"Machine translation (MT) is the application of computers to the task of translating texts from one natural language to another. One of the very earliest pursuits in computer science, MT has proved to be an elusive goal, but today a number of systems are available which produce output which, if not perfect, is of sufficient quality to be useful in a number of specific domains."
– Definition from the European Association for Machine Translation (EAMT).
Among the vast business applications of artificial intelligence, Slator has been keeping a close eye on neural machine translation (MT). However, the boundaries between MT and broader tech like natural language processing (NLP) are sometimes fuzzy. The services resulting from these technologies are often adjacent: translation on one side and chatbots on another. In fact, some companies combine them into a single service--multilingual chatbots, for instance. This is why a recent slew of significant funding rounds in the NLP space has caught our attention.
The word2vec method based on skip-gram with negative sampling (Mikolov et al., 2013)  was published in 2013 and had a large impact on the field, mainly through its accompanying software package, which enabled efficient training of dense word representations and a straightforward integration into downstream models. In some respects, we have come far since then: Word embeddings have established themselves as an integral part of Natural Language Processing (NLP) models. In other aspects, we might as well be in 2013 as we have not found ways to pre-train word embeddings that have managed to supersede the original word2vec. This post will focus on the deficiencies of word embeddings and how recent approaches have tried to resolve them. If not otherwise stated, this post discusses pre-trained word embeddings, i.e. word representations that have been learned on a large corpus using word2vec and its variants.
Last fall, Google Translate rolled out a new-and-improved artificial intelligence translation engine that it claimed was, at times, "nearly indistinguishable" from human translation. Jost Zetzsche could only roll his eyes. The German native had been working as a professional translator for 20 years, and he'd heard time and time again that his industry would be threatened by advances in automation. Every time, he'd found, the hype was overblown--and Google Translate's makeover was no exception. It certainly wasn't the key to translation, he thought.
"Microsoft Monday" is a weekly column that focuses on all things Microsoft. This week, Microsoft Monday includes details about the Windows 10 upgrade program fully ending, the shutdown of Outlook.com When Microsoft originally released Windows 10, it was available as a free upgrade PCs running on Windows 7 and Windows 8.1. After a year, the free upgrade offer was extended for people that were seeking enhanced accessibility features. This workaround will no longer be available after December 31, 2017.
In recent years, MIT scholars have helped develop a whole lexicon of science and math terms for use in Haiti's Kreyòl language. Now a collaboration with Google is making those terms readily available to anyone -- an important step in the expansion of Haitian Kreyòl for education purposes. The new project, centered around the MIT-Haiti Initiative, has been launched as part of an enhancement to the Google Translate program. Now anyone using Google Translate can find an extensive set of Kreyòl terms, including recent coinages, in the science, technology, engineering, and math (STEM) disciplines. "In the past five or six years, we've witnessed quite a paradigm shift in the way people in Haiti talk about and use Kreyòl," says Michel DeGraff, a professor of linguistics at MIT and director of the MIT-Haiti Initiative.
Neural machine translation (NMT) is now mainstream. This was New York University Assistant Professor Kyunghyun Cho's first message during his presentation on NMT at the recent SlatorCon New York on October 12, 2017. When Cho's team started looking into NMT in 2013 and 2014, he said previous MT researchers and industry insiders were convinced it would not work. Efforts in the 1980s and mid-1990s failed, after all. Fast forward to 2017, Cho pointed out that big names like Google, Microsoft, and Facebook use NMT, and sites like Booking.com and even the European Patent Office have all caught the NMT bug.
In a world increasingly dominated by cloud computing and outsourcing, vendor management has become a core competency of running a great IT department. Here's how to maximize your partnerships. I've been getting my Google back on this week. I had the opportunity to attend the Google Cloud Platform OnBoard in Tampa this week. Also, I just got my Pixel 2, which -- despite the issues with its larger sibling -- I think is a great device.
Facebook has apologised after an error in its machine-translation service saw Israeli police arrest a Palestinian man for posting "good morning" on his social media profile. The man, a construction worker in the West Bank settlement of Beitar Illit, near Jerusalem, posted a picture of himself leaning against a bulldozer with the caption "يصبحهم", or "yusbihuhum", which translates as "good morning". But Facebook's artificial intelligence-powered translation service, which it built after parting ways with Microsoft's Bing translation in 2016, instead translated the word into "hurt them" in English or "attack them" in Hebrew. Police officers arrested the man later that day, according to Israeli newspaper Haaretz, after they were notified of the post. They questioned him for several hours, suspicious he was planning to use the pictured bulldozer in a vehicle attack, before realising their mistake.
Microsoft is delivering the world's first fully neural on device translations in the Microsoft Translator app for Android, customized for the Huawei Mate 10 series. Microsoft achieved this breakthrough by partnering with Huawei to customize Microsoft's new neural technology for Huawei's new NPU (Neural Processing Unit) hardware. This results in dramatically better and faster offline translations as compared to existing offline packs. The Microsoft Translator app with these capabilities comes pre-installed on Huawei Mate 10 devices allowing every Mate 10 user to have native access to online quality level translations even when they are not connected to the Internet. Until now, due to the computational requirements of neural machine translation, it was not possible to do full Neural Machine Translation (NMT) on-device.