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Self-Assembly Gets Automated in Reverse of 'Game of Life'
In cellular automata, simple rules create elaborate structures. Now researchers can start with the structures and reverse-engineer the rules.
A direct translation of Life into a neural network would require just 25 parameters, according to simulations done in 2020 by Jacob Springer, who is now a doctoral student at Carnegie Mellon University, and Garrett Kenyon of Los Alamos National Laboratory. This year, Gabriel Béna of Imperial College London and his authors, building on unpublished work by the software engineer Peter Whidden, created algorithms for matrix multiplication and other mathematical operations. Stefano Nichele, a professor at Østfold University College in Norway who specializes in unconventional computer architectures, and his co-authors recently adapted NCAs to solve problems from the Abstraction and Reasoning Corpus, a machine learning benchmark aimed at measuring progress toward general intelligence.
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