Machine Learning and the Philosophy of Physics

A depiction of the Cost Curve in Machine Learning, with falling blue balls approaching the optimal minimum, where the algorithm predicts the best model.

Science is all about compressing the world. It’s kind of like a .zip file, trying to understand the phenomena and actions of the world around in a few equations that can be scribbled in the corner of a page. As science progressed, and spread, it began, however, to encounter the problem of metaphysics. How could we understand things like joy and happiness, and other abstract things?

These two models, of philosophy and physics, were not easily compatible however. One of the two methods of explaining the world had to dominate. While the two definitely still exist, Physics has since taken precedence. This may not seem the case, but that is because the way I’m thinking of physics is different from the standard understanding. To me, the Philosophy of Physics is the idea that all abstract things, ideas and phenomena are just the result of physical interactions. In other words, these ideas can be both explained and expressed in terms of discrete mathematics.

In many ways, this very same philosophy is expressed as Machine Learning. Intelligence is a very abstract thing to many. The idea that a system could take some input from the world around and give a complex output that represents a kind of individuality. In fact, even defining intelligence here is so hard. What Machine Learning does, though, is that it atleast claims to convert this complicated entity to sets of mathematical processes. This conversion provides incredible power, no matter how imperfect ML may be in its interpretation.

Physics and Machine Learning have much more direct connections than what we were talking about here, but we’ll get to them eventually.

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