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AI Is Discovering Its Own ‘Fundamental’ Physics And Scientists Are Baffled

Published: August 6, 2022 | Print Friendly and PDF


At Lipson’s Creative Machines Lab, he and colleagues want to better understand how this process of discovery takes place and how it can be improved upon using machine learning to uncover hidden, alternate physics that human scientists may have missed. 

To do this, Lipson and colleagues have designed a machine learning algorithm capable of studying physical phenomena by “watching” videos, such as the swing of a double pendulum or the flicker of a flame, and producing the number of variables needed to explain the action. For known systems, the algorithm was able to predict the correct number of variables within 1 value (e.g. 2.05 variables to describe a single pendulum instead of  2) and even make variable predictions for unknown systems. The findings were published last week in a study titled “Automated discovery of fundamental variables hidden in experimental data” in the journal Nature Computational Science.

While this algorithm is not the first to study data and try to extract a physical relationship from it, Lipson says that this work stands apart because it is the first to not provide the algorithm with any information on the number or type of anticipated variables in a system. Because of this, the system is not restricted to look for variables through only a human lens, which Lipson says could be crucial for uncovering hidden physics within these systems. 


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