Pythia (artificial intelligence) (nonfiction): Difference between revisions
No edit summary |
No edit summary |
||
| Line 7: | Line 7: | ||
https://engineering.fb.com/ai-research/pythia/ | https://engineering.fb.com/ai-research/pythia/ | ||
== Pythia (text restoration) | == Pythia (text restoration) == | ||
A model for restoring ancient text. Pythia recovers missing characters from a damaged text input using deep neural networks. Bringing together the disciplines of ancient history and deep learning, the present work offers a fully automated aid to the text restoration task, providing ancient historians with multiple textual restorations, as well as the confidence level for each hypothesis. | A model for restoring ancient text. Pythia recovers missing characters from a damaged text input using deep neural networks. Bringing together the disciplines of ancient history and deep learning, the present work offers a fully automated aid to the text restoration task, providing ancient historians with multiple textual restorations, as well as the confidence level for each hypothesis. | ||
| Line 18: | Line 18: | ||
There are thousands of ancient inscriptions we already know about, with dozens more discovered every year. Unfortunately, many have become eroded or damaged over the centuries, resulting in segments of text being lost. Figuring out what the gaps could be is a difficult task, involving looking at the rest of the inscription and other similar texts. | There are thousands of ancient inscriptions we already know about, with dozens more discovered every year. Unfortunately, many have become eroded or damaged over the centuries, resulting in segments of text being lost. Figuring out what the gaps could be is a difficult task, involving looking at the rest of the inscription and other similar texts. | ||
[[Yannis Assael (nonfiction)|Yannis Assael]] at [[DeepMind (nonfiction)|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. | |||
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 recognise 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. | The AI, called Pythia, learned to recognise 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. | ||
| Line 50: | Line 48: | ||
* [[Artificial intelligence (nonfiction)]] | * [[Artificial intelligence (nonfiction)]] | ||
* [[Yannis Assael (nonfiction)]] | |||
* [[DeepMind (nonfiction)]] | |||
== External links == | == External links == | ||
Revision as of 13:39, 20 October 2019
In the field of artificial intelligence, Pythia may refer to:
Pythia (vision and language)
A deep learning framework from Facebook which supports multitasking in the vision and language domain. Built on Facebook's open-source PyTorch framework, the modular, plug-and-play design enables researchers to quickly build, reproduce, and benchmark AI models. Pythia is designed for vision and language tasks, such as answering questions related to visual data and automatically generating image captions.
https://engineering.fb.com/ai-research/pythia/
Pythia (text restoration)
A model for restoring ancient text. Pythia recovers missing characters from a damaged text input using deep neural networks. Bringing together the disciplines of ancient history and deep learning, the present work offers a fully automated aid to the text restoration task, providing ancient historians with multiple textual restorations, as well as the confidence level for each hypothesis.
Artificial intelligence is learning to decipher damaged ancient Greek engravings. The AI seems to be better than humans at filling in missing words, but may be most useful as a collaborative tool, where researchers use it to narrow down the options.
There are thousands of ancient inscriptions we already know about, with dozens more discovered every year. Unfortunately, many have become eroded or damaged over the centuries, resulting in segments of text being lost. Figuring out what the gaps could be is a difficult task, involving looking at the rest of the inscription and other similar texts.
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 recognise 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.
Source: DeepMind AI beats humans at deciphering damaged ancient Greek tablets @ New Scientist
To do
TODO: establish if the second Pythia is built on the first Pythia, or not.
In the News
Fiction cross-reference
- Crimes against mathematical constants
- Gnomon algorithm
- Gnomon Chronicles
- Killer poke
- Mathematician
- Mathematics
Nonfiction cross-reference
External links
- Pythia (disambiguation) @ Wikipedia