Machine Learning

I took a week off from work to join a deep dive study group on machine learning. It was an incredible experience and I want to tell you about what I learned.

I learned with very smart and interesting people, and I encourage you to check out their work. You can see a list of everyone at the bottom of this article.

This is a birds-eye-view article, so hopefully it will be decipherable by laypersons, while still remaining valuable to anyone who doesn’t know what this field is about.

How should we delve into this incredibly complex topic? How about using the journalistic method of asking the classic five “W” questions: who, what, when, where, and why.

That seems like a successfully pointless structure, so let’s dive in.

What is machine learning

Machine learning is a broad term referring to training a computer using one data set to make predictions about a different dataset, without further human input.

For example, say you had a breakdown of sales for the past decade from a particular Orange Julius. Using machine learning, you might be able to have the machine predict when the most popular sales days will be for the mango pineapple smoothie in the upcoming year.

Why do I say might? In short, because it all comes down to the quality, and quantity, of your data.

In the above example, maybe the weather, or the stock market, or the cycle of the moon influence whether people want a mango pineapple smoothie or a raspberry one.

You need to have as much relevant data as you can to make the system work. If there’s a correlation between people wanting strawberry banana smoothies and the groundhog mating season, and you don’t have that data, guess what? You can’t make that connection.

However, if there is a connection between two data points—aka, groundhog mating and strawberry banana smoothies—you, the human, need to tell the machine about it to make the system go.

At the end of the day, computers are dumb. They literally don’t know their ass from their elbow. Contrary to every movie featuring robots, computers are self aware much the same way a brick is.

A computer is so dumb, it can’t figure out the relationship between data in both directions. This means that if you think there’s a relationship between Guatemalan migratory bird patterns and an uptick in blueberry smoothie sales, and you’re wrong, the computer won’t know! If you plug in that relationship, the machine will happily spit out a number. Never mind that it’s meaningless, the computer did its job.

So it’s our job, as the humans, to really make sure our thought quality is good before ascribing meaning to things. That’s probably a good call in general.

Oddly enough, in all these Orange Julius examples, nobody wants orange.

Why is machine learning

Machine learning exists for the same reason we do anything: first, curiosity, then art, and then finally money. Currently, all three reasons exist in perfect ~~harmony~~ tension.

Where is machine learning

All over the place. Here are just a few examples.

Some of these applications use algorithms like neural networks, deep learning, and others that we didn’t get into in this article. While the specifics (read: math) differ greatly, the basic principles still apply.

When is machine learning

Now, and into the foreseeable future.

Who is machine learning

In short: not many people. It’s a specialty inside (at least one other) specialty. Practitioners need an understanding of programming, statistics, calculus, plus the vagaries of whatever aspect of life they are trying to make predictions on. That’s on top of machine learning specifics.

That’s one of the reasons I wanted to study it. The more people know about machine learning the more we as a society will be able to deal with its consequences.

At least that’s the prediction; I haven’t proved that in a model yet 🤓.

With whom I learned

I’d like to give a big shout out to the super smart and interesting folks I worked with over the week. Here they are, along with links to what they’re up to. I highly encourage you to check them out.