This Chapter provides an overview of the course.

It covers the objectives of the course, how you can use the materials to learn, and different learning pathways throughout the course. It also introduces myself a little, so you know that the course was written by someone with 25+ years of experience in researching and deploying Artificial Intelligence.

The course is designed to fit all levels, and the pace is very measured and step-by-step. If you feel the videos are too slow, then you can listen at a higher speed -- I won't be offended!

If what is described in this introduction meets what you are looking for in a course, then there is a high chance this is the right course for you.

This chapter starts at the beginning : by discussing what Artificial Intelligence is, and what it isn't. You'll learn some common terms, and some ways to test for 'intelligence'.

There are so many opinions on AI, but often when asked to describe what AI actually is, people often struggle to do so. So it's worth taking the time going through both definitions but also introducing AI from a technology and research perspective.

The field of AI is huge -- almost certainly a lot larger that you thought! And it's definitely older than you might expect. So having an overview before we dive in to specifics helps set up the course, and already starts to build your knowledge of the fundamentals.

AI has certainly been at the front of media and business recently.

But given the long history of AI, why is it suddenly centre stage in the world right now?

In this Chapter we break down the reasons, and give the context for the current 'AI revolution'. This helps put the modern AI systems, and the social, business, economic, and political sentiments around AI in perspective.

The current interest in Artificial Intelligence is not a temporary fascination. It's a fundamental shift. The best analogy for this is with the emergence of Information Technology or 'IT' in the 1980s. Perhaps you lived through the change from a world without computers, to one where they proliferate across all aspects of our lives. Or you can try to imagine how big a shift that was. Recent developments in AI are shaping it to be a turning point on a similar scale.

This Chapter introduces a set of techniques that enable AI to play games like Tic-Tac-Toe, Chess, Go, and Poker.

This is a fun way to start introducing some of the 'tech' of AI. But don't panic! We go through it step-by-step.

Although it may not at first seem related to large language models and applications such as ChatGPT, most people are familiar with games. So this enables us to introduce some concepts that we'll apply in later chapters in a gentle and fun way.

To make all the modern AI systems work, computers need to process text.

We all communicate with words and language, and to do any kind of intelligent processing of text with a machine, we need to figure out how to represent words. This is not as simple as you might think.

This Chapter introduces how words are represented in computers, and how they can be manipulated in order to achieve different tasks.

It is perhaps one of the more 'technical' chapters, as it introduces a few new concepts, but don't worry, we go through everything step by step. Using both the pdf and the videos will help, as they complement each other, and we all have different ways to learn.

This chapter will also introduce your first machine learning algorithm, so you can see how basic email spam detectors work! We'll also have some fun generating some 'Shakespeare' and Sherlock Holmes stories, so we can already start to see how AI might be used to generate text. We also introduce the idea of hallucination and what it means, which is a big challenge for modern Large Language Model AI systems.

Neural Networks is one of the research areas within Machine Learning and Artificial Intelligence.

A lot of the modern applications, like Large Language Models and ChatGPT, and also the image and video generation AI that you see, depends on Neural Networks. So you need to learn what they are and what they can do.

Building on ideas from previous chapters we walk you through basic neural networks, even training one step-by-step to give you some insight and intuition as to what is happening when these huge real-world models are trained.

We look at how more complex networks can learn more complex things, and introduce more advanced topics such as recurrent neural networks, deep learning, and long short-term memory networks.

If these just sound like complicated buzzwords now, by the end of the chapter you'll know what each of these are, and how they work!

Reinforcement Learning is another large area of AI which has seen a lot of impressive recent developments.

It was at the core of AlphaGo -- which from the AI and Games chapter you already know, and is being applied to various real-world problems, including protein folding, with great success.

Reinforcement Learning also contributes heavily to the success of modern systems such as ChatGPT, Claude, Perplexity, Gemini and others. Part of the success is due to the Large Language Model trained on huge amounts of data from the web and other sources. But perhaps an equal part is due to reinforcement learning helping refine those models so they can follow instructions, do some level of reasoning, use other software tools and so on.

Reinforcement Learning is also used heavily in robotics, for instance, teaching bipedal robots how to balance and walk. Or, teaching your robot vacuum how to navigate your house. So it's definitely worth spending the time digging in and understanding it!

This is a fun chapter, as one of the classic introduction problems for introducing Reinforcement Learning is navigating a maze. So have fun watching our little animated robot try to reach the goal and avoid the traps!

This chapter gets into the details of how Large Language Models actually work. It builds on several previous chapters (NLP, Neural Networks, and Reinforcement Learning).

It may surprise you how these models actually process text and learn!

We start at the high level and go through each part of a transformer model (which is the AI architecture LLMs are based on) one component at a time.

Because we aren't getting into the math or coding, you won't be able to go off and build your own after this, but you will understand each of the components and have a sense of how these models work and what they are doing. As we cover in this chapter, understanding how they work is one thing, but understanding why this works, and why this works the best (for now), is not so intuitive...

Large language models and the applications that depend on them are for many part of daily life -- both at home and at work.

This chapter looks at getting more out of these models. We cover prompting strategies and go through examples on how you can use AI to up-level your use of AI.

Along with Large Language models, terms like RAG, MCP, and agents are current buzzwords -- and often they are misunderstood. So we break down each of these terms, explain what they do, and how they are currently and will continue to impact at work and in our social lives.

Perhaps the most impressive AI today is the ability to create images and videos. This technology is already impressive, but also still somewhat in it's infancy.

Like all other aspects of modern AI, the success didn't happen overnight, and it's based on years of previous research.

As you can imagine, generating images and video isn't the simplest of tasks, but with your knowledge of previous chapters we are able to introduce some more concepts and explain at a reasonably detailed level just how that magic works.

We'll introduce Convolutional Neural Networks, which you won't be hugely surprised to hear builds on the earlier Neural Networks chapter, and combined with your knowledge of encoder/decoders from Chapter 8, we can outline how you can type in some text, and an image of what you asked for appears.

We already live in a world with AI, but what do these recent technologies mean for society, business, and you?

With AI moving so fast, tools and fashions changing daily, how can anyone keep up? And what are the practical things you can do as an employee, or a business leader, to adopt AI in your business or your own personal work practices?

We look at some of the general trends, and give general principles to try and navigate this fast-moving world.

I think everyone will realise this is a HUGE topic. This chapter serves as a high level introduction as we cannot possible hope to cover breadth and depth in this course.

It would be remiss to not include some debate on the risks, regulation, and optimism in an AI course however. So if all this is new to you, I hope it proves and instructive guide. If you are already deeply in this area, then I hope my perspective can add something new to your thought process.

What can I say? If you've made it this far, well done!

I hope you've enjoyed the course, but most of all, I hope this has opened the gateway to a whole world of learning even more about AI.