The job of a scientist has its fun parts, and its not-so-fun parts. Making new discoveries, understanding the way things work, and experimenting with the natural world are all pretty cool ways to spend your day. Sifting through endless files of data looking for small correlations and insight…not so much. Which may explain the popularity of the new software from Cornell Computational Synthesis Lab called Eureqa. Toted as something of a virtual scientist, Eureqa finds hidden mathematical relations in large spreadsheets of data. The software uses a technique, symbolic regression, that slowly evolves equations over time to see which best fits the information you give it. How powerful is Eureqa? Well it can derive Newton’s Second Law from the motion of a pendulum without any input on the physical laws of mechanics in just a few hours. So it has Newton beat by several years. Other researchers are hoping to have Eureqa find the mathematical relations in their own work which is much more complicated than simple Newtonian physics. If successful, Eureqa could not only speed up scientific research, it could change the roles humans take in science. Check out video tutorials for Eureqa after the break.

Eureqa examines data from an experiment, and produces equations that explain what happened. Sounds like a scientist to me.
Eureqa is free to download and free to use and scientists are eager to give it a try. At this moment, it’s just another interesting software tool that will help researchers make new discoveries. In its success, however, are the roots of a much larger change. Programs like Eureqa could one day take over a large part of scientific work. Data analysis is a key task in any modern lab, and is the core service provided by many auxiliary companies working in major industries. Did you perform a geological survey, do some market research, or study the stars? Chances are you employed a data analyst or an entire firm of them. Now, programs like Eureqa are on their way to improving that analysis, and one day reducing the number of humans needed in the process. We’ve already seen software that can mimic the work of journalists, now it seems scientists can be automated to some degree as well. Sure, this could have some negative consequences (which we’ve discussed with Martin Ford’s recent book), but it’s also going to be amazingly helpful. The quicker we find the underlying mathematical equations for a phenomenon, the quicker we can learn how to harness it for everyone’s good.
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