What Is an Artificial Neural Network, and Why Does It Matter for AI?

Artificial neural networks (ANNs) are powerful tools in machine learning that are modeled after the structure of the human brain. You can use ANNs in various industries, including finance, engineering, and health care, to make predictions and gain insights based on inputted data. Warren McCulloch and Walker Pitts modeled the first artificial neural circuit in 1943, but it wasn’t until the 1980s that ANNs started to gain popularity in mainstream data operations. Since then, ANNs have exploded in popularity, becoming a popular tool across several industries. 

Explore what ANNs are, how they work, and why they are important in the field of artificial intelligence. Also, discover common artificial neural network examples across industries, careers you can pursue in the field, and the limitations of this emerging technology.

What is an artificial neural network?

An ANN is a type of machine learning algorithm designed to function as an artificial human brain. When you design an ANN, you will be compiling layers of interconnected nodes or "neurons" that process and transmit information, similar to the nerve cells in our brains. You can use artificial neural networks to recognize complex patterns, learn from changing sets of data, and make ongoing predictions in real time.

ANNs are mathematical models that use complex algorithms to determine the strength of each “neuron” and the strength of its relationship to each other neuron. An ANN uses the predicted and actual outputs to improve its function. Data scientists refer to this process as “training.”

You can utilize many different types of artificial neural network machine learning architectures, depending on your area of interest. Some common architectures include feedforward networks, recurrent networks, convolutional networks, and autoencoders. You can use each architecture for specific tasks, such as image classification, natural language processing, or time series prediction.

What is the purpose of an artificial neural network, and what can it do?

When you design an ANN, the primary purpose is usually to design a system that will learn from the data you input and make predictions or classifications based on that learning. You can already use ANNs in many different industries, and this list continues to grow. Some common uses of ANNs seen in industries are:

• Image classification: It’s possible to train certain types of ANNs to recognize objects in images, such as cars, animals, or buildings. Usually, you will use an ANN structure known as a convolution neural network (CNN) for this type of function. You do this by feeding the network large amounts of labeled images and adjusting the weights of the connections until it can accurately identify objects in new images. Electrical engineers, such as those who work with autonomous vehicles, often use image-processing techniques to identify objects such as road signs or pedestrians.

• Natural language processing (NLP): You can use ANNs to analyze and generate human languages, such as text or speech. This artificial neural network example is especially useful when you want your program to be able to read text and hear commands in human languages. Language input can be highly complex, requiring ANNs with more depth than many types of ANN architectures. An ANN architecture commonly used for NLP is deep neural networks (DNNs). You can use this type of technology for voice assistants like Siri or Alexa. 

As a computational linguist, you would explore different ways to build language and text into computational models. When combined with knowledge of ANNs, you can utilize these models to build NLP algorithms.

• Financial forecasting: Within the financial sector, you can use ANNs to predict stock prices, exchange rates, or other financial indicators based on historical data. You can train ANNs to make these predictions by analyzing patterns in the data and adjusting the weights of the network's connections. As a financial analyst, you can use these forecasts to help companies make informed decisions and predict future trends.

• Medical diagnosis: ANNs are useful in the medical field for diagnosing diseases based on images, such as X-rays or MRIs. You do this by training the network on a large data set of labeled images and adjusting its weights to accurately identify signs of disease. As a pathologist, you might use this type of technology to confirm cancerous tissues or irregularities in cell structures. 

By learning to design ANNs, you are building skills that you can use to solve complex problems in a wide range of fields. The ability of ANNs to learn from data and make predictions has led to many groundbreaking applications in AI and machine learning.

Why are artificial neural networks important? 

ANNs are important because they allow you to solve complex problems that you might have difficulty solving through traditional algorithms. Some of the most relevant industries that are currently benefiting from ANNs are:

• Data science: ANNs are beneficial for fundamental data science processes, such as analyzing large datasets and making predictions or classifications based on that data. As a data scientist, you can apply ANN techniques across several industries.

• Engineering: You can use ANNs for computer vision applications, which involve analyzing and interpreting images and videos. As a computer vision engineer, you may be particularly sought after in industries such as self-driving cars and surveillance.

• Customer service: ANNs in customer service use NLP applications, which involve analyzing and generating human language. As an NLP engineer, you may work to specifically design algorithms for use in customer service industries, such as chatbots, email filters, and voice assistants.

