How does A.I work?

Last night I was listening to a fascinating episode of StarTalk on Spotify where Geoffrey Hinton was explaining how AI actually works. Hinton isn’t just another commentator talking about AI from the sidelines, he’s one of the people who created the foundations of the technology itself. He has spent decades working on what are called neural networks, which are at the heart of modern AI systems like ChatGPT. What really stood out to me was how different the reality is compared to what most people imagine. Most people assume AI is just clever software that’s been programmed with lots of rules, but that isn’t what’s happening at all.

Traditional software works by following instructions written by humans. A programmer sits down, decides what the rules are, writes those rules into code, and the computer follows them exactly. The computer itself never learns or improves. It simply executes instructions. AI works in a completely different way. Instead of being programmed with fixed rules, it is trained using vast amounts of data, and through that training it learns patterns that allow it to make predictions.

At the core of modern AI is something called a neural network, which was inspired by the structure of the human brain. The brain is made up of billions of neurons connected together, and these neurons communicate by sending signals to each other. Neural networks in AI are a simplified mathematical version of this idea. They consist of layers of artificial neurons, and each connection between neurons has a numerical value called a weight. These weights determine how strongly signals pass from one neuron to another. When information enters the network, it flows through these layers, and each neuron performs a small mathematical calculation before passing its output forward.

What makes this powerful is that the network starts with random weights and doesn’t know anything at all. When it is shown an example, it produces an output, which is usually wrong at the beginning. The system then compares its output to the correct answer and calculates the error. This error is then used to adjust the weights slightly, strengthening some connections and weakening others. This process is repeated millions or billions of times. Gradually, the network adjusts itself so that the outputs become more accurate. The intelligence doesn’t come from someone programming knowledge directly into the system, but from the network adjusting itself based on experience.

One of the key concepts Geoffrey Hinton explained is that the network is essentially learning to represent patterns in a very high-dimensional space. Each neuron responds to certain features, and deeper layers of the network respond to more complex combinations of those features. For example, in image recognition, early layers might detect simple things like edges and shapes, while deeper layers detect more complex features like eyes, faces, or objects. In language models like ChatGPT, the network learns relationships between words, meanings, and context. It learns which words tend to follow other words, and how ideas are structured.

What’s particularly interesting is that the system isn’t storing facts in the way a database does. Instead, the knowledge is encoded in the strengths of billions of connections between neurons. The model doesn’t retrieve answers from memory like a filing cabinet. It generates responses based on the patterns it has learned. When you ask a question, the network processes the words and predicts what sequence of words would most likely form a useful and coherent answer. It is constantly making probability-based predictions at every step.

Another important part of this process is something called backpropagation, which is the method used to train the network. When the network produces an incorrect output, backpropagation works backwards through the layers, calculating how much each connection contributed to the error. It then adjusts those connections slightly to reduce the error next time. This happens across millions of connections simultaneously. Over time, this process allows the network to develop extremely sophisticated internal representations of information.

What makes modern AI so powerful is the scale at which this training happens. These models are trained on enormous datasets using vast computing power. They may have billions or even trillions of adjustable parameters, which allows them to capture incredibly complex patterns. This scale is what allows AI to perform tasks that once seemed impossible, such as understanding natural language, generating realistic images, or assisting with complex reasoning tasks.

One of the most surprising things Hinton mentioned is that even the researchers who build these systems don’t fully understand the exact internal representations that emerge during training. They understand the principles, the mathematics, and the training process, but the precise way the network organises knowledge internally is too complex to fully interpret. The intelligence emerges from the system as a result of the training process, rather than being explicitly designed step by step.

What this really made me realise is that AI isn’t just another technology tool like previous software innovations. It represents a fundamentally different approach, where machines learn from data rather than being told exactly what to do. This is why it can adapt to such a wide range of tasks, from writing and communication to analysis and automation.

We’re still in the early stages of understanding how to fully use this technology, but it’s clear that it’s going to reshape how businesses operate and how work gets done. The people who take the time to understand it, even at a basic level, will be in a much stronger position than those who ignore it.

Listening to Geoffrey Hinton explain it made it clear that this isn’t hype or science fiction. This is real, and it’s already here. The more you understand how it works, the more you start to see the opportunities it creates.