre-photoshop actual photo. It was purchased for $10,000 by a woman-run blog called Jezebel for the purpose of this side-by-side. Why? Because they've got a grudge against this Faith Hill lady? No. Because we've all been living in this world and looking at these images for so long, our brains have come to accept the image on the far left as normal. And it takes the image at near-left to remind us that the first one doesn't even look human, let alone normal. It's like having to be reminded that no one actually looks like anime or Disney "princesses." Our minds are warped and this unwarps them a little. And by the way, the woman in that unimproved picture? She is seriously pretty. Prettier than anyone I've bumped into in the last couple months. Why do we prefer looking at the alien creature on the left?
The New York Times has a piece about some of the latest advances being made in artificial intelligence. AI is important not just to creating the evil cylons that will turn into super-hot monotheistic cylons someday, but to making computers work better. We won't be able to just ask computers questions and get decent answers -- Star Trek style -- until those computers can think. So AI is important. And some of the most important work going is being done by a woman, Daphne Koller:
I'm actually kind of impressed that the author of this article did not find Ms. Koller's sex remarkable. At no point in this story does the article make any note at all of the fact that she is a woman working in a field that is usually regarded (stereotypically and stupidly) as "male."
A mathematical theoretician, she has made contributions in areas like robotics and biology. Her biggest accomplishment — and at age 39, she is expected to make more — 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.
Ms. Koller’s work, building on 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.
“She’s on the bleeding edge of the leading edge,” said Gary Bradski, a machine vision researcher at Willow Garage, a robotics start-up firm in Menlo Park, Calif.
I love that she's more inclined towards numbers than most -- further putting the lie to the "girls can't do advanced math/science" canard. But again, I'm the one pointing this out; the article doesn't even notice she's a woman or that there's anything noteworthy about that. I think that's wonderful and very optimistic, but are we there yet? No, we're not there. So I hold up Daphne Koller as the feminist hero of the day. Hurray Ms. Koller! You are awesome.
Since arriving at Stanford as a professor in 1995, Ms. 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.
Called the Bayesian approach, it centers on a formula for updating the probabilities of events based on repeated observations. The Bayes rule, named for the 18th-century mathematician Thomas Bayes, describes how to transform a current assumption about an event into a revised, more accurate assumption after observing further evidence.
Ms. 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 A.I.,” she said in a recent interview. “People didn’t think in numbers, so why should computers use numbers?”
Ms. Koller is beginning to apply her algorithms more generally to help scientists discern patterns in vast collections of data.
“The world is noisy and messy,” Ms. Koller said. “You need to deal with the noise and uncertainty.”