• AIP Team

What is an Artificial Neural Network? (And Why Does it Matter?)

In this post, learn what artificial neural networks are, how ANNs work to process data, and the ways that businesses can utilize powerful AI networks.

At its core, artificial intelligence is designed to mimic the thought processes of the human brain. To accomplish such a weighty task, AI researchers and developers use neural networks to build interlocking connections between sets of data.

Neural networks come in many variations, with use cases that range from basic to incredibly complex. These networks are the cornerstone components to deep learning and machine learning principles. Modern businesses leverage the power of both systems for a variety of applications and operational tasks.

Because there are many versions of neural networks, it can be challenging to understand their unique attributes. In this post, we’ll provide a basic overview of artificial neural networks (ANNs) to help you understand the features, benefits, and potential uses.

What is an artificial neural network?

An artificial neural network (ANN) refers to a foundational component of machine learning and deep learning. Artificial neural networks replicate the nerve response processes of the human brain. As a result, they make up an advanced system of algorithms and processing features that give artificial intelligence its power.

Brief history of artificial neural networks

Although we now interact with more types of neural networks than in years past, the history of artificial neural networks dates back several decades. In the 1940s and 1950s, researchers and data scientists began to develop ideas for how nerve-like networks fit into the world of computer modeling.

The development of neural networks continued to evolve throughout the twentieth century as scientists and mathematicians continued to speculate on deep learning principles. The 1980s saw a boom in artificial intelligence research connected to neural networks, with the first International Conference on Neural Networks taking place in 1987.

Since that time, the advancement of neural networks has happened at an astounding pace. Following the turn of the twenty-first century, artificial neural networks grew from theory, to product, to modern day necessity.

What is an artificial neural network used for?

Artificial neural networks (ANNs) are highly adept at finding and analyzing patterns. Many use cases are designed with this pattern-detecting feature in mind, given that a human programmer could easily overlook computer-based patterns and numbers that are extremely complex.

Modern day applications of artificial neural networks include:

Although these applications are diverse, there’s almost no limit to what an artificial neural network can accomplish. Over time, it’s probable that more advanced neural network adaptations will continue to influence the ways that we communicate and conduct standard business. We are already witnessing these advancements in industries like entertainment, vehicle design, and identity fraud prevention.

How does an artificial neural network operate?

To understand how an artificial neural network works, it’s necessary to grasp the three main components that make it what it is. These pieces are:

  • The input

  • The hidden layer

  • The output

An artificial neural network receives one or more inputs, translates them through the hidden layer, and produces a final result in the form of one or more outputs.

What is the hidden layer?

The hidden layer is a middle point in each neural network. At this step, the network extracts the data and turns it into something that can be properly used.

An artificial neural network can include more than one hidden layer, based on its complexity. If you think about this layer as a conveyor belt, the system should be able to pull new information from the input at each stop along the belt path.

An artificial neural network is akin to a conveyer belt; every input layer should result in an output layer.

Artificial neural networks and AI computing

Artificial neural networks are the building blocks to many artificial intelligence applications. Without this neural framework, developments in machine and deep learning wouldn’t exist.

Because of the way that artificial neural networks learn, they require initial training but progress toward making independent actions and decisions. This learning process makes them extremely helpful for advanced use cases related to communication, language, and imagery.

What are the benefits of an artificial neural network?

The primary benefit of an artificial neural network is that it can mimic the input-output relationship that’s innately present in the human nervous system. Most notably, designers and developers can train an artificial neural network to apply that energy to any project or intended outcome.

Important advantages of artificial neural networks include:

  • The ability to process or perform more than one AI-related task at a time

  • The chance to learn, grow, and adapt each network to scale

  • The opportunity to make future decisions based on acquired inputs or knowledge

How to differentiate ANN vs. other neural networks

There are many different types of neural networks, starting with the most basic form–a feedforward network. In a feedforward network, information and data only goes in one direction, from the input to the output stage. The data never loops back to learn or adapt based on past inputs and experiences.

Other neural networks include:

  • Recurrent neural network (RNN) – data flows in multiple directions, forming a loop

  • Convolutional neural network – inputs are assumed as images

  • Boltzmann machine network – used mostly in statistical mechanics

Each of these variations is a type of neural network used in artificial intelligence, but they have certain qualities that make them more relevant for specific applications.

Concluding thoughts

The development of the artificial neural networks we know and use today is largely due to the research and innovation of scientists throughout the past century. As a result of their curiosity and invention, artificial intelligence applications are a significant part of contemporary culture.

This truth is particularly important for businesses that rely on artificial intelligence for growth, advancement, and innovation. Neural networks make these goals achievable and attainable, which benefits businesses and consumers alike.

If your business needs a hand to find the right AI solution for your goals, connect with our team today. Additionally, the AIP resource center is a great place to find answers to common AI questions and to explore several AI success stories.