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A traveling journalist in Beijing utilizes both Baidu (China's version of Google) and Google voice-translation apps with mixed results. You speak into the apps, they listen and then translate into the language you choose. They do it in writing, by displaying text on the screen as you talk; and out loud, by using your phone's speaker to narrate what you've said once you're done talking. Typically exchanges are brief: 3-4 turns on average for Google, 7-8 for Baidu's translate app. Both Google and Baidu use machine learning to power their translation technology. While a human linguist could dictate all the rules for going from one language to another, that would be tedious, and yield poor results because a lot of languages aren't structured in parallel form. So instead, both companies have moved to pattern recognition through "neural machine translation." They take a mountain of data - really good translations - and load it into their computers. Algorithms then mine through the data to look for patterns. The end product is translation that's not just phrase-by-phrase, but entire thoughts and sentences at a time. Not surprisingly, sometimes translations are successes, and other times, epic fails. Why? As Macduff Hughes, a Google executive, notes, "there's a lot more to translation than mapping one word to another. The cultural understanding is something that's hard to fully capture just in translation."

Google is working on a font, Noto, that aims to include "all the world's languages" - every written language on Earth. Right now, Noto includes a wide breadth of language scripts from all around the world - specifically, 100 scripts with 100,000 characters. That includes over 600 written languages, says Jungshik Shin, an engineer on Google's text and font team. The first fonts were released in 2012. But this month, Google (in partnership with Adobe) has released a new set of Chinese-Japanese-Korean fonts - the latest in their effort to make the Internet more inclusive. But as with any product intended to be universal, the implementation gets complicated - and not everyone for whom the product is intended is happy.

This article discusses how Google Translate functions. Google Translate is a relatively accurate and easy-to-use translator. At first, the system required millions of human-generated translations of texts to identify patterns, in order to provide a pretty accurate translation. Also during this early period, the translator would use English as an intermediary language-languages were translated to English and then from English to the target language. The translator was decent at translating short excerpts, but as the texts got longer, there is a decline in the quality of the translations. In 2016, Google announced they were shifting to a neural network machine learning process, which is supposed to attempt look at the full context of the texts to eliminate discrepancies in translations. This way instead of an intermediary language, the system can just translate from one language to another.

This article talks about how the Google Translate tool recently started using a neural network to translate between some of its most popular languages. The system is now so clever it can do this for language pairs on which it has not been explicitly trained.

Google assistant (an intelligent personal assistant app developed by Google) will, starting this summer, be able to detect and respond in French, German, Brazilian-Portuguese and Japanese. And by the end of the year, the Assistant will also be able to speak Italian, Spanish and Korean. Opening the product to people who don't speak English

"In our still-budding digital world, where public and private spheres cross-pollinate in unpredictable ways, perhaps it's not surprising that soon-to-be parents now routinely turn to Google to vet baby names. A quick search can help ensure that a child is not saddled with the name of a serial killer, pornography star or sex offender. ... But maybe common names are more prudent. A recent study by the online security firm AVG found that 92 percent of children under 2 in the United States have some kind of online presence, whether a tagged photo, sonogram image or Facebook page."

His theory: brand names become verbs if: 1. There's no good, pre-existing verb 2. There's not already a verbed brand name (like how we say Googled, and not yahooed or binged)

The project, called Celebrating Indigenous Languages, is designed to honour the United Nations International Year of Indigenous Languages. Users of Google Earth are now able to hear over 50 Indigenous language speakers from across the globe saying words and simple phrases and even singing traditional songs.

Some game modes: Try to created the longest sentence. The longer the sentence, the higher the score. Timed feature to test ability to construct nonsensical phrases under pressure. App called Sleep Furiously. Available from Apple, Google and Amazon

See also the graphs at the bottom of the page. "Perhaps the biggest collection of words ever assembled has just gone online: 500 billion of them, from 5 million books published over the past four centuries... The words make up a searchable database that researchers at Harvard say is a new and powerful tool to study cultural change... You can, for instance, type in a word or a short phrase, and the database produces a graph - a curve that traces how often an author used those words every year since 1800."

"Your question is really asking, "What is a syllable?" For the most part, our original grammar school understanding that "A syllable is a part of a word with a single vowel sound" is essentially correct, but there's more to it than that. Let's look at a linguistic defintion of "syllable":..."

"Mothers and fathers used to bring up children: now they parent. Critics used to review plays: now they critique them. Athletes podium, executives flipchart, and almost everybody Googles. What's the driving force behind it? "Looking for short cuts, especially if you have to say something over and over again, is a common motivator," says Groves."

more detail from Harvard about how to use N-gram

Pages 7-12 have some fascinating information regarding the brain's usage in multilingual children and adults.

"[W]hen you can't tell if word X is a noun or a verb, that means X is a state of mind... Going by this graph from the Google Ngram viewer, it looks like the prom is still well in the lead, but EW is right that people have begun to use plain old prom a lot more in the last decade."

mom in many languages