Neural Networks Explained: The Technology Behind Modern AI

Neural networks are the foundational technology behind modern AI. ChatGPT, Google Gemini, Apple’s Siri, NHS diagnostic imaging tools, the recommendation algorithm on Netflix — all of these systems are built on variations of the same underlying architecture. Yet most people who use AI tools daily have only a vague sense of what a neural network actually is or how it works.

This guide explains what neural networks are, where they came from, how they learn from data, the main types used for different tasks, and — critically — what their fundamental limitations mean for anyone using AI tools in work or daily life in the UK today.

Where Neural Networks Came From

The idea of a neural network is older than most people assume. Warren McCulloch and Walter Pitts published a mathematical model of an artificial neuron in 1943, arguing that simplified models of biological brain cells could perform logical operations. Frank Rosenblatt built the first hardware implementation of a neural network — the Perceptron — at Cornell University in 1957. The Perceptron could learn to classify simple patterns by adjusting internal parameters based on whether its outputs were correct.

Early enthusiasm faded in the 1960s and 1970s as researchers discovered that single-layer networks had severe limitations — they could not learn the XOR function, a trivial logical operation. Neural network research entered a long quiet period sometimes called the AI winter.

The revival began in the 1980s with the development of backpropagation — a method for training multi-layer networks by propagating error signals backwards through the system. Multi-layer networks, called deep networks, could learn far more complex patterns than single-layer systems. But training them was computationally expensive, and available hardware in the 1990s and early 2000s was not powerful enough to make them practical for large problems.

The transformation came from an unexpected source. Gamers’ demand for increasingly powerful graphics processors created GPUs — chips with thousands of processing cores designed for parallel computation. Researchers at the University of Toronto realised in 2007 that GPU hardware was ideally suited to the matrix multiplication at the heart of neural network training. This insight, combined with the availability of large datasets from the early internet, triggered the deep learning revolution that has continued to the present day.

How a Neural Network Learns

A neural network consists of layers of artificial neurons connected by edges with associated weights. The input layer receives raw data — pixel values for an image, token embeddings for text, or numerical features for any structured dataset. The output layer produces a result — a classification, a probability, a generated text token. Between them are one or more hidden layers where the network learns increasingly abstract representations of the input data.

Each neuron receives inputs from the previous layer, multiplies them by its associated weights, sums the results, adds a bias value, and passes the sum through an activation function. Common activation functions include ReLU (Rectified Linear Unit), which outputs zero for negative inputs and the input value for positive inputs, and sigmoid, which maps any input to a value between zero and one. Activation functions introduce non-linearity — without them, a multi-layer network would be mathematically equivalent to a single-layer network regardless of depth.

Training a neural network means adjusting its weights so that its outputs match the correct answers for a training dataset. This is done through gradient descent: the network makes a prediction, the prediction is compared to the correct answer using a loss function, the loss function gradient is calculated with respect to each weight using backpropagation, and each weight is adjusted by a small amount in the direction that reduces the loss.

This process repeats millions or billions of times across the training dataset. The network does not receive explicit rules about what to look for in the data. It discovers statistical patterns by trial and error, adjusting internal parameters until its predictions improve.

GPT-4, OpenAI’s large language model underlying ChatGPT, was trained on approximately several trillion tokens of text — roughly equivalent to millions of books. The training computation cost was estimated at tens of millions of pounds in compute time. This scale of training is only accessible to a handful of organisations globally.

Types of Neural Networks and What They Are Used For

Not all neural networks have the same architecture. Different architectures are suited to different types of data and tasks.

Feedforward networks — the simplest type — pass information in one direction from input to output. They work well for tabular data classification, fraud detection, and simple regression problems.

Convolutional Neural Networks (CNNs) add layers that scan for local patterns in the input using learnable filters, making them highly effective for image and video data. CNNs underpin facial recognition systems, self-driving car perception modules, and medical imaging AI. The NHS AI Lab has evaluated CNN-based tools for detecting diabetic retinopathy from retinal scans and for identifying breast cancer in mammograms. A 2023 study in The Lancet Digital Health found that CNN-based mammography analysis matched or exceeded the accuracy of experienced radiologists on a large UK dataset.

Recurrent Neural Networks (RNNs) and their successors, Long Short-Term Memory networks (LSTMs), process sequences of data by maintaining a form of memory about earlier parts of the sequence. They were widely used for speech recognition and text processing before being supplanted by the transformer architecture.

