What Are Artificial Neural Networks? (Plus Career Paths)

Updated December 9, 2022

Many industries have increased their use of deep learning to aid in improved business and customer experiences. Deep learning is a form of machine learning that often uses artificial neural networks to process information in artificial intelligence programs. Learning about artificial neural networks may increase your understanding of artificial intelligence and how companies benefit from it. In this article, we discuss what artificial neural networks are, how they function within machine learning techniques, three types of artificial neural networks and nine possible career paths that use them.

Related: What Is Deep Learning?

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What are artificial neural networks?

Artificial neural networks (ANNs) are collections of nodes that mimic neuron behavior in the human brain and aid the development of artificial intelligence. These nodes, or units, work alongside other programs and aid in pattern recognition, signal processing, deep learning and problem-solving. Each node typically contains its own knowledge and can learn independently or alter its behavior after receiving new input. Some technology that uses artificial neural networks includes facial recognition, speech-to-text transcription, predictive text functions and weather predictions.

An artificial neural network includes at least three interconnected layers . The input layer sends data to the deeper layers, collectively named the "neural" layer. The neural layer changes the data through a transformation process after it passes through each inner layer. Each inner layer typically allows the ANN to learn more about specific subjects. The neural layer sends the final data to the output layer.

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How do they function?

Artificial neural networks function by connecting a series of nodes with individual weights and thresholds. The weight determines the importance of a variable and increases or decreases the signal strength at the beginning of a neuron. The threshold is a data or energy level that the node must receive before performing an action. A node activates when it receives an input signal above the node's specific threshold. It then begins sending the data through the next layers of the network and signals connected neurons. Edges, the connections between neurons, often change as the network begins learning.

After the inner layers process the data, they send it through an activation layer, which determines the strength of the output. After a node produces its output data, this becomes the input data for the next connected node. If the output data meets the threshold for the next node, the node begins processing the data and sending it through the next layers of the network. These networks act similarly to decision trees because data proceeds down and through the nodes.

Related: Deep Learning vs. Machine Learning: Learn the Difference

Types of neural networks

Here's a list of three common neural network types and how they process information:

1. Feedforward neural network

This type of ANN is the most simple form because data moves in one direction. Feedforward neural networks operate through a front propagated wave using an activation function. A front propagated wave means data passes through input nodes and exits through output nodes. These networks often feature hidden layers within the nodes. Many scientists use them because they're easily maintainable.

2. Radial basis function neural network

A radial basis function (RBF) measures the distance of a point in relation to a center point. These functions have two layers. The first layer combines the RBF with the individual features of the neuron, and the second layer produces an output. This neural network model classifies different points within a circle into different categories depending on their location to the center point. The location also typically decides the likelihood of the network categorizing a similar data point in that location or class. Some power networks use this ANN to determine the order of power restoration in communities during an outage.

3. Recurrent neural network (RNN)

This ANN saves the output of a node's inner layer and recycles it back to the input layer to assist in predicting the outcome of additional incoming data. This means that sometimes a neuron may remember previous information when processing similar data. If a neuron makes the wrong prediction, it uses error correction to improve future prediction efforts. It uses backpropagation, meaning data can move backward to alter a node's weight. Some organizations use RNNs for services like predictive text that predicts the next word or phrase you may type on your computer or smartphone.

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9 career paths that use artificial neural networks

Here's a list of jobs that typically use ANNs in their job duties. For the most up-to-date Indeed salaries, please click the links below:

1. Test engineer

National average salary: $77,794 per year

Primary duties: Test engineers are professionals who check procedures and machine or computer systems to ensure their most efficient functionality. They often perform tests on components, resolve issues, analyze results, suggest improvements and ensure products meet quality and industry standards. Test engineers may use artificial neural networks to build automated testing systems and optimize existing procedures.

2. Research scientist

National average salary: $78,515 per year

Primary duties: Research scientists are professionals who investigate issues within scientific knowledge by using research strategies and publishing documents through authoritative organizations. They may lead data collection efforts, perform scientific experiments and secure funding for additional studies. Research scientists may use artificial neural networks to assist with data collection or prediction analysis, or they may study them to improve their functions and uses.

3. Applied scientist

National average salary: $87,424 per year

Primary duties: Applied scientists are professionals who perform scientific research and apply their results to improving current technology or solving practical issues in specific industries such as health care or computer engineering. Applied scientists often help businesses improve their functions or services by experimenting, creating prototypes and training machine learning models. They may use artificial neural networks to improve business outcomes such as customer experiences or profits, or they may develop internal tools for organization, fraud detection or classification purposes.

4. Business intelligence developer

National average salary: $92,995 per year

Primary duties: Business intelligence developers are professionals who build computer programs, collaborate with other engineers and use data analytics to seek and share important business information with a client or organization. They often create software tools that companies use to analyze and use potential business strategies or improve upon existing company software. Business intelligence developers may use artificial neural networks to build predictive models for business strategies or organize large quantities of information.

5. Full-stack developer

National average salary: $105,604 per year

Primary duties: Full-stack developers are professionals who manage all the workload of databases, servers, computer systems and individual clients. They often manage both the front end and back end of an application, meaning they work with the visual aspects of a website and the actual infrastructure and features that users don't see. They may write code, test and solve coding issues or work with other scientists to develop or improve the software.

6. Data engineer

National average salary: $117,043 per year

Primary duties: A data engineer is a professional who analyzes and organizes raw data to build data systems for specific organizations and functions. They often evaluate an organization's needs, analyze trends or prepare and build algorithms or prototypes. They may use artificial neural networks and machine learning to build intelligent modeling for data extraction or analytical tools.

7. Software engineer

National average salary: $117,564 per year

Primary duties: Software engineers are professionals who design, test, analyze and modify software programs for both organizations and individual clients. They often develop information systems, investigate potential issues, document solutions and work alongside other engineers on projects. Software engineers may use artificial neural networks to create algorithms, develop predictive models or organize large quantities of data.

Related: Learn About Being a Software Engineer

8. Machine learning engineer

National average salary: $131,001 per year

Primary duties: Machine learning engineers are professionals who research, develop and design various artificial intelligence systems. They often develop machine learning systems, resolve data issues, create algorithms and improve the accuracy of existing AI software. Machine learning engineers often also solve complex problems, run tests and study developments in the field of machine learning.

9. Deep learning engineer

National average salary: $133,755 per year

Primary duties: Deep learning engineers are professionals who specialize in developing computer systems and programs that mimic human brain functions. They may develop AI software for automated predictive models to use in predictive searches, virtual assistants, chatbots and translation applications. Deep learning engineers may also develop machines that work with neural networks to operate without human intervention.

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