Why neural networks struggle with the Game of Life
This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence.
The Game of Life is a grid-based automaton that is very popular in discussions about science, computation, and artificial intelligence. It is an interesting idea that shows how very simple rules can yield very complicated results.
Despite its simplicity, however, the Game of Life remains a challenge to artificial neural networks, AI researchers at Swarthmore College and the Los Alamos National Laboratory have shown in a recent paper. Titled, “It’s Hard for Neural Networks To Learn the Game of Life,” their research investigates how neural networks explore the Game of Life and why they often miss finding the right solution.
Their findings highlight some of the key issues with deep learning models and give some interesting hints at what could be the next direction of research for the AI community.