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Home » What Makes People Engage With Math: Grant Sanderson (Transcript)

What Makes People Engage With Math: Grant Sanderson (Transcript)

Here is the full transcript of popular Math educator Grant Sanderson’s talk titled “What Makes People Engage With Math” at TEDxBerkeley 2020 conference.

Listen to the audio version here:

TRANSCRIPT:

What Makes People Engage with Math?

I want to ask, what makes people engage with math? We all seem very intent that our children should learn math, that we should learn math, that it somehow puts us in a better position to understand science as technology, and when you have someone sitting there in a classroom, it’s not a given that they’re engaged.

Now I work on a YouTube channel, and on YouTube, this question is put to an unusual level of, let’s just say, an extreme stress test, right? Because if you’re bored with what you’re watching, or you’re debating whether or not to watch something, there are billions of hours of content sitting there waiting for you. Some of the most entertaining things humanity has ever created are sitting there just one click away.

So if you’re trying to teach math on YouTube and someone’s not engaged, they’re not sticking around. So what I want to do is answer this question through a YouTuber’s lens in a way that’s hopefully helpful to more traditional teaching contexts, and I was asked to talk about some of what I do.

So I figured what we would do here is take a look at some of the content that I’ve made that, by the extremely coarse metric of view count, is in a sense more engaging than others. And part of the reason I choose to do this is the four specific videos at the top paint a very interesting picture to answer our question. So sitting at number four was a video about Fourier transforms.

Now this is a beautiful piece of math, absolutely wonderful. The whole idea is about understanding functions in terms of pure frequencies. So when you hear a musical note played, something like the A440 that’s used to tune an orchestra, what the air pressure over time would look like if you were to graph it is something maybe like this yellow graph.

Understanding Fourier Transforms

You know, it’s a pure sine wave, it wiggles at a nice steady rate. Pictures that are higher or lower also wiggle according to pure sine waves but may be faster or slower. Now when you play them all together to get a chord, what happens is that at each point in time, the strength of each of those individual notes is getting added together. So because there are different frequencies, you end up with this very complicated looking graph.

In this case, I’ve only added together four different frequencies but the one at the top, it’s notably more complicated. It’s definitely not a clean sine wave. And the question that Fourier transforms try to answer is how do you do this in reverse? How do you start with a signal that something like your microphone would pick up and reverse engineer what the pure frequencies that went into it are? Now it’s not just relevant for sound engineering.

If you ask any electrical engineer or someone who works with quantum mechanics or all kinds of physics, it turns out that being able to break up functions as pure frequencies is kind of a problem-solving superpower. But if you are bought into the idea that this is a very neat thing to learn, it’s a wonderful, beautiful piece of math, and you go to look it up, what you would find is something that looks like this, which is very intimidating, right? I mean, first of all, there’s an integral there, so you at least need to know calculus. That’s just a bare minimum.

But if you look closely, you see to this e to the pi i stuff, so we’re doing calculus with complex numbers. I mean, the very first line of the Wikipedia page is sort of screaming at you, you need to be at a certain elite level before you’re going to be able to understand this. But if you look past the symbols and the formalisms, it turns out there’s a very nice way to understand what the Fourier transform is actually doing. This isn’t sugarcoating it.

It’s showing what it’s actually doing, but in a very visual way, and this is what I tried to make a video about. I’ll just give you the very high level here. The idea is to take this graph that might be a mixture of different frequencies and sort of wind it around a circle, and to really talk through the details of what’s going on in this animation and how it pulls out the exact frequencies, it takes maybe 10, 15 minutes. It’s not too bad, but it’s a complicated image.

But the idea is that even if you don’t have a deep technical background, you can come to a substantive understanding of what the Fourier transform is doing before you see the calculus and the complex numbers. And at that point, once you bring in the formalisms, it has a way of articulating an idea that’s already in your mind, explaining the usefulness of those calculus and complex number terms. So that’s Fourier transforms. Now if we jump up to number two, I did a video on neural networks, and I think I hardly need to tell anyone in this audience just how useful neural networks have proved to be in the last couple of decades.

The Math Behind Neural Networks

But if you really drill in on what specifically is going on when we reference machines learning, giving them training examples, in what sense is it learning in this context how to recognize handwritten digits, there is a ton of wonderful math to be had in there. And again, it’s highly visualizable. It’s something where you can show what’s happening before you bring in the formalisms of matrix operations and nonlinearities between them and gradient descent and all of that delicious stuff.