A (very) basic guide to artificial intelligence Premium

Understanding artificial intelligence, linear separability, neural networks, transformers, and the role of GPUs in machine learning.

Intelligence is the capacity of living beings to apply what they know to solve problems. ‘Artificial intelligence’ (AI) is intelligence in a machine. There is currently no one definition of AI.

A simple place to begin is with AI’s materiality, as a machine-software combination.

A simple example-problem in AI is linear separability. You plot some points on a graph and then find a way to draw a straight line through the graph such that it divides the points into two distinct groups.

Let’s make this problem more abstract. For example, how would a machine differentiate between a cat and a dog?

Say you give the machine 1,000 pictures of cats and 1,000 pictures of dogs, and ask it to separate them. (This task is usually not given to a linear classifier but it illustrates a point.) You also equip the machine with tools — say, a camera and an app that can measure distances of different parts of an image, can analyse depth (using trigonometry), and can assess colours.

The machine can proceed by classifying the cat- and dog-pictures in different ways, say, by shape of the face, shape of the eyes, shape of the paw, body size, size of the tongue, fur colours, etc. Because the machine has the necessary computing power, it can plot these features two at a time on a graph. For example, the x-axis can represent the slope of the face and the y-axis the length of the paw. Or it can plot them three at a time in a 3D graph.

In all these cases, you watch until the machine has found a way to separate the pictures into two groups such that one group is mostly cats and the other is mostly dogs. At this point, you stop the machine.