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Lost Language Meanings Found By Machine Learning
A machine-learning system capable of deciphering lost languages has been devised by Jiaming Luo and Regina Barzilay from MIT and Yuan Cao from Google’s AI lab in Mountain View, California. It has successfully deciphered Linear B—the first time this has been done automatically.
Now Luo and co have gone further to show how machine translation can decipher languages that have been lost entirely. The constraint they use has to do with the way languages are known to evolve over time.
The idea is that any language can change in only certain ways—for example, the symbols in related languages appear with similar distributions, related words have the same order of characters, and so on. With these rules constraining the machine, it becomes much easier to decipher a language, provided the progenitor language is known.
(Via Technology Review.)
This seems like a pretty good implementation of the translator discs from science fiction writer Larry Niven's magisterial 1970 novel Ringworld:
Louis wore his inside his left wrist. The discs were linked to the autopilot aboard the Liar. They should work over such a distance, and the Liar's autopilot should be able to translate any new language...
The tattooed one made a short speech. That was luck. The autopilot would need data before it could begin a translation...
Presently the discs were filling in words and phrases... His voice was almost a chant, almost a recital of poetry. The autopilot was translating Louis's words into a similar chant, though it spoke to Louis in a conversational tone. Louis could hear the other translator discs whistling softly in Puppeteer, snarling quietly in the Hero's Tongue.
Compare to translatophone (1901) by Frank Stockton, the Babel fish from The Hitchhiker's Guide to the Galaxy (1979) by Douglas Adams and Language Rectifier from Ralph 124c 41 + (1911) by Hugo Gernsback.
A couple of years ago, a German team of researchers showed how [an] approach with much smaller databases could help translate much rarer languages that lack the big databases of text. The trick is to find a different way to constrain the machine approach that doesn’t rely on the database.
"Stay on your vehicles," Speaker ordered in a low voice. "Wait until they reach us. Then dismount. I assume we are all wearing our communicator discs?"
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I might also be thinking of K-9 from Doctor Who.
