Languages are like species. They evolve in mostly predictable ways, splitting into new species or dying out over time. Now, a group of linguists and computer scientists in the US and Canada have created a piece of software that can analyze enormous groups of languages to reconstruct what the earliest human languages might have sounded like.
It sounds like a subplot from Neal Stephenson's novel Snow Crash, but it's quite real. By using this program and others like it, linguists may one day know how people sounded when they talked 20,000 years ago, long before there was writing.
University of British Columbia statistician Alexandre Bouchard-Côté began working on the program when he was a graduate student at UC Berkeley. He used common algorithms to compare sounds and cognates - words that are the same in multiple languages - across hundreds of different modern languages.
By doing this, he could predict which language groups were most related to each other, and which kinds of sounds would be preserved most often. A sound that remained the same across distantly related languages was probably a sound that existed early in our linguistic evolutionary tree.