Samsung has filed for a patent covering a mobile device that could run a second operating system via virtualization. The Samsung patent application, reported on SamMobile and MSPowerUser, covers all sorts of digital devices, according to Samsung: smartphones, tablet PCs, notebook computers, and more. "In this specification, the case where as an example, the first operating system is the android, the second operating system is the window (Windows) is shown," the patent's translation reads. But the patent also makes clear than any OS could be used, including Tizen, Linux, or MacOS. In the world conceived by the patent, the host OS would run the device, and the secondary OS would be run essentially as an app.

Google Crowdsource lets you help the company with language translation, handwriting recognition and map translation accuracy. Google has a formidable artificial intelligence team working on everything from photo recognition to email spam filtering. The online giant on Monday released a new Android app called Crowdsource that lets you contribute your own suggestions to language translation, handwriting recognition and street sign transcription. "Each microtask takes no more than five to 10 seconds, so knock away a few the next time you find yourself with a few moments to kill," Google suggests in the app description. "Every time you use it, you know that you've made the internet a better place for your community."

Online retailer eBay is attempting to extend its machine language capabilities beyond automatic language translation to e-commerce uses designed to make product searches more relevant. As automation improves, the company said one goal eliminating the search box. Meanwhile, development cycles have been reduced as more machine learning libraries are released to the open source community. "As machines get better at decoding natural language, commerce should become increasingly conversational -- eventually rendering the search box redundant," eBay CEO Devin Wenig noted recently. Wenig added that the pace of machine intelligence development has quickened over the last year.

So how do we program a computer to translate human language? The simplest approach is to replace every word in a sentence with the translated word in the target language. This is easy to implement because all you need is a dictionary to look up each word's translation. But the results are bad because it ignores grammar and context. So the next thing you might do is start adding language-specific rules to improve the results.

Neural machine translation aims at building a single large neural network that can be trained to maximize translation performance. The encoder-decoder architecture with an attention mechanism achieves a translation performance comparable to the existing state-of-the-art phrase-based systems on the task of English-to-French translation. However, the use of large vocabulary becomes the bottleneck in both training and improving the performance. In this paper, we propose an efficient architecture to train a deep character-level neural machine translation by introducing a decimator and an interpolator. The decimator is used to sample the source sequence before encoding while the interpolator is used to resample after decoding. Such a deep model has two major advantages. It avoids the large vocabulary issue radically; at the same time, it is much faster and more memory-efficient in training than conventional character-based models. More interestingly, our model is able to translate the misspelled word like human beings.

A conference just ended, so it's that time of year! Here are some papers I liked with the usual caveats about recall. Before I go to the list, let me say that I really really enjoyed ACL this year. I was completely on the fence about going, and basically decided to go only because of giving a talk at Repl4NLP, and wanted to attend the business meeting for the discussion of diversity in the ACL community, led by Joakim Nivre with an amazing report that he, Lyn Walker, Yejin Choi and Min-Yen Kan put together. All in all, I'm supremely glad I decided to go: it was probably my favorite conference in recent memory.

As well, e2f's CEO, Michel Lopez, will be speaking along with Lilt's CEO, Spence Green, and GetYourGuide's Anne-Cécile Tomlinson, about our case study for the use of autoadaptive translation technology for large-scale localization projects. Machine Translation (MT) systems are traditionally criticized for poor quality output. Yet combining Machine Translation with auto-adaptive Machine Learning (ML) enables a new paradigm of "machine assistance." Of course, if you really want to learn how these new methods in machine learning, machine translation, and machine assistance are changing the world of translation, feel free to drop us a line!

If you have a few free minutes, try, for fun, filling them with Google Translate. And you need not be multilingual to enjoy it. Start with something straightforward: Enter an English phrase or sentence (idioms bring particular pleasure). Click a language, say, Spanish, and then "translate." Copy and paste the translated results over your original English phrase, reverse both languages (so that, in this example, Spanish is now where you begin and English is where you end), and again click "translate."

In a globally connected marketplace, new technologies ensure customer transactions won't get lost in translation. The world is becoming increasingly connected, and companies in search of worldwide markets need to be able to communicate with customers in their native tongues. They're depending on sophisticated new machine translation technologies to break down language barriers. "When you first enter a market, the early adopters for any new product -- whether it's a personal care product or a tech product -- tend to be internationally focused and English friendly, so you might think you're doing well," said Ben Sargent, content globalization strategist at the consulting firm Common Sense Advisory. To reach 80 percent of the world's total online population, businesses need to communicate in at least 12 languages, and to reach 98 percent, they need to translate across 48 languages.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages. For eons they have carried out a huge variety of tasks, from manufacturing goods, to transporting people around, to helping us decipher the natural world, to simply entertaining us. Machines can fight, protect, heal, and even teach us. But what they have not been able to do until quite recently is to learn, make decisions, and act on their own. Today, intelligent machines are everywhere. From the Netflix recommendation en- gine to Google Translate to Appleâ s Siri voice-recognition system, artificial intelligence has become sufficiently accurate, reliable, and useful to find its way into numerous devices and applications. These technologies have taken off in parallel with a dramatic expan- sion of the amount and complexity of data, which provides fertile teaching ground from which machines can learn to make intelligent decisions on their own.