Machines and Metaphors

As you can see by now, I love analogies. One thing I have begun to realize about Machine Learning is the structure of the ‘learning’ process. In fact, now that I think about it this way, I’m beginning to find analogs in Philosophy, and even in the more traditional Sciences. When you use Machine Learning, you don’t start with fancy algorithms, abstract conjectures or advanced mathematics. You start with data. This could be anything, depending on the nature of the problem you’re trying to solve: populations over years, inflation rates or number of cat videos per day watched by each person in the USA based on different zones or regions. All these( actually, may be not the last one ) have significant applications or derivations that Machine Learning can aid in achieving. But the important idea here is that no rationale can begin without data, at least in terms of AI. Why is this interesting? It reflects a much more fundamental understanding of our scientific or philosophical pursuits in th...

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 phy...

Humans go from guessing to logic, by correction. This is how ML learns. Machine Learning can tend to be a very mystical subject. How a machine can seemingly simulate what is in some way, our greatest ability as living beings- learning- is quite difficult to understand. The best way to do this is by analogy- and that’s what this blog is for. Welcome! This is our first blog, on the philosophy of human learning and how machine learning embodies some part of this. DISCLAIMER: I’m not an expert on Neuroscience. This article is based on my perceptions as a human and a learner.  Learning to talk was probably a pain. I definitely don’t remember it, but watching children around me makes it pretty clear. Relearning a language for school has only accentuated that experience.  But how it happens is very interesting. Here’s how it starts: Someone tells you a couple of rules for the language. Some words are verbs and others are nouns, and if you don’t want to be boring, please use pronouns....