Generative Adversarial Networks (GANs), introduced by Ian Goodfellow in 2014, pair two networks — a generator that creates synthetic data and a discriminator that tries to distinguish synthetic from real data — training them adversarially. GANs produced many of the realistic synthetic face images that appeared widely online between 2019 and 2022.

Diffusion models are the architecture behind modern image generators including DALL-E 3, Midjourney, and Stable Diffusion. They learn to gradually add and then reverse noise in images, learning the structure of realistic images by being trained to denoise progressively corrupted examples. The result is a model that can generate photorealistic or stylised images from text descriptions.

Deep Learning and the Breakthrough That Changed Everything

The moment that confirmed deep learning’s potential is often dated to 2012, when a team led by Geoffrey Hinton at the University of Toronto entered a convolutional neural network called AlexNet into the ImageNet Large Scale Visual Recognition Challenge — an annual competition where systems attempt to classify one million images into 1,000 categories.

AlexNet achieved a top-5 error rate of 15.3 per cent — compared to 26.2 per cent for the second-place entry, which used traditional computer vision methods. The gap was enormous and unexpected. Researchers immediately understood that deep neural networks, given sufficient data and GPU compute, could outperform decades of hand-crafted computer vision algorithms.

Within three years, deep learning systems were achieving error rates on ImageNet that surpassed average human performance on the same benchmark. The deep learning revolution had begun.

Transformers: The Architecture Behind Large Language Models

The transformer architecture, introduced in a Google research paper titled Attention Is All You Need in 2017, is the foundation of modern large language models. It replaced recurrent architectures for language tasks and proved to be dramatically more effective.

The key innovation of transformers is the attention mechanism, which allows the model to weigh the relevance of every part of an input sequence when generating each output. When processing the sentence “The bank by the river was steep,” the attention mechanism allows the model to connect “bank” to “river” rather than to financial institutions — resolving ambiguity by attending to context anywhere in the sequence.

GPT (Generative Pre-trained Transformer), developed by OpenAI, uses the transformer architecture at enormous scale. GPT-3, released in 2020, had 175 billion parameters — adjustable weights trained to predict the next token in a sequence. GPT-4, released in 2023, is estimated to have significantly more, though OpenAI has not published the exact figure.

The transformer architecture has also proved highly adaptable beyond language. Vision Transformers apply the same approach to images by treating patches of an image as tokens, and multimodal transformers like GPT-4o process text, images, and audio within a single architecture.

What Neural Networks Cannot Do

Understanding neural networks matters because their limitations are real and consequential for anyone relying on AI tools.

Neural networks are pattern-matching systems trained on historical data. They do not reason, plan, or understand in the way humans do. They produce outputs that statistically resemble correct answers given similar inputs in their training data. When asked about situations significantly outside their training distribution — unusual combinations of facts, novel events, specialised domains underrepresented in training data — they can produce confident-sounding but incorrect outputs. This is the source of hallucinations in large language models.

A neural network cannot explain why it produced a specific output in human-interpretable terms. Its decision is encoded across billions of numerical parameters with no accessible reasoning chain. This opacity is why regulators in the UK and EU have concerns about high-stakes AI decisions — in lending, insurance, hiring, and criminal justice — made by neural network systems that cannot explain themselves.

Neural networks also encode the biases present in their training data. A system trained on historical hiring decisions will encode historical biases against women or certain ethnic groups if those biases were present in the training data. Debiasing techniques exist but are imperfect, and audit requirements for AI systems used in employment and financial services are increasing under the EU AI Act, which the UK government is monitoring for regulatory alignment.

What This Means for UK Users

For most people in the UK using AI tools — writing assistants, chatbots, image generators, recommendation systems — understanding neural networks at a high level is more useful than technical detail. The practical implications are: AI tools are powerful pattern matchers, not reasoning systems. They can be confidently wrong. Their outputs require human verification in any consequential application. The impressive fluency of AI text does not indicate accuracy.

For UK businesses considering AI deployment, understanding that neural networks require large, representative, carefully curated training datasets is essential for planning. A classifier trained on unrepresentative data will perform poorly on populations not well-represented in training — a risk in healthcare AI particularly, where training datasets have historically over-represented certain demographics.

This article is for educational purposes only and does not constitute financial advice.