Machines and Metaphors

Introduction: Now that the strange title has attracted you to the article, StOp stands for Stochastic Optimization. This is the first episode in our mini-series. I've been mulling over this article for months now, which is kind of absurd considering that this is meant to be a quick series, but I apologize for my online dormancy. In the meanwhile, I was working on writing content for a course on Machine Learning. If you're still in school (not college), and you want to learn more, check out:  https://code-4-tomorrow.thinkific.com/courses/machine-learning At any rate, let's get started. Expansion and Exploitation: In some ways, the more of this you read about, the more you begin to think of the world as an array of optimization processes - from the bargain you settle on with the grocer to the conversation you had before you sold your company. But an unfortunate side-effect of this kind of outlook, is that you often become a visibly more selfish person. You spend more time exp...

Phase Spaces One of the least heard of, and most interesting techniques of the sciences, that you rarely realize you’ve used before. Phase spaces are symbolic representations of a particular problem, which you can then use to solve it. Let’s start with a simple problem - in physics maybe. Let’s say we have a car, as all good physics problems do. You’re driving at a set initial speed, and a set acceleration. At what time would you have travelled exactly 15 ft? Let’s look at it in terms of "a phase space". I have a velocity-time graph down here:                                                                                                                                ...

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...