NOVA: Prediction by the Numbers
2018 · 52 MIN · IMDB 7.4 · DIR. Daniel McCabe
Overview
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?
Dialogue Timeline — 1,029 lines
A SAMPLE ACROSS THE RUNTIME — EVERY LINE IS SEARCHABLE
00:00:01 The future unfolds before our eyes
00:03:00 To his surprise,
00:05:20 So we collected this data,
00:07:58 built on understanding probability
00:10:06 on the wheel are the numbers one through 36,
00:12:22 Blaise Pascal and Pierre de Fermat in the 1650s,
00:14:41 You could make them using mathematics
00:16:51 up through the atmosphere
00:19:00 It's amazingly crazy that it works
00:21:15 from your local forecaster
00:23:41 would be eight cups,
00:25:55 by many, including most scientific journals
00:28:28 to beat Donald Trump right up to election day
00:30:40 of the actual value that's in that pot
00:33:00 finding a random sample meant randomly dialing phone numbers
00:35:15 Nate Silver, the founder of the website FiveThirtyEight,
00:37:15 more than whether it's going to rain or not,
00:39:26 Today, every Major League Baseball team
00:41:44 Engaging
00:43:57 But as you flip it more times, those start to look like chance
00:46:19 with zero-one person on board
00:48:29 is machine learning,
00:50:37 If what you're doing is deciding
00:52:25 and prediction by the numbers
Cast & Characters
Dialogue Record
Production Record
Factoids
IMDb rating preserved from the supplied subtitle archive; it may differ from the current IMDb rating.
Local subtitle archive