Hi, the topic of AI has always been fascinating, and thanks to the recent innovation by companies like Google and ZipMine, showing us the infinite possibilities with this technology, it has risen to become one of the most popular technology within the 21st century. And you might have tried to learn this yourself by watching different YouTube videos or reading different articles, but they obviously, to know where as their comprised of unintelligible cram symbols and math, they are always using a language composed of the English alphabet, but it's clearly not English. So here you are expecting to see the same thing again, and leaving down votes and rage comments on my video, and because of that, I'm going to try to break your expectations. The reason for AI's wide and often magical application is for its ability to make decisions based on its environment, just like how an animal or a human would a nature. This awareness is accomplished by something called Indoor Network, which is a machine that we built to simulate a brain that is capable of decision making. Note that when I see a machine, I mean that it takes in some inputs and converted into some other outputs. So let's take a look at what a simple neural network that you might commonly see on the internet looks like. The first thing that you're probably noticing is that your cursor is currently hovering over the YouTube Home button, for that you have seen the exact same image displayed in front of you for the past two days of reading alien articles. Well please don't click away, otherwise it wouldn't be able to at least try to break your expectations. The second thing that you noticed is probably not so bad, and it is that the structure displayed right now is composed mainly of circles and lines connecting the circles together. Now if you know anything about circles and lines connecting each other, should be immediately thinking about a data tree, otherwise it would be disappointed, just like I am to myself. The third thing that you probably noticed is that this thing does not even remotely represent a machine, as it does not appear to have either a place for inputs or a place for outputs. And you're right, so in order to make this a machine, we have to define what the inputs and outputs are and how they should be represented. Now remember how I said earlier that a neural network is meant to generate an output based on whatever is given to it or its environment. Well this has something like a color to its input, just because it is the first thing that they came up with in my head when I was writing the script for this video. And for its output, we wanted to make a decision based on the input, and we also wanted to be useful to us. So for that, let's make this neural network determine how blue a color is. Now you might ask how is determining how blue a color is any used to us. For that I say, it will help you in understanding how this thing works, hopefully anyways. Now that we have our input and output data determined, and for those who are not paying attention or short-term memories, our input or colors in the outputs are how blue the color is, we need a way to represent these data. Basically turning whatever data that we have into data that a computer can recognize. A color is simple, we can just turn it into an RGB value. And assuming that you don't know what an RGB value is, it is just three numbers ranging from 0 to 255, each representing how red, how green, and how blue a color is. For how blue a color is, we can do the exact same thing like the RGB value, but instead just have one value ranging from 0 to 255 determining how blue that color is. Now that we turn all our data into numbers, let's figure out a way to throw them into the network. One way we can do is by making each of these circles hold a number like a bottle. So for example, we can plug in 100 and we'll hold the number 100. This way we can easily put our input data into it like this. For the output, we can make it also hold a number, but it needs to be determined through the input data and not by us, only if there's some way that they can be connected together. Introducing lines. They connect the circles together and act as direct connection between two circles. Now if you take a closer look at the lines in their neural network, you see that they have little arrow tips on one side of them. This just means that the circle on the arrow is pointing toward is influenced by whatever is on the other side of the arrows. So for example, this circle will be influenced by whatever value resides on this circle. We can also just have strongly this circle can affect this circle by making the connection either weaker or stronger. Back to a neural network. We can see that there are three lines connecting each of the input circles to the output circle. Because we want this network to determine how blue a color is, we can make the connection between the B value and the output stronger. Meaning that the output will be more influenced on it and make a weaker connection on the R and G value, meaning that the output will be less influenced on them. With the lines configured, we have a completed neural network. Now I promise to not show any math in the beginning of this video, so in the next demonstration of me testing this neural network, which involves some math, you're just going to have to trust that I did my math right. First let's input in this color, which is a sort of light blueish color. We convert it into an RGB, pass it in, and let's take a look at the output. The output number is nice and large, meaning that is pretty blue. And now if we put in something like orange and convert it into RGB and pass it in, we see that the output has become pretty small. So that's great. Our simple neural network works as intended. Well, you might be wondering to yourself, how does this prove any use besides for just extracting the number within three numbers? The answer is that the structure is extremely scalable and flexible. Say you want to see how blue the combination of two colors is without mixing them up. You just have to add in some more inputs and some new connections, tune them, and have it work with any color that you throw at it. You can also pass in things that are not colors, things like cloud patterns or images, and have it determine the weather for tomorrow, or what the image is represents. And as the structured complexity of a neural network grows, where there are millions or even sometimes trillions of connections to be tuned, we use special algorithms and complex math such as gradient descent or linear regression to tune those connections. Neither of which I want to even mention in this video because it hurts my head thinking about it. But if you want to see me make a video regarding those topics, leave a comment and I might in the future. Okay, bye.