• Health care: ANNs in health care are valuable within applications such as medical imaging, disease diagnosis, and drug discovery. In health care, you can use these applications to improve patient outcomes and reduce health care costs. As a physician, you may validate your diagnoses through ANN image processing. 

• Finance: You can use ANNs in finance for applications such as stock price prediction, fraud detection, and risk management. Financial institutions that do so can make better decisions and reduce risk. As a loan advisor, you might use ANNs to determine whether to approve or deny a loan application based on risk.

ANNs are important because they enable machines to learn from data and make intelligent decisions in real time. By learning from data, you can use ANNs to solve problems with changing data and make adjustments as new developments arise. 

Additionally, ANNs are capable of processing large amounts of data quickly, which is essential in industries such as finance, health care, and data science. This technology can be used across several industries to improve safety, reduce risk, and improve daily operations.

Types of artificial neural networks 

When deciding which ANN model to use, you have several options, each with unique features. Some of the most common types you can choose from include:

• Feedforward neural networks: This is the simplest type of ANN, with one or more layers of neurons that you use for sorting and predicting things.

• Recurrent neural networks (RNNs): These ANNs process data sequences, like text or speech, and you can use them in language processing and speech recognition.

• Convolutional neural networks (CNNs): Convolutional neural networks are good at processing images and videos. You can use them for object detection, facial recognition, and more.

• Autoencoders: Autoencoders are especially useful for unsupervised learning, where you can train the network to reconstruct data without a target to aim for.

• Generative adversarial networks (GANs): This type of artificial neural network uses two networks to generate new data that looks like the original training data.

• Long short-term memory (LSTM) networks: These RNNs are useful in speech and language processing and robotics.

Different ANNs will have different advantages and disadvantages, and you can use these features to choose the right one for your specific task. Knowing the differences between ANNs can help you design more effective machine learning models.

Limitations of artificial neural networks 

Understanding the limitations of ANNs can help you work with them effectively and efficiently. When you're working with ANNs, you may choose certain algorithms based on the pros and cons that make them more or less suited for different applications. Here are some of the main limitations you might come across:

• Data size requirements: ANNs need big amounts of labeled data to train well. If you don't have enough data, the network might not have enough input to make accurate predictions. For instance, if you train a CNN on only a few images, it might not be able to recognize new images at a high level.

• Change sensitivity: ANNs can be sensitive to differences in the input data, which can cause mistakes or unexpected results. For example, if you're using a CNN to identify an animal and the animal is hiding or in a weird position, the algorithm might not recognize it. If you use the same data to train ANNs as you do to test them, you may limit the algorithm's accuracy.

• Flexibility limitations: ANNs that you trained for one task might not work well for other tasks or areas. For instance, if you train a CNN to recognize faces, it might not be very good at recognizing things in other kinds of pictures, like landscapes.

Even though these limits can be tough, researchers and developers always try to improve ANNs and find new ways to work around these issues.

How to get started with ANNs

Learning how to build and implement ANNs can help you expand your career opportunities in several industries. Thankfully, several online options exist where you can begin learning this exciting skill. Here are some steps you can take to get started:

1. Learn the basics of machine learning: ANNs are a subset of machine learning (ML), so building a strong foundational knowledge of ML may help you feel more comfortable before diving into ANN architectures. You can take several online courses, such as those in Andrew Ng's Machine Learning Collection on Coursera.

2. Learn TensorFlow: TensorFlow is a widely used deep learning framework that is often used when building ANNs. Taking an introductory course in TensorFlow can help you begin building this skill.

3. Read books and research papers: Many books and research papers are available on ANNs and deep learning. Taking the time to explore different ANN architectures and applications may help you decide which skills will be the most beneficial for your career goals.

4. Practice with projects: Once you have a basic understanding of ANNs, you can strengthen your skills by working on projects. Several beginner-level projects on Coursera are designed to help you gain familiarity with TensorFlow and build neural networks. As you expand your skills, you can take on more complex projects and courses. 

Getting started with Coursera 

Artificial neural networks are already becoming valuable tools across industries and are likely to continue to grow in importance over the coming years. These algorithms make computers increasingly powerful with less need for human assistance, making it possible to solve complex problems not only efficiently but accurately as well.

Explore several course offerings on Coursera to help you learn this exciting technology and build job-ready skills. Consider building your skills with the Mathematics for Machine Learning and Data Science Specialization.