With AIs developing as fast as they are, today let’s look at how a well-made AI can make a decision. In the end, the goal of the AI is to take an input, run whatever process it wants on the data and use the data to make a decision. Neural networks try to emulate actual thinking in the computers (Very Terminator-esque, right?) and can be used to play games or even predict illnesses in hospitals. To do this, the network tries to learn about whatever task by taking in as much input as possible. This input (Whatever you want to tally, from age, to SAT scores, to handwriting) becomes a ‘node’ in the network. Try to imagine actual human neurons. Meet Nathaniel.

Now Nathaniel is a special guy. He’s got a lot of friends who like to tell him stuff. Whenever he gets a signal from some of the other neurons, and the signal is strong enough, he passes it on to his own friends.

In the brain, the connections in the neurons can be weaker or stronger than others, and the stronger the connection between any two neurons, the stronger the signal is passed between them. Similarly, neural networks “pass on” strong data and values, until they arrive at a result that the user likes.

So let’s visualize a fully developed network. In essence, if you’ve ever tried to make a family tree for yourself, imagine that tree, but on its side. Basically, it is layer after layer of nodes that each connect to the next ‘layer’ and so on, until at the end, the network can have output.

When the network gets any input in the input layer (These are the values that we know), it can use them in value functions, called “Activation Functions”, to try and get a value for the input, which is then passed on through the rest of the layers. The function can be either really simple (Kind of a bad AI if it is though…) or extremely complicated. Each node gets a value, and uses the function to get a number. It then passes the number to each connected node, and on the way, it is multiplied by the ‘weight’ of the connection, or the strength of that particular connection.

So, now that we have a design in mind, we can talk about how the network runs. The network basically works backwards, and tries to figure out what it got wrong. The network checks to see what connection made the output wrong, and then tries to avoid that the next time it runs. As a result, it may take multiple runs for the desired output. There also exist multiple types of neural networks. One kind is the “Feed Forward Neural Network” shown here, passing each value through the rest of the network. However, other types of networks, called “Recurrent Neural Networks” pass the data back into themselves. With Recurrent Networks, you can run programs to make sentences or even write books! (The previous word used matters a lot in a sentence, as it decides what the next word will be).

Well that’s pretty much a general summary of networks. If you want, check out Brandon Rohrer’s video explaining Deep Neural Networks. Also check out Code Bullet on youtube to see flash games made easy with AIs. Neural Networks make it so that the computer can make decisions for itself. As a result, we get swarms of numbers and values that would be too big for humans, which the computer deals with by itself. So let’s see where Neural Networks take us in the future. Until then, good luck.

Resources : towardsdatascience.com, askabiologist.asu.edu