In the tech-driven workplaces of Industry 4.0, terms such as generative AI, machine learning, and neural network have become almost like buzzwords. Business leaders are enticed on all fronts with promises of more efficient and productive operations through cutting-edge technology.

Yet, despite the widespread use of these terms, few truly understand how the underlying technology works. What is under the hood of enterprise AI? What’s making it happen? With generative AI, much of it comes down to a foundation built with neural networks.

In this article, we will take a deep dive into neural networks and how they work, plus look at some proven use cases across various industries. We will also explore some limitations of neural networks, and the challenges adopters may face. Consider this a primer on neural networks for business leaders and decision-makers designed to help them better understand the modern enterprise tech landscape.

What is a neural network?

At its core, a neural network is a computational model designed to mimic the human brain. In the brain, neurons fire when stimulated, passing signals to other neurons and forming neural patterns in response to specific inputs.

An artificial neural network works in a very similar way. It comprises multiple layers of nodes (the neural network equivalent of neurons) that process and transmit information to each other. This allows data to flow through the network in a structured way, enabling the various pieces of its AI architecture, such as transformer models, to recognize patterns and make predictions.

Each node receives inputs and processes them. The output is then passed to the next layer, creating patterns similar to those in the human brain. Using this technique, neural networks can learn, make decisions, and generate predictions.

Neural networks are capable of processing large unstructured datasets of different formats—including text, sound, and images—without relying on traditional programming or detailed instructions. Instead, they learn by adjusting internal weights and biases, which enable neural networks to make accurate predictions, making them a foundational technology for modern AI.

Neural network types

Although they share similar capabilities, different types of neural networks operate in different ways. Each has a distinct architecture that is appropriate for particular data types and tasks. The most common ones are detailed below.

Feedforward neural networks

Feedforward Neural Networks (FNNs) are the simplest type of neural network. In an FNN, data only moves from the input layer, and down through the hidden layers to the output layer, in a single direction.

In an FNN, data doesn’t “look back”. Instead, it transforms as it flows through each layer, from the input layer to the output layer. This makes FNNs easier to understand and implement than other types of neural networks. They are particularly useful for tasks such as:

Text classification, which means sorting text into pre-established categories, like sentiment analysis in customer reviews or spam detection in emails.
Regression analysis, which involves making predictions about continuous values, like stock or home prices, by using specific features of the data at hand.

Convolutional neural networks (CNNs)

Convolutional Neural Networks (CNNs) are generally used for processing spatial data, such as images and video. They are designed to take input data and learn adaptively by recognizing spatial hierarchies—such as edges, textures, and shapes—contained within it.

CNNs are unique in the way their convolutional layers work. Each layer applies one or more filters to the data, scanning a certain limited region. This can be used to uncover features such as the edge of images or shapes within an image. Alongside the convolution layers, a CNN may use pooling layers, like max pooling or average pooling, which help to simplify the data and make the network faster. These also minimize the risk of overfitting, which happens when the model learns too much from the training data and struggles with new data. CNNs suit tasks such as:

Recognizing and classifying images
Analysis of medical images, such as X-rays or MRI scans
Facial recognition
Detecting objects in an image

Recurrent neural networks (RNNs)

Recurrent Neural Networks (RNNs) are more complex than FNNs or CNNs, and are best used for handling time-dependent and sequential data—that is, data where the order of events matters. RNNs have cyclic connections, which means that data can flow both ways through the layers; it can loop back, allowing the network to retain memory of previous inputs. This makes RNNs particularly effective for tasks where context and sequence are crucial. RNNs are suited to tasks such as:

Generating responses for virtual assistants using large language models
Time series forecasting, such as predicting future data points based on past trends
Speech recognition
Natural language processing (NLP) tasks, such as text analysis, sentiment detection, translation

Neural networks and deep learning: Understanding the terminology

What is a neural network in AI? Answering the question involves understanding where neural networks fit within the AI landscape. Terms such as deep learning and neural networks are sometimes used interchangeably, but it’s important to note that, though similar, they are not the same. In this section, we examine neural networks and deep learning in greater detail, laying the groundwork for understanding their differences and applications.

