Thursday, March 17, 2011 at 10:30am to 11:30am
For centuries, scientists have attempted to identify and document analytical laws that underlie physical phenomena in nature. Can this discovery process be automated? Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. A key challenge to finding analytic relations automatically is defining algorithmically what makes a correlation in observed data important and insightful. By seeking dynamical invariants, however, we can go from finding just predictive models to finding deeper conservation laws. We demonstrated this approach by automatically searching motion-tracking data captured from various physical systems, ranging from simple harmonic oscillators to chaotic double-pendula. Without any prior knowledge about physics, kinematics, or geometry, the algorithm discovered Hamiltonians, Lagrangians, and other laws of geometric and momentum conservation. The discovery rate accelerated as laws found for simpler systems were used to bootstrap explanations for more complex systems, gradually uncovering the “alphabet” used to describe those systems. Applications to modeling physical and biological systems will be shown. As the New York Times said, "Theoretical physicists are not yet obsolete, but scientists have taken steps toward replacing themselves."
But wait, there's a catch. While the computer can discover new laws, can we still understand them? Our ability to have insight into science may not keep pace with the rate and complexity of automatically-generated discoveries. Are we entering a post-singularity scientific age, where computers not only discover new science, but now also need to find ways to explain it in a way that humans can understand?