Just as you don’t need to know what exactly goes on underneath the hood of a car to understand how it works, you should be able to get a clear picture of what a neural network does without having to obtain a degree in computer science.

So in this post, we’ll skip all the parts about multilayer perceptrons, backpropagation, activation functions, hyperparameters, and other technical details that form the foundation of artificial neural networks (ANNs).

In simple terms, an ANN attempts to duplicate how a living neural network learns from experience. It consists of a series of mathematical models or equations, referred to as algorithms, that are designed to recognize patterns. ANNs receive a large amount of numerical data which it classifies according to the examples given to it.

Why are ANNs Referred to as Neural Networks?

The concept of ANN finds its origins in biology. Biological neural networks consist of densely packed, layered, and interconnected brain cells called neurons. These neurons pass electrical impulses to each other, creating the sensations we refer to as thoughts.

In parallel, ANNs are computer programs developed to emulate their biological counterparts. Said applications feature computational algorithms that approximate how neurons operate by using a mathematical technique, called matrix algebra, which simulates the interconnections between artificial neurons.

Are ANNs Really Intelligent?

In terms of its ability to learn simple tasks, you may consider ANN as intelligent. It learns by experience from data inputs and improves its performance continuously based on feedback. This in itself is a huge step upward, and what makes the ANN special compared to traditional software.

Indeed, conventional computer programs need to work with specific rules and they cannot function outside of these. For example, a calculator program will compute numbers you feed it according to operations you program into it. If you program it to add, subtract and multiply only, it will not be able to proceed if you ask it to divide. 

ANNs, on the other hand, are not driven by rules but trial and error. They develop an awareness of the rules in this manner, without requiring anyone to program them.

Here’s what we mean.

However, don’t hold your breath waiting for the next Nobel prize to be awarded to ANNs any time soon. The addition of neural networks is only a step to developing cognitive abilities in computing devices.

What are ANNs Used for?

In theory, there is no limit to the applications of ANNs. They can apply to every conceivable human activity. At this stage of its development, they are already coming in handy to spot fraudulent transactions, recognize faces of people in photographs, predict outcomes, recognize speech and natural language and more.

However, our understanding of artificial intelligence and machine learning barely scratch the surface. More work still needs to be done to truly harness the power of ANNs.

Will We Someday See Machines that Can Truly Think like Humans?
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