Samuel Chen is a physics student and an entrepreneur. He graduated from Cornell with a master’s in applied and engineering physics. During his undergraduate years at UCLA, he double-majored in physics and business economics. Samuel has experience as a CEO of a start-up in Los Angeles, California, and an engineer in a semiconductor company in Silicon Valley. His interests include social entrepreneurship, physics, and most recently cellular automata and NKS. Away from his computer, he enjoys snowboarding, skiing, sailing, tennis, and golf. He is married and currently lives in Sunnyvale, California.
Project: Computer Vision with 2D Cellular Automata
Humans can easily recognize and distinguish thousands of visual categories. The best computer algorithms achieve only a fraction of human performance in terms of both the number of classes recognized and the accuracy in distinguishing between those classes. In this study, I have utilized two-dimensional cellular automata (2DCA) as image sensors. I have demonstrated that there exists a class of 2DCA that can filter horizontal, vertical, and 45-degree lines, among other inputs. I have shown in this study that instead of using traditional computer algorithms for tasks such as computer vision, one can use simple programs for these concrete applications.
Favorite Four-Color, Nearest-Neighbor, Totalistic Rule
Rule chosen: 109700
Rule number 109700 in four-color CA: When one runs this rule a few steps, it gives a very repetitive behavior. When one runs this rule for a larger number of steps, starting from a single black cell, it gives a nested behavior. However, when one starts with random initial conditions, this rule shows large grey areas interacting with white areas, which can be taken as phase transition or avalanche. After normally a few thousand steps, those large grey areas will end and the output becomes nested again. To me, this kind of change in behavior represents the essence of “emergence.”