Shelf one · How a machine learns
Machine Learning
Kitchen table
Imagine teaching someone to spot cats without ever writing down what a cat is. You just show them thousands of pictures – cat, not cat, cat, not cat – and one day they can do it themselves, even with cats they have never seen. That is the whole trick: instead of writing rules, you show examples and let the machine work out the rule. The catch is that it only knows what the pictures taught it. Show it nothing but house cats and its first tiger will go very badly.
Whiteboard
Fit a function to data: supervised when the labels exist, unsupervised when you are hunting for structure without them, self-supervised when the data is made to label itself. Everything downstream rests on one quiet assumption – that the distribution at inference resembles the distribution you trained on. Most production failures are not exotic. They are that assumption breaking while nobody was measuring it. The craft is less about picking the cleverest model and more about knowing what your data can and cannot testify to.
Boardroom
The system learns your patterns from your history, which means the quality of every answer is capped by the quality of the record you feed it. It does not know your policy. It knows what your organisation actually did, including the habits you would rather it had not learned. Budget for the data work first – cleaning, labelling, and keeping it current – because that, not the algorithm, is usually the constraint. And ask what happens when the world shifts: a model trained on last year is a photograph, not a camera.
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