AI Explained: A Mental Model of Neural Networks

Over the past decade, “neural networks” (NNs) have transformed from an obscure, purely theoretical concept to Silicon Valley’s hot new buzzword. Now, they’re everywhere. From Google Translate to Tiktok, it’s nearly impossible to go a single day without somehow interacting with neural networks.

But what exactly are they?

Well, for a machine to be (artificially) intelligent, it must have a brain. And that brain takes the form of a neural network.

Together, we’ll walk through exactly how NNs work, assuming no background knowledge of AI whatsoever. Yes, the world of artificial intelligence and neural networks is vast, but upon closer inspection, we’ll see that they’re actually more approachable than meets-the-eye.

Neural Networks by Example

Let’s say we want to build a speech recognition app. (Deepgram knows a thing or two about automatic speech recognition using deep learning, so that’s why we’re sticking to audio for this example. These same core principles can be applied to other types of data too.)

If a user says a word, we want our computer output what the user said. To simplify this example, let’s say there are only four words the user can say: “Reindeer,” “Raining,” “Adhere,” and “Adding.”

So the input (type: waveform) and output (type: string) would look like this:

So while speech recognition is simple for humans, it seems a bit more daunting to program a computer to do this. Computers don’t have ears, so they’ll have to somehow mathematically parse the numerical values expressed in .mp3 or .wav files and turn those numbers into language.