検索キーワード： ナレーター "Tom Chivers" すべてのカテゴリー

Tom Chivers follows hidden pathways, explores lost islands and uncovers the geological mysteries that burst up through the pavement and bubble to the surface of our streets. From Roman ruins to a submerged playhouse, from an abandoned Tube station to underground rivers, Chivers leads us on a journey into the depths of the city he loves.

Every day, most of us will read or watch something in the news that is based on statistics in some way. Sometimes it'll be obvious—'X people develop cancer every year'—and sometimes less obvious—'How smartphones destroyed a generation'. Statistics are an immensely powerful tool for understanding the world, but in the wrong hands they can be dangerous. Introducing you to the common mistakes that journalists make and the tricks they sometimes deploy, How to Read Numbers is a vital guide that will help you understand when and how to trust the numbers in the news.

This is an audiobook about AI and AI risk. But it's also more importantly about a community of people who are trying to think rationally about intelligence, and the places that these thoughts are taking them, and what insight they can and can't give us about the future of the human race over the next few years. It explains why these people are worried, why they might be right and why they might be wrong.

At its simplest, Bayes’s theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. But in Everything Is Predictable, Tom Chivers lays out how it affects every aspect of our lives. He explains why highly accurate screening tests can lead to false positives and how a failure to account for it in court has put innocent people in jail. A cornerstone of rational thought, many argue that Bayes’s theorem is a description of almost everything. But who was the man who lent his name to this theorem?

Thomas Bayes was an eighteenth-century Presbyterian minister and amateur mathematician whose obscure life belied the profound impact of his work. Like most research into probability at the time, his theorem - independently discovered a few years later by Pierre-Simon Laplace, the great French mathematician - was mainly seen as relevant to games of chance, like dice and cards. But its implications soon became clear, affecting fields as diverse as medicine, law and artificial intelligence.