Basics of artificial intelligence: What are neural networks and what makes them important?

The creation of new models is just one facet of the AI industry's advancements. But it may be quite daunting to keep track of what's actually happening with every new development. This is a result of the artificial intelligence field's profusion of jargon; words like algorithms, neural networks, and LLMs are are casually and without context.

These explainers will help you better understand what is going on by breaking down some of the most common AI buzzwords and their meanings. In the last installment, we explained two basic ideas: artificial intelligence and machine learning.

This installment will focus on neural networks, their function, and their significance in the AI field. Understanding these networks is essential since they form the foundation of much of modern AI.

What are neural networks?

Neural networks, also known as artificial neural networks (ANNs), can be used to teach computers to analyze data. They are a subset of machine learning (ML) and mimic how the human brain work

Frank Rosenblatt, a psychologist, developed the perceptron, which was essentially one of the earliest artificial neural networks (ANNs), in the late 1950s. The concept was inspired by the way neurons process information.

Rosenblatt used a similar process to build his perceptron, which took numerical inputs and generated a single output. "The perceptron sums up its inputs, and if the sum is equal to or greater than the perceptron's threshold, the perception produces the value 1 (it 'fires'); otherwise, it outputs the value 0 (it 'does not fire')," Mitchell said. This is comparable to the function of a neuron. Each perceptron input is assigned a numerical weight, and each input is multiplied by its weight before being added to the total. The threshold is set by the perceptron itself or by the programmer.

To put it briefly, the perceptron makes a yes-or-no (1 or 0) choice based on whether the sum of its weighted perceptron's threshold, the perception produces the value 1 (it 'fires'); otherwise, it outputs the value 0 (it 'does not fire')," Mitchell explained. This is similar to how a neuron does the same thing. Every input of the perceptron is given a numerical weight, and before being added to the total, each input is multiplied by its weight . This process helps the perceptron to learn from data and improve its prediction accuracy over time.

In short, a yes-or-no (1 or 0) decision is made by the perceptron depending on whether the total of its weighted inputs is greater than a certain threshold.

How do neural networks function?

Neural networks are essentially extended perceptrons. A traditional neural network consists of three layers of artificial neurons, or nodes. An inner layer receives the data first. An inner or hidden layer follows, which takes in data from the input layer and keeps processing it. The output layer, which comes last, shows the results of all the data processing the neural network has completed.

Similar to the human brain, each node in this system has a weight and threshold and connects to other nodes. Data is sent to the next layer of the network by any node that generates an output above the specified threshold value.

In short, a yes-or-no (1 or 0) decision is made by the perceptron depending on whether the total of its weighted inputs is greater than a certain threshold.

Similar to the human brain, each node here has a weight and threshold of its own and connections to other nodes.If a node's output surpasses the specified threshold value, it becomes active and transmits data to the network's subsequent layer. On their website, IBM explains that "otherwise, no data is carried down to the next tier of the network."

Neural networks require a lot of data to be trained for a certain function, like classifying data or making predictions (how researchers train these networks will be addressed in the next explainers). Once the precision of a neural network has been tuned, it can be a useful tool in computer science and artificial intelligence.