In a meeting at Google in 2004, the discussion turned to an e-mail message the company received from a fan in South Korea. Sergey Brin, a Google founder, ran the message through an automatic translation service that the company had licensed.
The message said Google was a favourite search engine, but the result read: “The sliced raw fish shoes it wishes. Google green onion thing!”
Brin said Google ought to be able to do better. Six years later, its free Google Translate service handles 52 languages, more than any similar system, and people use it hundreds of millions of times a week to translate web pages and other text.
“What you see on Google Translate is state of the art” in computer translations that are not limited to a particular subject area, said Alon Lavie, an associate research professor in the Language Technologies Institute at Carnegie Mellon University.
Google’s efforts to expand beyond searching the web have met with mixed success.
But Google’s quick rise to the top echelons of the translation business is a reminder of what can happen when Google unleashes its brute-force computing power on complex problems.
The network of data centers that it built for web searches may now be, when lashed together, the world’s largest computer. Google is using that machine to push the limits on translation technology. Last month, for example, it said it was working to combine its translation tool with image analysis, allowing a person to, say, take a cellphone photo of a menu in German and get an instant English translation.
“Machine translation is one of the best examples that shows Google’s strategic vision,” said Tim O’Reilly, founder and chief executive of the technology publisher O’Reilly Media. “It is not something that anyone else is taking very seriously. But Google understands something about data that nobody else understands...”
Creating a translation machine has long been seen as one of the toughest challenges in artificial intelligence. For decades, computer scientists tried using a rules-based approach — teaching the computer the linguistic rules of two languages and giving it the necessary dictionaries.
In the mid-1990s, researchers began favouring a so-called statistical approach. They found that if they fed the computer thousands of passages and their human-generated translations, it could learn to make accurate guesses about how to translate new texts. It turns out that this technique, which requires huge amounts of data and lots of computing horsepower, is right up Google’s alley.
“Our infrastructure is very well-suited to this,” Vic Gundotra, a vice president for engineering at Google, said.
Automated translation systems are far from perfect, and even Google’s will not put human translators out of a job anytime soon. Experts say it is difficult for a computer to break a sentence into parts, then translate and reassemble them.
But Google’s service is good enough to convey the essence of a news article, and it has become a quick source for translations for millions.