Not everything has gone smoothly for Android, however. Charles Covin, a Forrester Research analyst covering Android, says "I think the Android platform is a long-term play, and its short-term hiccups are no surprise. Google is intent on reaching consumers wherever they can, and it's clear that, while Internet use on mobile phones is still limited, it is the next venue where Google can expect to interact with its customers."
Facial recognition search
Image search is a burgeoning market that is woefully untapped. Today, when you type "Paris Hilton" at Google.com, you'll find images that other users have tagged. Yet tagging is a tedious process. At Flickr.com, for example, many images are left untagged, making it impossible to find them by searching. The more images stored without tags, the harder it is to find them.
At Google, new facial recognition technology will make it easier to find untagged images. Unlike the technology used for biometrics -- where you can pass through a security checkpoint when a video camera confirms your identity -- this image search is purely for finding the information you want.
"What Google did for text, we want to do for vision," says Shumeet Baluja, a Google research scientist. "We want to make images just as searchable and accessible as text."
Imagine this scenario: Five years from now, when all of your digital photos are stored online, you decide you want to search for pictures of your grandmother. With Google facial recognition technology, you might start with a source scan that measures the distance between the eyes, arrangement of nose, ears, eyes and other data. In seconds, you find every image you ever uploaded -- and any image stored anywhere online.
Translation has been around for years, especially as part of search engines such as Alta Vista. Google has made progress with the vast number of languages it has made available for translation, including Russian, Arabic and the recent addition of Hindi. Another innovation is in researching the rules applied to machine translation based on cultural phenomena of languages, which requires a great deal of computer processing.
"The more rules used, the better the quality of the translation," says Franz Och, a Google machine translation research scientist. "If you want to perform an English-to-Hindi translation, for example -- which has a small subset of the language pairs [matching words] of French or Spanish -- the smaller the language, the more important machine translation becomes. Finnish is a challenging language because of the morphology. One word could have all kinds of information inherent to it. Other language translations are more complicated because there are so many differences between the languages. Nice languages with historic roots and similarities are easier, like French to English."