An anonymous reader quotes a report from New Scientist: Yannis Assael at DeepMind and his colleagues trained a neural network, a type of AI algorithm, to guess missing words or characters from Greek inscriptions, on surfaces including stone, ceramic and metal, that were between 1500 and 2600 years old. The AI, called Pythia, learned to recognize patterns in 35,000 relics, containing more than 3 million words. The patterns it picks up on include the context in which different words appear, the grammar, and also the shape and layout of the inscriptions.

Given an inscription with missing information, Pythia provides 20 different suggestions that could plug the gap, with the idea that someone could then select the best one using their own judgement and subject knowledge. “It’s all about how we can help the experts,” says Assael. To test the system, the team hid nine letters of a Greek personal name from Pythia. It managed to fill in the blanks. In a head-to-head test, where the AI attempted to fill the gaps in 2949 damaged inscriptions, human experts made 30 per cent more mistakes than the AI. Whereas the experts took 2 hours to get through 50 inscriptions, Pythia gave its guesses for the entire cohort in seconds. The arXiv paper is available here.

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Source: Slashdot – DeepMind AI Beats Humans At Deciphering Damaged Ancient Greek Tablets