Today's Reading

Supervised learning

Supervised machine learning is where you have a specific outcome in mind, and you program the algorithm to achieve it. A bit like some of your maths textbooks, in which you could look up the answer at the back of the book, and the tricky part was working out how to get there. It's supervised because, as the programmer, you know what the answers should be. Your challenge is how to get an algorithm to always reach the right answer from a wide variety of potential inputs.

How, for instance, can you ensure an algorithm in a self-driving car will always recognize the difference between red and green on a traffic light, or what a pedestrian looks like? How do you guarantee that the algorithm you use to help diagnose cancer screens can correctly identify a tumour?

This is classification, one of the main uses of supervised learning, in which you are essentially trying to get the algorithm to correctly label something, and to prove (and over time improve) its reliability for doing this in all sorts of real-world situations. Supervised machine learning produces algorithms that can function with great efficiency, and have all sorts of applications, but at heart they are nothing more than very fast sorting and labelling machines that get better the more you use them.

Unsupervised learning

By contrast, unsupervised learning doesn't start out with any notion of what the outcome should be. There is no right answer that the algorithm is instructed to pursue. Instead, it is programmed to approach the data and identify its inherent patterns. For instance, if you had particular data on a set of voters or customers, and wanted to understand their motivations, you might use unsupervised machine learning to detect and demonstrate trends that help to explain behaviour. Do people of a certain age shop at a certain time in a certain place? What unites people in this area who voted for that political party?

In my own work, which explores the cellular structure of the immune system, I use unsupervised machine learning to identify patterns in the cell populations. I'm looking for patterns but don't know what or where they are, hence the unsupervised approach.

This is clustering, in which you group together data based on common features and themes, without seeking to classify them as A, B or C in a preconceived way. It's useful when you know what broad areas you want to explore, but don't know how to get there, or even where to look within the mass of available data. It's also for situations when you want to let the data speak for itself, rather than imposing pre-set conclusions.

Making decisions: boxes and trees

When it comes to making decisions, we have a similar choice to the one just outlined. We can set an arbitrary number of possible outcomes and choose between them, approaching problems from the top down and starting with the desired answer, much like a supervised algorithm: for example, a business judging a job candidate on whether they have certain qualifications and a minimum level of experience. Or we can start from the bottom, working our way upwards through the evidence, navigating through the detail and letting the conclusions emerge organically: the unsupervised approach. Using our recruitment example, this would see an employer consider everyone on their merits, looking at all the available evidence—someone's personality, transferable skills, enthusiasm for the job, interest and commitment—rather than making a decision based on some narrow, pre-arranged criteria. This bottom-up approach is the first port of call for people on the autistic spectrum, since we thrive on bringing together precisely curated details to form conclusions—in fact we need to do that, going through all the information and options, before we can even get close to a conclusion.

I like to think of these approaches as akin to either building a box (supervised decision making) or growing a tree (unsupervised decision making).

Thinking in boxes

Boxes are the reassuring option. They corral the available evidence and alternatives into a neat shape where you can see all sides, and the choices are obvious. You can build boxes, stack them and stand on them. They are congruent, consistent and logical. This is a neat and tidy way to think: you know what your choices are.

By contrast, trees grow organically and in some cases out of control.

They have many branches and hanging from those are clusters of leaves that themselves contain all sorts of hidden complexity. A tree can take us off in all sorts of directions, many of which may prove to be decisional dead ends or complete labyrinths.

So which is better? The box or the tree? The truth is that you need both, but the reality is that most people are stuck in boxes, and never even get onto the first branch of a decision tree.

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