Blog
A few times a year, when something is interesting enough to be worth explaining properly. Mathematics, physics, computer science, and the corners of artificial intelligence and machine learning that turn out to matter later. Each one is summarised below and published in full on Medium.
Searches for "Never Gonna Give You Up" track Apple's stock price almost move for move, and that fact says nothing about either one. What the correlation coefficient actually measures, and why a real cause reliably shows up as a correlation while a correlation on its own tells you nothing about the cause.
A qubit is not a coin spinning in the air with the answer already decided. What a bit actually is, what superposition adds to it, and why "0 and 1 at the same time" is the wrong way to say it — no equations required.
The normal distribution is the default assumption in applied statistics, and most of the time nobody checks whether it earns that status. What the central limit theorem actually promises, and what it does not.
Part II — why the problem has resisted every obvious line of attack, what a proof in either direction would actually have to look like, and what would change the day someone finds one.
Part I — what the question is really asking, why "easy to check" and "easy to solve" are not the same thing, and how a problem about algorithms became a question about the limits of knowledge.