I ask myself if this time AI is different, if the 2026 hype is going to last. And every time, I remember this question has already been asked before. Twice, actually, and both times the answer was no, the money ran out and the labs pulled down the shutters. This chapter is the story of […]
READ MORELast chapter I promised we’d come back to the map, so here it is: what types of AI exist, and why almost everything you’ve seen in this series so far, without exception, falls into the lowest and most boring of the three tiers. This chapter separates what already exists from what’s still, at best, a […]
READ MOREBefore we get into classic machine learning, neural networks, and language models, this chapter takes a different shape: no history, no philosophy, just the minimum toolkit of math for AI you need for the rest of the series to actually click. No heavy formulas, no exams, no flashbacks to university. Just the intuition behind three […]
READ MOREThree different ways to learn: with correct answers, without them, or by trial and error. Three ways to learn In 1959, IBM engineer Arthur Samuel published a paper with a title that sounds obvious today and was a complete novelty back then: “Some Studies in Machine Learning Using the Game of Checkers.” That’s where the […]
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