A filmmaker forges an unusual friendship with an octopus living in a South African kelp forest, learning as the animal shares the mysteries of her world.
After years of swimming every day in the freezing ocean at the tip of Africa, Craig Foster meets an unlikely teacher: a young octopus who displays remarkable curiosity. Visiting her den and tracking her movements for months on end he eventually wins the animal's trust and they develop a never before seen bond between human and wild animal.

As tuition rates spiral beyond reach and student loan debt passes $1 trillion (more than credit card debt), IVORY TOWER asks: Is college worth the cost? From the halls of Harvard, to public colleges in financial crisis, to Silicon Valley, filmmaker Andrew Rossi assembles an urgent portrait of a great American institution at the breaking point. Through interviews profiled at Arizona State, Cooper Union, and Sebastian Thrun's Udacity-among several others-IVORY TOWER reveals how colleges in the United States, long regarded as leaders in higher education, came to embrace a business model that often promotes expansion over quality learning. Along the way we also find unique programs, from Stanford to the free desert school Deep Springs to the historically black all women's college Spelman, where the potential for life-changing college experiences endure.

An astonishing close encounter with some of the most exotic creatures inhabiting the hidden depths. In the realm of the giant octopus, the rainbow nudibranch (sea slug), and the Scorpion fish, viewers become a fearless undersea explorers, discovering the strange and unusual partnerships these 'star wars' creatures forge to ensure their survival, and learning how this cooperation allows life in this enchanting world to flourish. Filmmaker Howard Hall guides an astonishing adventure. Johnny Depp and Kate Winslet narrate as Green Sea Turtles gather so Surgeonfish can strip harmful algae from their shells. A Humboldt Squid changes color four times per second like a flashing strobe light. A Mantis Shrimp’s claws have the speed of a bullet in battling a hungry octopus. Brace yourself to be submerged in a wondrous new dimension.

Mammals' ability to learn new tricks is the key to survival in the knife-edge world of hunters and hunted. In a TV first, a killer whale off the Falklands does something unique: it sneaks into a pool where elephant seal pups learn to swim and snatches them, saving itself the trouble of hunting in the open sea. Slow-motion cameras reveal the star-nosed mole's newly-discovered technique for smelling prey underwater: it exhales then inhales a bubble of air ten times per second. Young ibex soon learn the only way to escape a fox - run up an almost vertical cliff face - and young stoats fight mock battles, learning the skills that make them one of the world's most efficient predators.

The film takes viewers behind the scenes of one of the world’s greatest institutions of learning. The film examines how this legendary establishment has continued to go about its regular activities while adapting to the digital revolution. 'Ex Libris: The New York Public Library' explains how libraries inform and educate in many ways: books, concerts, conferences, classes and much more. This library strives to inspire the study of advanced knowledge and to strengthen the community.

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?