AI Fluency Foundations

The field of AI changes fast, and AI skills expire quickly. In this lecture, we'll break down what the course covers, and why building AI fluency matters so much.

AI literacy isn't the same as fluency, but how are they different? In this lecture, we'll break down the differences between these two concepts.

Most people believe they're better at working with AI than they actually are. In this lecture, we'll look at what the research says about that gap, and what it means for you.

AI fluency isn't one skill, it's several. In this lecture, we'll introduce the four dimensions of our fluency framework, and the sub-skills that sit underneath each one.

AI and generative AI are often talked about as if they're the same thing, but they aren't. In this lecture, we'll cut through the hype and show how the different branches of AI fit together.

The AI tool landscape is vast, and it's growing every week. In this lecture, we'll tour the main categories of AI tools, from the foundation models underneath to the specialised applications built on top.

Every AI system is only as good as the data behind it. In this lecture, we'll unpack what makes data fit for AI, and why its structure shapes everything that follows.

Different kinds of data call for different kinds of AI. In this lecture, we'll walk through the methods best suited to tabular, text, image and network data, and why the match matters.

Not all data is organised the same way, and that difference has real consequences. In this lecture, we'll compare structured, unstructured and semi-structured data, and what each one lets you do.

Before you can pick a model, you need to know what job you're actually asking it to do. In this lecture, we'll explore how to frame a problem as a specific AI task.

Generative AI can do a lot, but it isn't always the right choice. In this lecture, we'll look at when to reach for it, and at the design patterns behind more sophisticated AI applications.

We talk about AI models constantly, but what is a model, really? In this lecture, we'll explain how models work, how generative models differ from what came before, and what to ask before trusting one.

The models we use every day didn't arrive fully formed. In this lecture, we'll go deeper into how large language models are trained and specialised, and the landscape that's emerged as a result.

Knowing what to hand AI, and what to keep for yourself, is one of the most important calls you'll make. In this lecture, we'll get into how to make that decision, and what happens when people get it wrong.

AI fails in patterns, and those patterns have names. In this lecture, we'll go through the most common AI failure modes, what each one looks like in practice, and how to spot them coming.

Handing a task to AI is rarely as quick as it looks, because someone still has to check the work. In this lecture, we'll cover the hidden cost of verification, and what it's cost organisations that ignored it.

Not every task deserves the same level of AI involvement. In this lecture, we'll introduce the delegation dial, and how to move it as the stakes of a task change.