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. This approach has been demonstrated 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.
Recognizing the implications of this work, the New York Times said, “Theoretical physicists are not yet obsolete, but scientists have taken steps toward replacing themselves.” But there's a catch. While the computer can discover new laws, will 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?
Hod Lipson is an Associate Professor of Mechanical & Aerospace Engineering and Computing & Information Science at Cornell University in Ithaca, NY. He directs the Computational Synthesis group, which focuses on novel ways for automatic design, fabrication and adaptation of virtual and physical machines. He has led work in areas such as evolutionary robotics, multi-material functional rapid prototyping, machine self-replication and programmable self-assembly. Lipson received his Ph.D. from the Technion - Israel Institute of Technology in 1998, and did postdoctoral work at Brandeis University and MIT. His research focuses primarily on biologically-inspired approaches, as they bring new ideas to engineering and new engineering insights into biology. He has authored and presented over 160 papers, edited three books and contributed chapters to many others. His work has been recognized by a variety of technical award and he is the recipient of the DARPA MTO Young Faculty Award and the Merrill Educator Award, among others. His work has been discussed in the national press, such as the New York Time, and in the popular press. Discover Magazine named his work one of the 25 most important discoveries of 2009. Popular Mechanics awarded his work the Breakthrough Award in 2007. And he was picked as one of Esquire’s Best and Brightest in 2007. In addition, he is an inventor in several patents and the co-founder of two wireless GPS-companies. For more information about Mr. Lipson visit his website at http://www.mae.cornell.edu/lipson.
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