 So why do we worry about binary anyway? You'll never use it again, right? Well, that's the entire hallmark of computers. Everything that we do on a computer creates what we call data files. You create an image. You create a music file. You create a Word document for your English class. That is a data file. And if we look at something like, say, for example, making an image, well, an image is just a collection of colors. So if I draw a line across here, and I'm just going to say draw a terrible looking oval for a second, and boom, what do you know? Still looks terrible. But why is it that suddenly you can tell the difference between the black background and the blue foreground? Well, what's going on here is actually binary conversion. So if we take a look at that color, what we're actually looking at is a whole slew of different numbers. But the focus I want you to really look in on is this RGB value, this RGB red, green, blue. Now, those numbers only go from 0 to 255, so only 0 to 255. But it's through the combination of these three colors, I can change this from, say, a blue. Notice how if we look at red, green, blue, blue seems to have the biggest number, 185. If I change that, let's drop it down to 100. Now, as soon as I hit Enter, notice how all of a sudden I'm doing a much more green tone. Whereas if I, say, make my red really stand out. Say I give that 241. Say I increase it to 241. Now I have a red. I've got more of a salmon color going on here that I can work from. And it's throughout these combinations of 1s and 0s that I get any particular image. So basically what's getting broken down is we'll just say this tiny, imagine if you will, this is a picture, an image stored on your computer. Now as I continue to draw these things out, I'll stop right about there. Why not? I'll do one more round right down here as well. Well, all these squares I'm drawing out, these are what we call pixels. And every single pixel just stores an RGB value to it. So say, for example, this last pixel right here. If I were to draw that in, I circle, color it in, making sure to stay between the lines, right kids? If I color that in, well, what's happening is the RGB value of that single pixel right now is 241,128,100. Well, because I have three values. So I have these three values. Guess what? Each one is from 0 to 255. I can start to do the binary conversion. I can start to do what is necessary for this microprocessor to understand what changes need to happen. So suddenly, something like 100, it's off the top of my head, it's a 0 there, sorry. It's a 064, 32, doing this off the top. That's suddenly a 96, so 1684. So all of that right there is the binary representation of 100. Don't double check that, or double check that. Make sure I get it right. Otherwise, you can laugh at me and maybe extra credit. You can see I can continue to do this with every single one of those numbers. So that's with graphics. So that's how we make all of our graphics up. But what about something a little bit more complex? What about sound? Sounds a little bit more interesting. So let's say, for example, I make a recording. I say something into a microphone. What gets produced is a wavelength, something like this. And in fact, let's actually take a look at that. This is Audacity. It's a little program that does sort of the exact same thing. If GIMP allowed me to do image manipulation, Audacity allows me to do sound manipulation. So let's say, for example, I do a little recording for a second. Hello, my name is Adam Gawita, and this is Understanding Binary Conversion. So you see on the screen, we've got a large number of just wherever I made a sound. And if we zoom in on that, if we take just a little bit, and I'll just pick an arbitrary, let's take a look at this section right here, you can start to see, we can start to break this down even further. And I'll even do that. I'll break this down even tiny amounts. You can start to see every time I spoke, every subtle nuance in my voice was causing the microphone to shake, which in turn was causing some vibration. And so if we take a look, all of a sudden, we can actually see the subtle sine wave going on there. Now, let's say, for example, let me take this image, and let me put it in GIMP for just a second. So let me zoom in right here. We take a look at this guy right here. What's going on is every single time I spoke. So right about here, we're going to just say right there. Right there, shrink this down a little bit, right there is getting measured. That's actually what's known as my bit rate. And you can see there's a little dot on every single one of these as I continue to go through. Now, every single time I see that little tick mark, what's going on is it's getting measured. And so somewhere down here, for example, that gets measured a little lower. Now, that goes the same way. The same way we did binary conversion for graphics 0 to 255, we do the exact same thing with sound. We start to go, all right, well, if I look at this little tiny square right here, if I draw it out to, say, the edge just ever so, this is a terrible straight line. Let me try that again. It's still pretty bad, but if we draw that out versus this, still terrible. Learn to draw, ladies and gentlemen. But if you take a look at that, you can see that both of these have a measurement that's slightly different. Well, that measurement is actually what happens on the computer side of things. As time increases, the number increases or decreases based on the recording. And it will continue to do that. So we continue to move forward. And as you can see, as I move over, I continue to see, all of a sudden, the progression of just this subtle part of my sound file. And once I've built all this up, I get to see everything as a whole. So that's actually where binary really starts to come into play, not just in our Word documents, seeing an ASCII characterization. So I hope that gave you a little bit more clarification on why we do binary.