This article was written in collaboration with Gowling WLG. Gowling WLG is one of world's largest law firms and advises clients from offices in many of the world's most dynamic markets. It was recently ranked as the second most innovative firm in Europe in the prestigious FT Innovative Lawyer Awards 2016. The future is out there people. And its name might just be Alexa.

Language translation has typically been done by recurrent neural networks (RNN), which process language one word at a time in a linear order, either right-to-left or left-to-right, depending on the language. This CNN-based architecture pays attention to words farther along in a sentence to help understand the meaning from context farther along the string of words, much like humans do. Facebook hopes to use the new methodology to scale its translation efforts to cover "more of the world's 6,500 languages." Now that the popular social network has chosen CNN translation processing architecture, it will be interesting to see what comes next.

The tech world is looking ahead to next week's Google I/O conference, but there was plenty going in the technology realm this week. Here's some of the stuff you might have missed. Amazon proudly announced that its Alexa-enabled family of products grew by one member with the unveiling of the Echo Show, which has a screen for doing things like making video calls. Facebook's AI research group said they had created a faster, better language translation system using a neural network. Microsoft held its annual Build developer's' conference, where it talked about, among other things, forthcoming updates to Windows 10 and how it's going to use AI to make workplaces safer (watch that jackhammer there!).

Since launching in 2006, Google Translate has grown to over 500 million users worldwide, translating more than 100 billion words daily. In 2016, the tool supported 103 languages, with 92% of its users residing outside of the United States. While the tech giant sits comfortably atop the growing list of translator apps, there's one longstanding giant in the shadows, actively innovating and developing the blueprint for how companies like Google define the future of global communications. Founded in 1968, Systran stands as the leading provider of language translation software products, delivering real-time language solutions compatible for desktop, mobile, and web-based platforms. Credited as a pioneer in machine translation for over four decades, Systran remains committed to advancing multilingual communications around the world, removing language barriers between people and businesses to make forging meaningful connections seamless.

Two Japanese firms are developing an instant audio translation app for use at municipal offices in a bid to overcome the language barrier between foreign residents and local officials amid an increase in workers from abroad. Toppan Printing Co. and Feat Ltd., a developer of natural language processing technologies, hope the app will help people understand each other better during administrative procedures. The app, which will be used on tablets, supports English, Chinese and Portuguese. A prototype is expected to be completed in fiscal 2019, which starts in April 2019. The team developing it is focusing on procedures foreign residents face shortly after arriving in Japan, such as registering residency status and joining the national health insurance program. The app has already been tested at municipal offices in Tokyo's Itabashi Ward and Maebashi, Gunma Prefecture.

For decades, artificial intelligence (AI) researchers have sought to enable computers to perform a wide range of tasks once thought to be reserved for humans. In recent years, the technology has moved from science fiction into real life: AI programs can play games, recognize faces and speech, learn, and make informed decisions. As striking as AI programs may be (and as potentially unsettling to filmgoers suffering periodic nightmares about robots becoming self-aware and malevolent), the cognitive technologies behind artificial intelligence are already having a real impact on many people's lives and work. AI-based technologies include machine learning, computer vision, speech recognition, natural language processing, and robotics;1 they are powerful, scalable, and improving at an exponential rate. Developers are working on implementing AI solutions in everything from self-driving cars to swarms of autonomous drones, from "intelligent" robots to stunningly accurate speech translation.2 And the public sector is seeking--and finding--applications to improve services; indeed, cognitive technologies could eventually revolutionize every facet of government operations. For instance, the Department of Homeland Security's Citizenship and Immigration and Services has created a virtual assistant, EMMA, that can respond accurately to human language. EMMA uses its intelligence simply, showing relevant answers to questions--almost a half-million questions per month at present. Learning from her own experiences, the virtual assistant gets smarter as she answers more questions. Customer feedback tells EMMA which answers helped, honing her grasp of the data in a process called "supervised learning."3 While EMMA is a relatively simple application, developers are thinking bigger as well: Today's cognitive technologies can track the course, speed, and destination of nearly 2,000 airliners at a time, allowing them to fly safely.4

This is fairseq, a sequence-to-sequence learning toolkit for Torch from Facebook AI Research tailored to Neural Machine Translation (NMT). It implements the convolutional NMT models models proposed in Convolutional Sequence to Sequence Learning and A Convolutional Encoder Model for Neural Machine Translation as well as a standard LSTM-based model. It features multi-GPU training on a single machine as well as fast beam search generation on both CPU and GPU. We provide pre-trained models for English to French, English to German and English to Romanian translation. LuaRocks will fetch and build any additional dependencies that may be missing.

Language translation is important to Facebook's mission of making the world more open and connected, enabling everyone to consume posts or videos in their preferred language -- all at the highest possible accuracy and speed. Today, the Facebook Artificial Intelligence Research (FAIR) team published research results using a novel convolutional neural network (CNN) approach for language translation that achieves state-of-the-art accuracy at nine times the speed of recurrent neural systems.1 Additionally, the FAIR sequence modeling toolkit (fairseq) source code and the trained systems are available under an open source license on GitHub so that other researchers can build custom models for translation, text summarization, and other tasks. Originally developed by Yann LeCun decades ago, CNNs have been very successful in several machine learning fields, such as image processing. However, recurrent neural networks (RNNs) are the incumbent technology for text applications and have been the top choice for language translation because of their high accuracy. Though RNNs have historically outperformed CNNs at language translation tasks, their design has an inherent limitation, which can be understood by looking at how they process information.

Facebook's billion-plus users speak a plethora of languages, and right now, the social network supports translation of over 45 different tongues. That means that if you're an English speaker confronted with German, or a French speaker seeing Spanish, you'll see a link that says "See Translation." But Tuesday, Facebook announced that its machine learning experts have created a neural network that translates language up to nine times faster and more accurately than other current systems that use a standard method to translate text. The scientists who developed the new system work at the social network's FAIR group, which stands for Facebook A.I. Research. "Neural networks are modeled after the human brain," says Michael Auli, of FAIR, and a researcher behind the new system. One of the problems that a neural network can help solve is translating a sentence from one language to another, like French into English.