Abstract

Can robots discover scientific laws automatically? Despite the prevalence of computing power, the process of finding natural laws and their corresponding equations has resisted automation. This talk will outline a series of recent research projects—starting with self-reflecting robotic systems and ending with machines that can formulate hypotheses, design experiments, and interpret the results—to discover new scientific laws. We will see examples from psychology to cosmology, from classical physics to modern physics, from big science to small science.

For additional information:

Schmidt M., Lipson H. (2009) “Distilling Free-Form Natural Laws from Experimental Data,” Science, Vol. 324, no. 5923, pp. 81 - 85. 

Bio

Hod Lipson is an associate professor in the School of Mechanical and Aerospace Engineering and Computing and Information Science department at Cornell University in Ithaca, New York. He directs the Creative Machines Lab, 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 3D printing, machine self-replication, and programmable self-assembly. Lipson received his Ph.D. from Technion – Israel Institute of Technology in 1998, and completed postdoctoral appointments at Brandeis University and MIT. His research focuses primarily on biologically inspired approaches, as they bring new ideas to engineering and new engineering insights to biology.