Big data—and big processing power—is a big deal for science. By crunching massive amounts of data billions of times faster than could be done by hand, computers have allowed scientists to discover faraway planets, unravel our genetic code, and even find the subatomic particle responsible for gravity. But imagine a future in which computers don't just use their awesome power to help scientists. Imagine a future in which computer can come up with useful scientific ideas and hypotheses all on their own.
Well, that just happened. As they report in the science journal PLOS, Michael Levin and Daniel Lobo, two computer scientists/biologists at Tufts University, have programed a computer that independently created its own scientific theory. It's one that may solve a 120-year-old mystery in biology that has eluded even our best explanations: exactly how the genes of a sliced-up flatworm conduct its symphony of cells when they regenerate into new organisms.
Simply put, Levin and Lobo's computer attempted to mimic real-life studies over and over again in an excruciatingly-detailed simulation. The machine would randomly guess how the worm's genes formed a regulatory network that allowed for this amazing regeneration, then let that genetic network take control in a simulation, and finally measure how close the results were to real experimental data. If its guesses were good (meaning the gene network made the simulated worm regenerate similarly to real-life experiments), then the machine slightly modified the random genetic network it had created, and tried again until its model was even better.
Although the computer needed just three days to solve the flatworm genetics problem, Levin says that it took years and years to design and prepare the computer program. For one thing, the duo had to track down hundreds of scientific experiments performed on flatworms just to translate those experiments into a massive database for the computer—essentially giving the machine rigidly structured raw material. They even had to devise a personalized formal computer language that fit the data they needed to describe.