In his first column for Seed magazine, my Institute for the Future colleague and pal Alex Pang looks at efforts to create software that doesn't just support scientific discovery, it actually does new science. From Seed:
Older 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 â€" you don't need intuition when you have processor cycles to burn â€" but it bears an interesting similarity to the way scientific communities work. (Cornell professor Hod) Lipson says it figures out what to look at next "based on disagreement between models, just as a scientist will design an experiment that tests predictions made by competing theories."
But that doesn't mean it will replace scientists. (Cornell graduate student Michael) Schmidt views it as a tool to see what they can't: "Something that is not obvious to a human might be obvious to a computer," he speculates. A program, says Schmidt, may find things "that look really strange and foreign" to a scientist. More fundamentally, the Cornell program can analyze data, build models, and even guess which theories are more powerful, but it can't explain what its theories mean â€" and new theories often force scientists to rethink and refine basic assumptions. "E=mc2 looks very simple, but it actually encapsulates a lot of knowledge," Lipson says. "It overturned a lot of older preconceptions about energy and the speed of light." Even as computers get better at formulating theories, "you need humans to give meaning to what the system finds."
Why We're Not Obsolete: Alex Pang in Seed
