简介：Deception is an integral part of human nature and it is estimated we all lie up to nine times a day. But what if we created a world in which we couldn't lie? In a radical experiment, pioneering scientists from across Europe have come together to make this happen.
简介：拉姆·达斯（Ram Dass）,原名理查德·阿尔伯特，是20世纪60年代哈佛大学心理学教授，后为追求人生真义，赴印度灵修达数十年之久。他被称为20世纪最受推崇的心灵导师。1971年，拉姆·达斯首部著作——绘本《活在当下》”(Be here now)出版，迅速在美国热销200多万册，其“活在当下的理念唤醒了整整一代人的心灵意识。
简介：An investigative look and analysis of gender disparity in Hollywood, featuring accounts from well-known actors, executives and artists in the Industry.
简介：In 1998, wildlife enthusiast and photographer Chris Packham had a remarkable encounter with the Orang Rimba, a tribe of hunter-gatherers in the rainforests of Sumatra, Indonesia. It was the first time he had ever seen people living in perfect harmony with their environment. One photograph in particular that Chris took, a picture of a young tribal girl, has since become immensely important to him as a barometer of how we are treating our planet. In this real-life detective story, with no clues as to her identity or whereabouts other than his original photograph, Chris sets off to Sumatra 20 years on to try to find her, the girl in the picture.
简介：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?