This will be a series of posts intended to provide a basic understanding of what artificial intelligence is and how it works to people with little-to-no maths or computer science background. Almost everyone has encountered AI at some stage, somewhere online. I don't think it would be an exaggeration to say that AI is, by far, the loudest technological revolution of our lifetimes. Because of all this noise around the subject, I have found that nearly everyone outside the field has difficulty keeping up [1], and there are a number of common misunderstandings and misconceptions associated with it that are hampering productive discussions. Eighty years of science-fiction writing and futurist speculation have popularised a great many ideas, hopes, and fears about AI that are misleading at best, and simply untrue at worst. AI has also very rapidly become a highly politicised flashpoint, sometimes with good reason, but often without. My aim with this series is to clear up this confusion as much as possible.
Another AI Explainer? Really?
As it happens, everything I can cover here has certainly already been said many times. The internet is filled with educational content about AI, much of which was vital to my own studies. However, there appears to be a gap in audience coverage. All of the highest quality content I have seen on this subject (the best of which, for my money, is the 3Blue1Brown series on machine learning [2]) depends on a baseline knowledge of maths that most people don't have (and otherwise don't need). What is available for those outside the field is often lacking, not because it skips technical details, but because it is unhelpfully high-level or misunderstands the subject matter [3].
AI for Normal People is my attempt to split the difference. This series will build an intuition for how AI works and what it can do, while including absolutely as little formal mathematical notation as I can get away with. My goal is for my non-techy family and friends to come away with a clearer lens through which to interpret the AI news they hear, and the ability to approach the AI systems they encounter in their lives and work with a better understanding of what they can expect. Most AI systems that most people encounter are unique in that not only do they lack an instruction book, even the designers of the systems don't fully understand everything that can be done with them. Understanding the guts of these tools is, in my view, the best way to use them effectively and to decide when not to.
Terminology is Hard
One of the least helpful quirks of the current AI boom is that it abounds with buzzwords and jargon, and sometimes the two switch places.
The term "Artificial Intelligence" itself is something of a problem child. The trouble is that it is vague. Consider the following:
- An abacus
- A calculator
- Deep Blue (the chess bot that beat Garry Kasparov in 1997)
- YouTube's video recommendation engine
- Nano Banana 2 (Google's image generator)
- ChatGPT
- C-3PO (if he actually existed)
Which of these would you consider an "AI", and why? Where do we draw the line? There are a few possible answers here:
Between 4 and 5: I think this is what most people without computer science experience would say. Image generators, chatbots, and sci-fi droids are all AI. If you are exposed to AI discourse in the news or through advertising, this is almost certainly the definition they (implicitly) use:
AI is a computer program that does things people can do which computers have traditionally found difficult, like drawing an image, writing a poem, or translating between languages.
The obvious problem with this definition is that it's a moving target. What "computers find difficult" changes whenever someone develops a new way to achieve these tasks. Once upon a time, Deep Blue was considered an "AI" despite being basically a conventional program (I'll come back to what "conventional" means in a moment [4]), and the line at the time was between 2 and 3. Computers no longer find it difficult to draw, write, or translate, so we must look for a new line to draw.
Between 5 and 6: This is the view that nothing we currently have is really AI, and it is not without its supporters [5]. The argument goes that "intelligence" demands certain capabilities like abstract and symbolic reasoning, physical intuition, and continual learning. Modern technology does not currently do this (though even this is debated). ChatGPT is fundamentally linguistic, not physical and embodied. This is a respectable enough definition, but defies the typical use of the term "AI", claiming that no such thing has ever existed, no matter what the rest of the list can do.
Between 3 and 4: This is as close as you'll get to a "standard" view from within the field itself. The difference between Deep Blue and the YouTube recommendation engine is that the latter integrates "neural networks" and various other kinds of "machine learning", while Deep Blue does not [6] (when I call Deep Blue a "conventional" program, I mean that it does not involve machine learning at any stage). On this definition, "machine learning" and "artificial intelligence" are basically synonymous.
Now, machine learning and neural networks are themselves jargon, and they mean something more specific than their names might imply. This is what I will be discussing in these posts, and as such I will be adopting this definition for "AI". In truth, I prefer to avoid using the term in general, precisely because it is so vague and contested, and I will tend to use "machine learning" (ML) or "deep learning" (DL) instead of AI for the rest of this series.
[1] They're not alone; I doubt there are any professional AI researchers who are keeping up either.
[2] https://www.youtube.com/watch?v=aircAruvnKk&list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi
[3] I once saw an AI tutorial which explained that "AI is made of data and algorithms", which is not so much wrong as it is borderline meaningless.
[4] In fact, Deep Blue is probably quite a bit better at chess than ChatGPT (until maybe fairly recently).
[5] Like Yann LeCun, Meta's former AI chief scientist and currently running his own AI company AMI Labs
[6] When I first drafted this post my example was Stockfish, the current top chess engine, but it turns out that a few years ago they also started integrating neural networks, while Deep Blue did not.