Artificial intelligence, machine learning, deep learning and generative AI are all terms often used together, but they actually mean different things. And I get it — it’s confusing! With these buzzwords dominating headlines, it can feel like you need a degree in computer science just to have a conversation. But here’s the truth: understanding the basics of these technologies can be quite simple — if we use some real-world examples.

Let’s break it down together, in a way that’s easy to grasp, and see why this matters for our future.

Artificial intelligence: the big picture

Imagine AI as the big umbrella under which all these other terms fall. AI refers to the overall concept of creating machines that can think and act like humans. It’s the magic behind personal assistants such as Siri or even your car finding the fastest route home during rush hour.

AI is about making machines “intelligent” enough to solve problems — but under this umbrella, there are different ways we teach them to do that.

Machine learning: teaching the machine

Machine learning is a specific approach to AI. Instead of writing step-by-step instructions, the computer is trained to recognise patterns from examples. Imagine teaching a child to recognise apples by showing them lots of pictures. Eventually, they will be able to point out an apple in real life. Machine learning works similarly: it’s about giving data to computers and allowing them to find patterns. This is how Netflix learns what shows you like or how Spotify recommends your next favourite song.

Deep learning: the brain-inspired approach

Deep learning is a branch of machine learning, and it is all about using models called neural networks, which are inspired by the human brain. These deep neural networks have many layers — hence “deep” — and are particularly good at recognising complex things, such as identifying a friend’s face in a crowded photo or understanding spoken language.

A good way to picture this is to think about how we learn as humans. Deep-learning models take small pieces of information, like our senses do, and build a complete understanding. For example, Tesla’s self-driving cars use deep learning to understand roads, pedestrians and even other cars.

Large language models: understanding language

Large language models are trained on vast amounts of text data, allowing them to generate coherent and contextually relevant text. LLMs can answer questions, draft e-mails, write stories and even help brainstorm ideas.

Think of LLMs as a system that has read and learnt from millions of books and articles. They don’t “understand” language in the way we do, but they can predict what comes next in a sentence based on patterns. This makes them useful for a wide range of tasks, from customer service to creative writing.

Think of generative AI as having a creative system capable of generating novel content, which, instead of just giving you existing answers, actually makes something brand new — whether that’s a story, a drawing or even a piece of music.

Why does this matter for innovation?

Understanding these basic terms is important because it is driving the innovation of today and tomorrow. We are using these technologies not only to improve efficiency in industries but also to solve problems we couldn’t imagine solving before. AI is behind everything from predicting natural disasters to optimising business processes, and you don’t need to be a techie to see how this touches our lives.

The more we all understand about how these technologies work, the better we will be able to participate in conversations about their impact — whether it’s excitement about the future of innovation or concerns over privacy and ethics.

I am constantly learning more about these topics, and I invite you to do the same. Don’t be intimidated by the jargon; if you think about these technologies in practical ways, they become much more approachable. AI isn’t some distant concept — it’s part of our everyday world, and we’re all on this journey of understanding together.

• Christian Chin-Gurret is a Bermudian writer with a Master of Science in Innovation and Entrepreneurship and a Bachelor of Science in Product Design, who offers a unique perspective on shaping the future of business through innovation, disruption and technology