If you dip even a toe into the realm of artificial intelligence, you'll come across artificial neural networks. Artificial neural networks are the systems that power artificial intelligence. Neural networks process vast amounts of information to help create an understanding of what's already right in front of you. People think the key to understanding neural networks is calculus, but this system of computing has roots in biology. The human brain inspired scientists who created these systems. So, what better way to explore computing than by thinking about the way we process information?

What Are Artificial Neural Networks?

An artificial neural network is a type of computing system. It's made up of simple processing elements. These elements are also highly interconnected. Interconnectivity lies at the heart of neural networks because it helps the system operate dynamically. The dynamic nature of the system also makes these machines challenging to comprehend. Fortunately, there's a simple comparison available. Artificial neural networks operate on a far simpler plane compared to the human brain, which host billions (at least) of neurons that connect with every single cell in our bodies. A system will only include thousands or hundreds of thousands of connections compared to the billions found in the human brain. Moreover, the processing power is presently far weaker than your cerebral processing power.

How Neural Networks Work: Layers of Learning

Neural networks feature layers and layers of connections. Each layer features an input layer to receive information and output layers to share the information. Hidden layers process all the information in between. For instance, walking down the street, you look down and see a golden retriever. How do you know it's a golden retriever? Well, you were taught to use logic to recognize things. The 'data' presented to you shows that it has four legs. It also has a furry body and a long, feathered tail. His cheerful face is also consistent with what you know to be a golden retriever. Plus, someone at one point taught you the words 'golden retriever', so you have a name for what you're looking at. In the end, your brain processes all these details using logic and comes up with an answer. If it looks like a golden retriever, and it walks like a golden retriever, then it must be a golden retriever. Artificial neural networks work similarly. They receive information, like four legs, and reason based on the information given and what's called a "learning rule." Just as you had to be taught that a big, yellow dog with a happy smile is a "golden retriever" so do networks. A neural network's learning depends on the type of learning it engages in.

How Is This Different from a Normal Computer?

Surely, you think, the processing happening here isn't so far away from the way a conventional computer works. The truth is that artificial neural networks are vastly different in both structure and function. A traditional computer has a central processor that reads instructions given to it from other parts of the machine and the memory. Your computer is merely performing like a trained monkey. If you provide it with information that's new and unknown, you'll get an error. An artificial neural network doesn't feature a central processor. It uses hundreds of simple ones that collect information from other processors. These networks don't follow the rules. They respond to the information given to them. You can see the difference in the dog analogy just used. Conventional computers hold a single processor that follows the instructions it already knows. If your laptop saw a golden retriever, it would only know it was a dog because it already knew it's a dog. Neural networks, however, don't have to worry so much about it. It receives the four legs through one input node. Furry faces get processed by another node. The output rationalizes that we're looking at a golden retriever. As you can see, neural networks operate closer to the way your brain works compared to a conventional computer. It's by no means a complete replica, but the inspiration is clear.

How We Use Artificial Neural Networks

Artificial neural networks are interpreters and approximators. They use prescribed learning rules to reason with the data they receive. Because they're approximators, their best uses are in interpretations that tolerate errors well. These networks to discover patterns. These tools are particularly helpful when the amount of data is too significant for humans to see patterns. For example, medical researchers sort through every single published paper about chemotherapy. They can use neural networks to find patterns and associations beyond the subject of chemotherapy.

Neural Networks Help Us Learn

Artificial neural networks don't create new facts. Instead, they help us make sense of what's already in front of us. Their structure is what enables artificial intelligence, machine learning, and super-computing to flourish.


Terence Mills, CEO of AI.io and Moonshot N.A., is an AI pioneer and digital technology specialist. Connect with him about artificial intelligence on LinkedIn at in/terencemills or Twitter @terence_mills.

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