As OpenAI ran out of quality data for GPT-4, it abandoned all filtering and scraped whatever it could find: Twitter links, YouTube transcripts, code repositories, even content from Pastebin. This 'data swamp' approach meant training on the internet's darkest corners - hate speech, conspiracy theories, explicit content.

The quality degradation was stark: GPT-2 used carefully curated Reddit posts with at least 3 upvotes. GPT-4 included random web garbage with no quality controls. This created the need for traumatic content moderation work outsourced to vulnerable workers in Kenya who had to sort through AI-generated descriptions of the worst human behaviors.

Section: 2, Chapter: 9

Author: Karen Hao

Modern casinos are increasingly using data analytics and machine learning to model player behavior and optimize every aspect of their gaming floors. Pioneered by Gary Loveman, former CEO of Caesars Entertainment, casinos now employ teams of MIT-trained mathematicians and data scientists to determine everything from the optimal slot machine hold percentage to the personalized comps offered to loyal high rollers.

By tracking player activity through loyalty cards, mining behavioral data, and running experiments, mega-resorts can create a perfectly tuned ecosystem that maximizes both player satisfaction and casino winnings.

Section: 1, Chapter: 3

Author: Nate Silver