Podbit · Freakonomics Radio
Before neural network analysis tools, bad backgammon players could lose for years and still believe they were near-equals to the pros. When the AI arrived, it gave everyone an error rate — and the illusion shattered. The money game dried up almost overnight.
Where this was said
At 43:00 · chapter starts 42:30
Bob Wachtel had spent years accumulating a foot-high stack of position cards he couldn't solve even through extensive rollouts. When neural networks arrived, he finally got the answers — pure intellectual bliss. But the same tools that enlightened him revealed the truth to his opponents. A lawyer in Marin County who had lost to Wachtel for five years straight had convinced himself they were basically equals; the neural net analysis proved otherwise. Once weak players could see their error rates, many stopped playing for money. Mark Olsson contextualizes the technology: TD-Gammon, created by Gerald Tesauro at IBM in the 1990s, was the first neural network to beat the world's best human players at any board game — roughly contemporaneous with Deep Blue vs. Kasparov in chess. Backgammon was the ideal test environment because it was complex enough to be meaningful but small enough to simulate infinitely. Today, Backgammon Galaxy is built atop a successor to TD-Gammon, and the game at the elite level has shifted from inspired genius plays to something closer to the zero-error-rate standard of gymnastics.
In the 1990s, Gerald Tesauro created TD-Gammon, the first neural network to play backgammon at a superhuman level, pioneering reinforcement learning AI.
Gerald Tesauro chose backgammon as the case study for his new reinforcement learning algorithm in the 1990s because the game was complex enough to be meaningful yet small enough to simulate infinitely. TD-Gammon became the first AI to beat the world's best human players — predating AlphaGo by decades.
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