I've got a new short article at Seedmagazine.com, on automated scientific discovery and the sociology of knowledge. Sounds fascinating, I know, but it really is a better read than I make it sound.
In a recent article in Science, Cornell professor Hod Lipson and graduate student Michael Schmidt described a new computer system that can discover scientific laws. At first glance, it looks like a fulfillment of the dreams of “computational scientific discovery,” a small field at the intersection of philosophy and artificial intelligence (AI) that seeks to reverse-engineer scientific imagination and create a computer as skilled as we are at constructing theories. But if you look closer, it turns out that the system’s success at analyzing large, complicated data sets, formulating initial theories, and discarding trivial patterns in favor of interesting ones comes not from imitating people, but from allowing a very different kind of intelligence to grow in silico — one that doesn’t compete with humans, but works with us….
lder AI projects in scientific discovery tried to model the way scientists think. This approach doesn’t try to imitate an individual scientist’s cognitive processes — you don’t need intuition when you have processor cycles to burn — but it bears an interesting similarity to the way scientific communities work.
Though I have to give credit where it's due: if it turned out well, it's because it's a great project, and several people were very generous with their time, talking me through its details, and speculating on what the project and this approach to automated scientific discovery could mean for the future of science. I should never be amazed that people are almost always willing to talk about their work and what makes it interesting, but I never fail to be. Remember that when I call you!