As we all know, AI has become a buzzword over the past year or so. Despite its omnipresence, you may still feel unclear on what it really is, or what’s so special about it. In this post, we’re going to break down some AI fundamentals to help you feel more in the loop the next time it comes up in conversation or you see it mentioned online.

To understand AI, we need to look at what came before it. In traditional computing, humans give instructions to computers (in the form of code) to achieve specific goals or perform tasks. This method works well for many applications: it underpins how this website was made, and the web browser running it, and the operating system running the web browser.

However, this approach is limited by human ability to find the best way of achieving a goal and communicating that through code. For example, trying to give computers the ability to recognize objects in pictures using this approach has proven very difficult.

Enter AI. What makes AI special is that the computer can learn for itself. Rather than being given explicit instructions, an AI is given a goal and it experiments with various approaches to find the most effective way to achieve that goal. These AI-driven approaches are often more efficient and can be quite counterintuitive compared to human approaches.

Now that we’ve defined AI as essentially software that can learn on its own, let’s look at the two broad types of AI: analytical AI and generative AI.

Analytical AI has been around for a longer time than generative, and can performs tasks like analyzing text, audio, images, video.

For instance, Analytical AI is how we have taught computers to recognize objects within images, which means, for example you can now take a photo of a plant and your phone can identify it. For the most part, the computer hasn’t been instructed on how to do this, but it has been given a large enough dataset that it has been able to ‘learn’ to perform this task itself.

This then takes us to Generative AI, which is behind much of the recent hype around AI. Generative AI is able to do exactly that, generate (or create) new content, in the form of text, audio, images, or video. A tool like ChatGPT, at its core, generates text, images, and speech. For example, if I ask ChatGPT to “generate me an image of a person driving a car in space” it can do exactly that:


Or if I ask it to “write me a poem about doing healthcare market research” here’s what we get:

What’s more, AI is now able to ‘understand’ language, which fundamentally changes the way we can now interact with computers. Rather than having to remember which buttons in the toolbar perform the actions what you want, there are versions of word, powerpoint and excel that now work by you simply typing in natural what actions you’d like them to perform, e.g. “create me a deck out of the notes from my call this morning”.

So to summarise, when we talk about AI we are essentially talking about computers being able to learn for themselves, which allows them to perform tasks that weren’t possible before like image recognition, turning text into a video or understanding natural language.

For more on how AI is expected to impact healthcare specifically, read here


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