We propose a statistical model for natural language that begins by considering language as a monoid, then representing it in complex matrices with a compatible translation invariant probability measure. We interpret the probability measure as arising via the Born rule from a translation invariant matrix product state.

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.

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.

Sequence-to-sequence(seq2seq)[1] is a versatile structure and capable of many things (language translation, text summarization[2], video captioning[3], etc.). For a short introduction to seq2seq, here are some good posts: [4][5]. Sean Robertson's tutorial notebook[6] and Jeremy Howard's lectures [6][7] are great starting points to get a firm grasp on the technical details of seq2seq. However, I'd try to avoid implementing all these details myself when dealing with real-world problems. It's usually not a good idea to reinvent the wheel, especially when you're very new to this field.

Chinese messaging app WeChat has reportedly apologised after an AI error resulted in it translating a neutral Chinese phrase into the n-word. The WeChat error was reported by Shanghai-based theatre producer and actor Ann James, a black American. In a post on the service's Twitter-like Moments feature, she wrote that it had translated hei laowai – a neutral phrase which literally means "black foreigner" – as the n-word. "We're very sorry for the inappropriate translation," a WeChat spokesperson told Chinese news site Sixth Tone. "After receiving users' feedback, we immediately fixed the problem."

Very often it is only a matter of time before something in science-fiction becomes science-fact. This past week tech giant Google held an event in San Francisco where it unveiled products like the Google Home Mini, a new Chromebook, and its new version of the Google Pixel phone. One of the most intriguing announcements at the event was Google's new Pixel Buds which are reportedly capable of translating up to 40 different languages by using the Google Translate technology. Regarding the Pixel Buds, media sources have made a number of allusions to Douglas Adam's Hitchhiker's Guide to the Galaxy and its Babelfish that allowed anyone to understand any language simply by putting a fish into their ear. Artificial intelligence has enabled this concept to come out of the realm of fiction and into reality.

IF YOU have a Google Pixel phone, you will soon be able to speak 40 languages. All you need is a pair of the earbuds Google announced last week in San Francisco. These can be used to make phone calls and listen to music – but they also provide on-demand two-way translation. To talk in one of the supported languages, you use the earbuds to access Google Assistant and the Google Translate app. Pressing on the earbud and saying "let me speak German", for example, initiates translation of your speech into German, playing the results on the phone's speakers.

Deepgram, a startup applying machine learning to audio data, is releasing its machine transcription platform this morning for free. No more will you have to pay for other services like Trint to get the dirty work of automated transcription done. Hint: it has something to do with data. In fact, machine anything isn't solved. And it seems like everyone these days is making haste to build their own Fort Knox of data to solve machine everything.