What is deep learning?

Deep learning can be considered to be a subset of both AI and machine learning, focusing on neural networks with many layers. Deep learning models can have hundreds of layers, enabling deep learning systems to learn more complex representations of input data than simple models.

The more layers that a neural network has, the better it becomes at uncovering subtle relationships and patterns found within unstructured datasets. This capability is why deep learning has driven some of the most significant AI breakthroughs in recent years.

It could be said that deep learning models are neural networks, but neural networks are not always deep learning models.

For example, a basic neural network may be able to predict environmental temperature based on limited inputs, such as wind speed and humidity. However, to build a solution that can understand complex patterns, such as speech or facial recognition, a deep learning model will be needed.

Why does "deep" matter?

The number of layers a neural network has—also known as its ‘depth’—has a direct impact on its abilities to learn complex patterns. The top layers might learn to recognize simple patterns, such as the edges of an image. As data flows through deeper layers, more advanced patterns are learned, such as identifying a human face within an image.

The power of deep learning lies in its ability to learn both simple and complex patterns. It’s essential for applications such as computer vision, speech recognition, and autonomous systems—those systems that are highly dependent on processing large amounts of data and identifying intricate patterns.

How it fits together

Neural networks provide a fundamental framework for machines to learn from data. Deep learning expands on this framework by adding additional layers stacked like a skyscraper. This added depth enables deep learning models to perform tasks that require a more sophisticated understanding of the world.

The history of neural network models

The idea behind neural networks isn’t new: they’ve been considered and even implemented in basic forms for more than 70 years. Some of the key events along this timeline are:

1943: McCulloch and Pitts propose the first mathematical model of a neuron.
1958: Frank Rosenblatt introduces the Perceptron, an early form of neural network.
1986: Rumelhart, Hinton, and Williams popularize backpropagation, enabling the training of multi-layer networks.
1990s: Neural networks face a decline in popularity due to limited computational power and lack of large datasets.
2006: Geoffrey Hinton and colleagues reignite interest with advances in deep learning and unsupervised pre-training.
2012: A deep CNN wins the ImageNet competition, demonstrating superior performance in image recognition.
2010s-Present: With massive growth in AI research and deployment, neural networks now power systems in healthcare, finance, entertainment, and more.

How does a neural network work?

Neural networks learn from data, which involves adjusting internal weights and biases (more on this later). This adjustment enables the neural network function to make more accurate decisions.

Traditional computer programs only follow a fixed set of instructions, but neural networks train themselves to solve problems. Let’s look at a neural network’s underlying architecture to help understand how these results are achieved.

Layers: the key components of a neural network

A neural network usually consists of three main types of layers: input, hidden, and output. Each of these has a specific function within the network, and how it processes and transforms input data.

Input layer

The first layer that incoming data hits is the input layer. In this layer, every node represents a specific feature of the data, with examples being:

Each cell in a spreadsheet
Each word in a piece of text
Each pixel in an image

Hidden layers

It is the hidden layers that perform the processing of data that has been sent from the input layer. These are the layers that find patterns and features within the data. Each node receives its input from the previous layer, and passes the results of the input on to the next layer.

The more hidden layers in a neural network, the deeper it can learn—that’s where we get the term ‘deep learning’.

Each layer builds on the representation of the data. For example, earlier layers might find edges in an image, with lower layers finding objects or features such as faces.

Output layer

As the name suggests, the output layer is the final stage; it’s the decision or prediction or answer made by the neural network. The number of nodes found in the output layer will generally correspond with the potential output categories, or range of prediction values.

Other essential components of a neural network

Weights and biases

These are used to change the settings of a neural network and make adjustments to the way it processes data. Weights are used to set the strength of the connections between nodes. Biases are additional parameters that can be changed to enable nodes to shift the activation function, effectively making it more flexible. During training, both weights and biases are fine-tuned to improve accuracy.

Activation functions

Activation functions help introduce complexity by making the network capable of handling more than just simple linear problems. This makes the network more capable of learning advanced patterns. Some of the common types of activation functions are:

Sigmoid: Generally used for binary classification, where allowed values are between 0 and 1.
Tanh: Working in a similar way to Sigmoid, with the difference being they allow for values between -1 and 1.
ReLU: Usually found in the hidden layers, they activate nodes only if the input is positive, which can improve efficiency.

