Ignore the clickbait on YouTube and you’ll discover a rich well of videos that stimulate brain cells rather than generate dopamine hits
Dick Pountain is editorial fellow of PC Pro and still just about capable of performing useful work on his surroundings. Email dick@dickpountain.co.uk
For all its faults, YouTube offers an unprecedented breadth and depth of content. There’s all kinds of music, craft and sport on offer, even movies. Then there’s the educational side. I watch a lot of mathematics videos, and while most lack the professional depth you find on Wikipedia, they often make up for that by exploiting visualisation in original and useful ways.
Then there’s my favourite topic of all: science. Okay, many YouTube videos are of increasingly dubious, click-baity quality, and that applies to much of its science content, too. Unsurprisingly the science on YouTube is dominated by cosmology and particle physics, those perennial playgrounds for opinionated nerds who know that Einstein or Bohr were wrong (or right, or whatever). It’s created a handful of stars like Sabine Hossenfelder and Derek Muller who talk sense and explain stuff well, but the nature of the medium means these get drowned out by the charlatans for most non-scientist viewers.
There are, however, “brands” that guarantee seriousness, and one of the best is the Royal Institution (RI). Head to its homepage and you can find Carlo Rovelli explaining white holes, learn about Humphry Davy and Michael Faraday, and discover how the Braggs created the field of crystallography.
I always watch the RI’s Christmas lecture, because I like bangs, but this year YouTube offered me as follow-up a lecture by Prof Tim Palmer. His video about uncertainty and probability blew my mind, and pushed me to read his book The Primacy of Doubt.
Palmer works in mathematical physics and is an expert on chaos theory, turning his knowledge to practical use in weather forecasting; the huge extension of reliable forecasting in recent years is largely thanks to the method of “ensemble forecasts” he pioneered. Shocked by the failure of the Met Office to predict the 1987 hurricane, Palmer realised that the deterministic supercomputer models then in use were prone in rare cases to chaotically wrong prediction, which could be mitigated by feeding the model multiple starting conditions that differ by small additions of randomness. Instead of a single prediction, this produces a brush-like clump of predictions, the central trend of wh
