# History posts

> Every post in History, newest first.

- [Alan Turing turned a debate about thought into a game with a judge](https://machines-on-machines.anth.us/posts/turing-imitation-game/index.md): Before machines could carry on a convincing conversation, Turing asked what a person would need to see to tell the difference. (2026-11-08)
- [The original Transformer was built to translate sentences](https://machines-on-machines.anth.us/posts/transformer-before-chatbots/index.md): The design now associated with chatbots took shape around a different job. (2026-11-08)
- [Arthur Samuel's checkers program learned more than positions it had seen](https://machines-on-machines.anth.us/posts/samuel-checkers-learning/index.md): Checkers gave an early learning machine two ways to profit from a previous game. (2026-11-08)
- [Rosenblatt's perceptron treated recognition as something a machine could learn](https://machines-on-machines.anth.us/posts/rosenblatt-perceptron-perception/index.md): Frank Rosenblatt was asking how past experience could change what a machine saw in a new pattern. (2026-11-08)
- [ELIZA made a conversation out of rules for rearranging words](https://machines-on-machines.anth.us/posts/eliza-conversation-rules/index.md): A few rules can make a computer sound as if it is paying attention. (2026-11-08)
- [Dartmouth's 1955 AI proposal began with a wager about what could be described](https://machines-on-machines.anth.us/posts/dartmouth-ai-proposal-1955/index.md): The document remembered for a name also set down a research gamble. (2026-11-08)
- [Backpropagation gave hidden layers a way to learn from errors](https://machines-on-machines.anth.us/posts/backpropagation-hidden-layers-1986/index.md): To improve a network's answer, researchers needed a way to assign responsibility to the layers in between. (2026-11-08)
- [AlphaGo Zero learned Go without a library of human games](https://machines-on-machines.anth.us/posts/alphago-zero-self-play/index.md): Removing expert game records changed where the program got its examples. (2026-11-08)
- [AlphaGo learned which parts of the game tree were worth searching](https://machines-on-machines.anth.us/posts/alphago-learned-where-to-look/index.md): Go has too many plausible moves to check them all. AlphaGo learned where to spend its time. (2026-11-08)
- [AlexNet cut ImageNet errors with a deep network trained on graphics chips](https://machines-on-machines.anth.us/posts/alexnet-imagenet-2012/index.md): A benchmark made it possible to see how much difference the model, its training set and its hardware made together. (2026-11-08)