The neural network training process

Training a neural network involves providing a large enough dataset for the network to learn from, so that it can then use its knowledge base to make accurate predictions and decisions. The process also involves adjusting weights and biases. Step-by-step, the training process looks something like this:

Forward propagation: The forward propagation process is how the neural network makes predictions. The input data moves through the network, passing through layers where each step involves some calculations. At the end, the network gives an output based on these calculations.
Loss calculation (error measurement): After the network makes a prediction, the loss function checks how far off the prediction is from the actual result. It calculates an ‘error score’ to measure how well (or poorly) the network did. The smaller the error, the better the network’s performance.
Backpropagation: Backpropagation uses the error (calculated during the loss step) to work backward through the network. It figures out how much each weight and bias contributed to the mistake. Then, it calculates how to adjust them to improve the network’s accuracy.
Optimization (updating weights and biases): Once the network calculates the gradients (which show how much each weight and bias contributed to the error), an optimization algorithm adjusts the weights and biases. The goal is to make the network more accurate the next time it processes data.
Iteration (epochs and training cycles): An important consideration here is that this cycle of forward propagation, loss calculation, backpropagation, and optimization is not carried out just once. Instead, the process is repeated many times to make the neural network as accurate as possible, based on the input dataset.

Neural network example use cases

Image recognition

Neural networks can become very good at analyzing and classifying images, and uses range from detecting faces to identifying tumors by analyzing a medical scan. Object recognition applications such as security systems have used neural networks for image recognition.

Speech and language processing

Voice assistants and real-time language translation applications use speech recognition that is powered by a neural network. These applications rely on the neural network to help machines understand and generate human language. Language processing also has applications in sentiment analysis and AI-powered virtual assistants.

Financial forecasting

In the finance sector, financial institutions are using neural networks in several effective ways, including detecting fraud, risk assessment, and predicting market trends. Neural networks can analyze huge datasets to uncover patterns within the data, usually quicker and more accurately than human analysts can. These patterns can then be used for predictions and to help with decision-making.

Medical diagnosis

Medical professionals can leverage neural networks to analyze complex medical data—such as patient records, genetic information, and scan images—quickly and accurately. Neural networks have been used to support successful diagnosis of illnesses such as diabetes, heart conditions, cancer, and more.

Autonomous vehicles

At the heart of most self-driving vehicles is a neural network. The network analyzes real-time data inputs from hardware such as cameras, radar, and LiDAR. The network can use the input data to read road signs, avoid obstacles and other vehicles, and spot pedestrians, for example.

Recommendation systems

E-commerce platforms use neural networks to provide customers with recommendations of products or services they may be interested in. These recommendation engines analyze customer behavior, past purchase history, and purchasing preferences to create a highly personalized list of recommendations.

Hydropower forecasting

Neural networks have been used to analyze weather data for many years to predict hydroelectric plants' potential power generation capacity. This analysis can consider seasonal variances, high-demand periods, and infrastructure availability to optimize power generation and plan to meet energy demands as needed.

Urban planning

Urban planners have used AI and neural networks to get predictions about land usage and the best way to optimize planned infrastructure. By analyzing diverse data sources, including satellite images, economic data, and emerging trends in demographics, these predictions can help planners judge the environmental impact of planned development projects, optimize traffic flow, and ensure the area has sufficient utility coverage.

Material design and discovery

Neural networks excel at processing huge datasets, making them effective at analyzing material properties and chemical compounds to extrapolate experimental results.

This capability has been used to discover new or improved manufacturing materials for specific uses, such as alloys that are lighter yet just as strong as existing materials. The sheer power of neural networks at crunching this kind of data means that the discovery process is far more rapid than manually performing the analysis and experimentation.

Advantages of neural networks

Neural network models have become one of the most powerful tools in modern AI and machine learning (ML), helping to enable breakthroughs across a wide range of industries. Their unique structure and learning capabilities offer several compelling advantages that set them apart from traditional algorithms. Below, we look at some of the main benefits of using a neural network.

