The expanding universe is a complicated place. During inflation the universe expanded faster than light, but that's something that actually happens all the time, it's happening right now. This doesn't violate Einstein's theory of relativity since nothing is moving through space faster than light, it's just that space itself is expanding such that far away objects are receding rapidly from each other. Common sense would dictate that objects moving away from us faster than light should be invisible, but they aren't. This is because light can travel from regions of space which are superluminal relative to us into regions that are subluminal. So our observable universe is bigger than our Hubble sphere - it's limited by the particle horizon, the distance light could travel to us since the beginning of time as we know it.
2014 • Astronomy
A witty and mind-expanding exploration of data, with mathematician Dr Hannah Fry. This high-tech romp reveals what data is and how it is captured, stored, shared and made sense of. Fry tells the story of the engineers of the data age, people most of us have never heard of despite the fact they brought about a technological and philosophical revolution. For Hannah, the joy of data is all about spotting patterns. Hannah sees data as the essential bridge between two universes - the tangible, messy world that we see and the clean, ordered world of maths, where everything can be captured beautifully with equations. The film reveals the connection between Scrabble scores and online movie streaming, explains why a herd of dairy cows are wearing pedometers, and uncovers the network map of Wikipedia. What's the mystery link between marmalade and One Direction? The film hails the contribution of Claude Shannon, the mathematician and electrical engineer who, in an attempt to solve the problem of noisy telephone lines, devised a way to digitise all information. Shannon singlehandedly launched the 'information age'. Meanwhile, Britain's National Physical Laboratory hosts a race between its young apprentices in order to demonstrate how and why data moves quickly around modern data networks. It's all thanks to the brilliant technique first invented there in the 1960s by Welshman Donald Davies - packet switching. But what of the future? Should we be worried by the pace of change and what our own data could be used for? Ultimately, Fry concludes, data has empowered all of us. We must have machines at our side if we're to find patterns in the modern-day data deluge. But, Fry believes, regardless of AI and machine learning, it will always take us to find the meaning in them.
2016 • Math
Professor Marcus du Sautoy concludes his investigation into the history of mathematics with a look at some of the great unsolved problems that confronted mathematicians in the 20th century. After exploring Georg Cantor's work on infinity and Henri Poincare's work on chaos theory, he sees how mathematics was itself thrown into chaos by the discoveries of Kurt Godel and Paul Cohen, before completing his journey by considering some unsolved problems of maths today, including the Riemann Hypothesis.
Professor David Spiegelhalter tries to pin down what chance is and how it works in the real world. A blend of wit and wisdom, animation, graphics and gleeful nerdery is applied to the joys of chance and the mysteries of probability, the vital branch of mathematics that gives us a handle on what might happen in the future. How can you maximise your chances of living till you're 100? Why do many of us experience so many spooky coincidences? Should I take an umbrella? These are just some of the everyday questions the film tackles as it moves between Cambridge, Las Vegas, San Francisco and Reading. Spiegelhalter discovers One Million Random Digits, a book full of hidden patterns and shapes, introduces us to the unit called the micromort (a one-in-a-million chance of dying), and uses the latest infographics to demonstrate how life expectancy has increased in his lifetime and how it is affected by our lifestyle choices - drinking, obesity, smoking and exercise.
2012 • Math
Hannah goes back to the time of the ancient Greeks to find out why they were so fascinated by the connection between beautiful music and maths. The patterns our ancestors found in music are all around us, from the way a sunflower stores its seeds to the number of petals in a flower. Even the shapes of some of the smallest structures in nature, such as viruses, seem to follow the rules of maths. All strong evidence for maths being discovered. But there are those who claim maths is all in our heads and something we invented. To find out if this is true, Hannah has her brain scanned. It turns out there is a place in all our brains where we do maths, but that doesn't prove its invented. Experiments with infants, who have never had a maths lesson in their lives, suggests we all come hardwired to do maths. Far from being a creation of the human mind, this is evidence for maths being something we discover. Then along comes the invention of zero to help make counting more convenient and the creation of imaginary numbers, and the balance is tilted in the direction of maths being something we invented. The question of whether maths is invented or discovered just got a whole lot more difficult to answer.
1/3 • Magic Numbers: Hannah Fry's Mysterious World of Maths • 2018 • Math
Predictions underlie nearly every aspect of our lives, from sports, politics, and medical decisions to the morning commute. With the explosion of digital technology, the internet, and “big data,” the science of forecasting is flourishing. But why do some predictions succeed spectacularly while others fail abysmally? And how can we find meaningful patterns amidst chaos and uncertainty? From the glitz of casinos and TV game shows to the life-and-death stakes of storm forecasts and the flaws of opinion polls that can swing an election, “Prediction by the Numbers” explores stories of statistics in action. Yet advances in machine learning and big data models that increasingly rule our lives are also posing big, disturbing questions. How much should we trust predictions made by algorithms when we don’t understand how they arrive at them? And how far ahead can we really forecast?