You find yourself at another startup meetup, leaning against the bar and sipping away at your drink. Tonight’s the night, you think to yourself. Tonight, I get some networking done.

And then you hear it. A fragment of conversation floats over to your ears:

“…they’re definitely the machine learning startup to watch.”

No…

“Don’t they still use statistical NLP though? Recurrent neural nets seem like the way forward…”

Not again…

And like a virus, it spreads. You watch in horror as, within minutes, everyone at the bar is talking about machine learning.

You’ve heard the term before, of course. Who hasn’t? Machine learning is the way forward, the future of artificial intelligence. Of course it is.

Trouble is, you don’t have a damned idea what any of that means.

You down your drink. It’s going to be a long night.

The end of the world

First, we hear that machine learning bots are taking our blue collar jobs.

Then, we find out white collar jobs aren’t safe either.

Resigned to our jobless future, we find out that the bots are now talking behind our back.

Gossiping about us lazy unemployed humans, probably.

Machine learning is talked about in so many different contexts that it can be hard to grasp what exactly it is. You look it up and get abstract theoretical explanations, high power Scrabble words, and a wall of math and code.

You just want to know what exactly machine learning is, why it’s a big deal, and maybe a bit about how it works. Honestly, you just want to make sense of all the buzzwords that get thrown around, like curse words in a middle school classroom: everyone’s using them, but you have a feeling nobody else knows what they mean either.

Let’s dive in

So, machine learning. Is that like… AI?

That’s a good place to start. We all know what artificial intelligence means. I mean, we’re all thinking of different things, but surely it’s the thought that counts.

Artificial intelligence (AI) is the study of building systems that can make “intelligent” decisions.

Basically, if a computer does something that seems somewhat smart, we label it artificial intelligence.

Let’s use an example you might have come across. Computer games often feature enemy characters who appear intelligent. They follow us around, and they act in ways that make the game a challenge. That’s an example of AI.

The developer of the game has achieved this by giving the AI a set of rules. Follow the player. If the player is shooting, find cover. If the player stops shooting, try to shoot the player. The more of these rules there are, the more intelligent the game appears to be.

Thing is, a computer game is usually pretty limited. The player can only perform a few specific actions, and the level was entirely designed by the developer. So, developers can come up with rules for great AI characters that seem really intelligent.

Well, pretty intelligent.

Not all problems can be solved with rules

Say we want a computer to detect if this is a picture of a dog.

How would we even start defining rules for this task?

No, really. Try to think of the kinds of rules we need.

Dogs have four legs?

Dogs are white in color?

Dogs have fur?

This one already knows “sit”.

Computer vision problems, like recognizing an object, are really complex. But our brain solves them almost intuitively. So, it’s really hard for us to come up with explicit rules.

Enter machine learning

We don’t build a system that recognizes dogs. We build a system that can learn to recognize dogs.