Adaptability: Learning directly from data

One of the main advantages of implementing a neural network is that neural network modeling doesn’t rely on programmed rules to learn directly from a dataset. This means that a neural network is highly flexible and can be adapted to multiple uses, including those that would be too complex to solve programmatically.

Handling complex patterns and relationships

Neural networks are very good at uncovering complex, non-linear patterns in data that traditional models struggle to achieve. It is the multilayered approach to the architecture of the network that enables them to process multiple factors at the same time. The result is uncovering subtle relationships within the input data.

Parallel processing and computational efficiency

Neural networks—and, more specifically, neural networks using deep learning—can perform a huge volume of computations. Their structure is well suited for carrying out tasks that require parallel processing, with a large number of nodes and layers processing data at the same time.

This is especially relevant when hardware such as Tensor Processing Units (TPUs), which are specialized chips designed to speed up machine learning tasks, or Graphics Processing Units (GPUs)—powerful processors originally made for video games, now used to handle complex calculations in machine learning—are used as the platform for running the neural network. These hardware platforms support parallel processing.

Scalability: Growing to meet complex task demands

The architecture of neural networks can be scaled fairly simply, which can be a significant advantage in some use cases. More layers can be added, scaling vertically, or more nodes can be added to layers, scaling horizontally. Both options will increase the power of the neural network.

When the complexity of the problems the neural network is intended to solve grows, the architecture can be scaled accordingly. Another benefit is that neural networks can be scaled to match training data. The neural network can grow as more training data becomes available, enabling it to learn better from the new data.

Limitations and challenges of neural networks

Neural networks have proven their worth in real-world applications; they can be both flexible and powerful. However, some potential pitfalls could be encountered when implementing a neural network.

These pitfalls generally fall into one of three categories: practical process changes, ethical considerations, and technical challenges that need to be overcome. In the section below, we will examine some of the limitations and challenges faced with neural network adoption.

Computationally intensive: High demand for hardware resources

Deep neural networks require significant computer resources to operate and train. In some cases, specialized hardware such as TPUs and GPUs are needed for the underlying hardware architecture on which the neural network runs.

For large-scale implementations, the costs of this computing power can be very high. For example, a model used for image recognition will be trained on millions of images, or a model designed for generative AI-produced text will be trained with billions of parameters, making for rather costly experiments.

Black box nature: Lack of transparency and explainability

One of the main criticisms of neural networks is their inherent “black-box” nature—it is very difficult to determine how a neural network reached a decision or made a prediction.

Neural networks don’t rely on clearly stated rules and programmatical instructions to process data. Instead, the complex, layered results of input data filtering through all the layers of a neural network can result in millions of interactions between nodes based on the weights and biases. These computations are very hard to monitor and understand. The result is a lack of transparency in how AI-powered systems that incorporate a neural network operate.

Overfitting: Learning noise instead of signal

Neural networks are very good at modeling complex patterns, making them very flexible. However, this flexibility has a downside: overfitting.

Overfitting is a term used to describe a situation where the model has learned irrelevant patterns and relationships within the training data. This is generally caused by noise in the training data, often due to ineffective data sanitization.

Data hungry: Need for large and high-quality datasets

Training a neural network requires huge volumes of training data, and this training data must be accurate and relevant. When working with some applications where specific domain knowledge needs to be part of the training corpus, it can be difficult to build a large enough dataset for training.

Furthermore, the training dataset must be as free of biases as possible. If any bias exists within the data, the model will learn these biases. It could then require further expensive fine-tuning to remove these biases later.

Neural networks: A fundamental part of AI technology

Neural networks have become one of the key enabling technologies that modern AI-driven enterprise systems rely on. They have often delivered impressive results in use cases such as rapidly diagnosing illnesses or performing real-time feedback for self-driving vehicles.

As AI finds new applications within the enterprise, we’ll likely discover additional uses for neural networks. The underlying architecture of neural networks will continue to evolve, improve, and become more dependable, understandable, and affordable.

As AI continues to evolve, neural networks will undoubtedly play an even bigger role in shaping the future of enterprise systems.

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What is a neural network and how does it work?