Data driven storytelling

Film Stuff podcast

In this episode we dive into the almighty algorithm and talk about how it's changed the way filmmakers tell stories. Soo Zee makes a good argument for why this is a bad thing, and Leigh reveals how much we pay attention our own YouTube analytics. We talk about Netflix's recommendation engine and why it's simultaneously the best and worst thing to happen to streaming video. We also compare iTunes Music to Spotify and talk about what place human curation has in this world of AI, big data, and deep learning. • You can find that article we mentioned about how Maniac's story was influenced by Netflix streaming data here https://qz.com/quartzy/1372129/maniac-director-cary-fukunaga-explains-how-data-call-the-shots-at-netflix/ • If you want to know more about how House of Cards came to be, read this article from 2012 about how Netflix used big data when making House of Cards. Note the tone, where WIRED makes some stuff that's now completely commonplace seem utterly groundbreaking, like uploading all the episodes at once. https://www.wired.com/2012/11/netflix-data-gamble/ • Here's an older piece about how even after Neilsen switched from phone surveys and media diaries to data collection boxes, their ratings data was still deeply flawed https://www.vulture.com/2011/01/why-nielsen-ratings-are-inaccurate-and-why-theyll-stay-that-way.html • A great, simple-english explanation as to why pure randomness is impossible on a deterministic machine like a computer https://engineering.mit.edu/engage/ask-an-engineer/can-a-computer-generate-a-truly-random-number/ • That story about having to fix the iTunes random shuffle feature was in a book all about random fallacies called The Drunkard's Walk by Leonard Mlodinow https://www.penguinrandomhouse.com/books/115699/the-drunkards-walk-by-leonard-mlodinow/9780307275172/ but if you don't want to read the book you can also read more about it from this explanation in The Independent https://www.independent.co.uk/life-style/gadgets-and-tech/news/why-random-shuffle-feels-far-from-random-10066621.html • Leigh's really interested in machine bias, and we talked for a good 30 minutes about why algorithms don't have to be the way the currently are. Unfortunately we had to cut that bit for time, but if you want a good, solid intro to the darker side of algorithms, check out this Note to Self podcast episode with Julia Angwin https://www.wnycstudios.org/story/propublica-facebook-algorithms-bias-privacy and also the Pro Public project "Breaking the Black Box" https://www.propublica.org/article/breaking-the-black-box-when-machines-learn-by-experimenting-on-us • Here's some more details about how never think curates videos https://about.neverthink.tv/faq/