AI
Neural Networks
A practical guide to understanding what neural networks are and why they matter for anyone working with AI.
What neural networks are, how they relate to modern AI, and what product and design teams need to know without needing to understand the mathematics.
What it is
A neural network is a type of computational glossarySystemA system is a collection of interconnected components that work together to achieve a specific function or outcome.Open glossary term loosely inspired by the structure of the human brain. It is made up of layers of interconnected nodes (sometimes called neurons) that glossaryProcessA process is a defined sequence of steps used to achieve a specific outcome.Open glossary term and transform information as it passes through the network.
Neural networks learn by adjusting the strength of connections between nodes based on the glossaryDataData is raw, uninterpreted information collected and stored so it can be analysed, processed, or used to inform decisions. On its own it carries no meaning; context and interpretation are what make it useful.Open glossary term they are trained on. Over millions or billions of training examples, these adjustments result in a glossarySystemA system is a collection of interconnected components that work together to achieve a specific function or outcome.Open glossary term capable of recognising glossaryPatternA pattern is a reusable solution to a design problem that recurs across products and contexts. It captures an approach already shown to work, so a team solves the problem once rather than every time it appears.Open glossary term, making predictions, and generating outputs that no one explicitly programmed.
Modern AI language glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term (including the ones that power ChatGPT, Claude, and Gemini) are built on a specific type of neural network glossaryArchitectureArchitecture refers to the structure and organisation of a system, including how components interact and are designed.Open glossary term called a transformer. Image recognition, speech processing, and recommendation glossarySystemA system is a collection of interconnected components that work together to achieve a specific function or outcome.Open glossary term also use neural network approaches.
Neural networks are not a recent invention, but advances in computing power and the availability of large glossaryDatasetA dataset is a structured collection of data used for analysis, training models, or processing.Open glossary term have made them dramatically more capable over the past decade.
When to use it
Understand when knowledge of neural networks is practically useful. It is relevant when:
Key takeaway
Neural networks are the engine behind modern AI. You do not need to build one, but understanding what they are makes you a more informed designer, product manager, or strategist working with AI.
How it works
The basic mechanism
A neural network consists of an input layer that receives glossaryDataData is raw, uninterpreted information collected and stored so it can be analysed, processed, or used to inform decisions. On its own it carries no meaning; context and interpretation are what make it useful.Open glossary term, one or more hidden layers that glossaryProcessA process is a defined sequence of steps used to achieve a specific outcome.Open glossary term it, and an output layer that produces the result.
Each connection between nodes has a weight, a number that determines how strongly one node influences another. During training, these weights are adjusted through a glossaryProcessA process is a defined sequence of steps used to achieve a specific outcome.Open glossary term called backpropagation, which updates them based on how wrong the network's output was compared to the correct answer.
After training on enough examples, the network develops the ability to generalise, applying what it has learned to new inputs it has never seen before.
Language glossaryModelA model is a system or representation used to process data and generate outputs, often trained to perform specific tasks.Open glossary term are a specific type of neural network trained to predict and generate text. Their scale (billions of parameters) is what gives them the breadth of glossaryCapabilityCapability refers to an organisation’s ability to perform a specific function or deliver a particular outcome.Open glossary term that has made them so impactful.
What this means for designers and product teams
Neural networks are not programmed with rules. They learn glossaryBehaviourBehaviour refers to how users interact with a system, including actions, patterns, and responses.Open glossary term from glossaryDataData is raw, uninterpreted information collected and stored so it can be analysed, processed, or used to inform decisions. On its own it carries no meaning; context and interpretation are what make it useful.Open glossary term. This is why they can do things that are difficult to explicitly code (like understanding the nuance of natural language) and also why they sometimes fail in unpredictable or surprising ways.
The glossaryBehaviourBehaviour refers to how users interact with a system, including actions, patterns, and responses.Open glossary term of a neural network is emergent from its training. This means outputs can be hard to fully anticipate, explain, or control in the way that traditional software can be.
What to look for
Focus on:
Where it goes wrong
Most issues come from: Applying traditional software debugging instincts to neural network failures will not work. They fail differently.
What you get from it
Understanding neural networks gives you:
Key takeaway
Neural networks learn from data, not rules. That single insight explains most of what makes AI powerful and most of what makes it unpredictable.
Get in touch
You do not need the maths to brief and challenge AI work, but you do need the mechanism. We can help you build that understanding.
No guesswork. No assumptions. Just enough understanding to ask better questions.
FAQ
Common questions
A few practical answers to the questions that usually come up around this method.
What is a neural network in simple terms?
A neural network is a type of computational system made up of layers of interconnected nodes that learn to recognise patterns by processing large amounts of training data. It is the technology underlying most modern AI systems, including language models, image recognition, and speech processing.
Do I need to understand the mathematics to work with AI?
No. Understanding the principles (that neural networks learn from data, that their behaviour is emergent and sometimes unpredictable, that performance depends on training) is what matters for product and design work. The mathematics is for engineers and researchers.
How are neural networks different from traditional software?
Traditional software follows explicit rules that a programmer writes. Neural networks learn their own rules from data. This makes them capable of tasks that are too complex to program explicitly, but also means their behaviour can be harder to predict, explain, and debug.
What is a transformer?
A transformer is a specific neural network architecture that has become the foundation of modern language models. It is particularly good at processing sequences of tokens and understanding context across long passages of text. GPT, Claude, Gemini, and most other leading language models are built on the transformer architecture.
Are all AI systems neural networks?
No. Traditional rule-based systems, decision trees, and statistical models are not neural networks. But most of the AI capabilities that have attracted widespread attention in recent years (language models, image generators, voice assistants) are built on neural network foundations.
Quick take
You do not need to understand the mathematics, but understanding what neural networks are helps you work more confidently with AI products and teams.
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