The idea has been kicked around a bit. I see Gary Marcus is one of the authors. have much enjoyed his book on the current limitations of AI. See my review at the tag below. So the answer is if we include a rather narrow range of predictions, based on lots of data, we might get some predictive things that look like 'laws'. But will they be useful broadly, testable in broader contexts?
Are Neural Networks About to Reinvent Physics?
The revolution of machine learning has been greatly exaggerated.
By Gary Marcus and Ernest Davis in Nautil.us
Can AI teach itself the laws of physics? Will classical computers soon be replaced by deep neural networks? Sure looks like it, if you’ve been following the news, which lately has been filled with headlines like, “A neural net solves the three-body problem 100 million times faster: Machine learning provides an entirely new way to tackle one of the classic problems of applied mathematics,” and “Who needs Copernicus if you have machine learning?”. The latter was described by another journalist, in an article called “AI Teaches Itself Laws of Physics,” as a “monumental moment in both AI and physics,” which “could be critical in solving quantum mechanics problems.”
The trouble is, the authors have given no compelling reason to think that they could actually do this.
None of these claims is even close to being true. All derive from just two recent studies that use machine learning to explore different aspects of planetary motion. Both papers represent interesting attempts to do new things, but neither warrant the excitement. The exaggerated claims made in both papers, and the resulting hype surrounding these, are symptoms of a tendency among science journalists—and sometimes scientists themselves—to overstate the significance of new advances in AI and machine learning.
As always, when one sees large claims made for an AI system, the first question to ask is, “What does the system actually do?” .... "
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