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Artificial Neural Networks and Their Mathematical Theorems

How an Esoteric Theorem Gives Important Clues About the Power of Artificial Neural Networks

Nowadays, artificial intelligence is present in almost every part of our lives. Smartphones, social media feeds, recommendation engines, online ad networks, and navigation tools are examples of AI-based applications that affect us on a daily basis.

Deep learning has been systematically improving the state of the art in areas such as speech recognition, autonomous driving, machine translation, and visual object recognition. However, the reasons why deep learning works so spectacularly well are not yet fully understood.

Hints from Mathematics

Paul Dirac, one of the fathers of quantum mechanics and arguably the greatest English physicist since Sir Isaac Newton, once remarked that progress in physics using the “method of mathematical reason” would

“…enable[s] one to infer results about experiments that have not been performed. There is no logical reason why the […] method should be possible at all, but one has found in practice that it does work and meets with reasonable success. This must be ascribed to some mathematical quality in Nature, a quality which the casual observer of Nature would not suspect, but which nevertheless plays an important role in Nature’s scheme.”

— Paul Dirac, 1939

Portrait of Paul Dirac is at the peak of his powers (Wikimedia Commons).

There are many examples in history where purely abstract mathematical concepts eventually led to powerful applications way beyond the context in which they were developed. This article is about one of those examples.

Though I’ve been working with machine learning for a few years now, I’m a theoretical physicist by training, and I have a soft…



Marco Tavora Ph.D.
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Theoretical physicist, data scientist, and scientific writer.