All right, don't panic, but computers have created their own secret language and are probably talking about us right now. Well, that's kind of an oversimplification, and the last part is just plain untrue. But there is a fascinating and existentially challenging development that Google's AI researchers recently happened across. You may remember that back in September, Google announced that its Neural Machine Translation system had gone live. It uses deep learning to produce better, more natural translations between languages. Following on this success, GNMT's creators were curious about something.

The Google Cloud, Google's eponymous artificial intelligence platform, is quite the capable little set of services. Its algorithms can handle everything from language translation to the identification of objects and landmarks. On Tuesday, Google Cloud chief Diane Greene announced the formation of a new team, the Google Cloud Machine Learning group, that will manage the Mountain View, California-based company's cloud intelligence efforts going forward. The group will be helmed by Jia Li, former head of research at Snapchat and pioneer behind the feature that lets you attach emojis to real-world objects, and Fe-Fei Li, former director of AI at Stanford. They will oversee a slew of upgrades to Google's cloud services in the coming months, much of which will involve Google Cloud's hardware infrastructure.

The Google Neural Machine Translation paper (GNMT) describes an interesting approach towards deep learning in production. The paper and architecture are non-standard, in many cases deviating far from what you might expect from an architecture you'd find in an academic paper. Emphasis is placed on ensuring the system remains practical rather than chasing the state of the art through typical but computationally intensive tweaks. To understand the model used in GNMT we'll start with a traditional encoder decoder machine translation model and keep evolving it until it matches GNMT. The GNMT evolution seems primarily motivated by improving accuracy while maintaining practical production speeds for both training and prediction. The encoder decoder architecture started the recent neural machine translation trend.

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Google says data from side-by-side evaluations, where human raters compare the quality of translations, shows the improvements achieved by Google Neural Machine Translation, or GNMT. Google boasts that its latest update to Google Translate has given it the biggest leap of the past decade in natural language translation. Google Translate might not be as good as humans at translating language, but the search giant's recent work using neural networks to improve speech recognition and computer vision has made it hard to beat in machine translation. The latest update to Google Translate utilizes Google's Neural Machine Translation (NMT) system for translating phrases, which is rolling out to eight language pairs. Google actually announced NMT in September in a paper describing how it's using neural networks to close the gap between human and machine translation.

Google says its Translate app now spits back more natural translations. Google said Tuesday it has vastly improved its Google Translate app, available on phones and the web. The search giant said it's now incorporating "neural machine translation" into the software, which means it can translate whole sentences at a time, instead of breaking the text down to smaller chunks and translating those pieces. The result is translations coming out more natural, with better syntax and grammar, Google said. "It has improved more in one single leap than in 10 years combined," Barak Turovsky, the product lead for Google Translate, said during a press event at Google's San Francisco office.

Google Translate is rolling out a major upgrade that promises more human-like language translations. Google is bullish on its Neural Machine Translation technology, claiming that it's a bigger upgrade to the service than everything that's been accomplished in the last ten years combined. The company is rolling out the improvements to eight language pairs in Google search, the Translate apps, and the website. You'll find the new technology behind translations between English and French, German, Spanish, Portuguese, Chinese, Japanese, Korean and Turkish. Google says that makes up more than 35 percent of all language queries.

Google is improving its language translation service with a new approach that interprets whole sentences at a time rather than phrases piece-by-piece, the search giant announced Tuesday. The firm says that should make translations from the service, called Google Translate, easier to understand. "It uses this broader context to help it figure out the most relevant translation, which it then arranges and adjusts to be more like a human speaking with proper grammar," Barak Turovsky, product lead for Google Translate, wrote in a new blog post. This new version of Google Translate is powered by neutral machine translation, which is a new method of teaching computers to translate human languages, according to the Association for Computational Linguistics. Google published updates regarding its research into this field in September; it's now integrating the technique directly into Google Translate.

What started with Mandarin Chinese is expanding to English; French; German; Japanese; Korean; Portuguese and Turkish, as Google has increased the languages its Neural Machine Translation (NMT) handle. "These represent the native languages of around one-third of the world's population, covering more than 35 percent of all Google Translate queries," according to The Keyword blog. The promise here is that because NMT uses the context of the entire sentence, rather than translating individual words on their own, the results will be more accurate, especially as time goes on, thanks to machine learning. For a comparison of the two methods, check out the GIF embedded below. Google says that the ultimate goal is to have all 103 languages in Translate using machine learning.

Google Cloud on Tuesday announced new offerings in Cloud Machine Learning, one of its fastest growing product areas. The new tools and services are making machine learning more accessible and giving businesses new ways to leverage the technology. First, Google is rolling out an entirely new API to help with job searches. It illustrates how machine learning can provide a simple solution for a complicated task: finding the best job listings based on a potential employee's preferences, including their search terms and factors like seniority and location. Early adopters of the Cloud Jobs API include CareerBuilder and Dice.com, two leading job sites.