Machines and Metaphors

I quite enjoy discussions. However, talking just to people within my friend circle can sometimes become boring. After all, I tend to attract similar perspectives, and to occasionally challenge my own outlook, and see things from another vantage point is something I'd like to do. Events with discussion groups are a great place to do this. But one thing that tends to be a challenge with such events, is who to allocate which group to. Let's say there are 5 discussion groups, and the Google Form you have created allows a prospective participant to select their First-Choice and Second-Choice. How do you decide which groups to allocate to each participant? And this is a more general problem than the obscure idea of a discussion event: it could apply in a virtual conference of educational seminars, or even in class allocations.  The most interesting part about AI, is its ability to express and solve seemingly subjective problems. Yes, I do mean that Simulated Annealing can solve this ...

  I've written all the instructions and code into another  Python Notebook . This will be a more non-traditional application of simulated annealing. We'll implement One More, and then move on to the next algorithm. This is a viewer to see the notebook. Then, you can click Open with Google Colab, Login to your Google Account, and you will be able to edit your own copy of the notebook. If you are doing this, ignore the request in the notebook to make a copy before editing.

I've written all the instructions and code into a Python Notebook . This is a viewer to see the notebook. Then, you can click Open with Google Colab, Login to your Google Account, and you will be able to edit your own copy of the notebook. If you are doing this, ignore the request in the notebook to make a copy before editing.

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...

Machine Learning is quite analogous to the way we learn. One of the most difficult things about learning is preconceptions- on a larger scale, stereotypes. They make it harder to learn something easily, when you have a simple and incorrect idea in mind. ML has not one, but two analogous problems, and this can be very important to make accurate models. So, what are stereotypes? The first way to look at this is that they are training, done from a skewed data set. Let’s take an example. You live in a strange part of the world, where the prevalent flower is a rose. You’ve seen a rose a billion times, and you’ve observed a lot about it: it has thorns, a green stalk and red petals. You know a lot about them. But let’s say that in search of a better job, you set out towards the city. The city seems a bit bewildering, with all the noise and pollution, but the people seem alright, you decide. And then you find the shop. You duck under the huge label that says ‘John’s Floral Arrangements’, and e...

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....