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.

Christopher Manning, a Stanford University professor who specialized in machine translation and has reviewed the paper, calls it an "impressive achievement," particularly because it can train translation models more quickly than existing systems. This past fall, Google unveiled a new translation system driven entirely by neural networks that topped existing models, and many other companies and researchers are pushing in the same direction, most notably Microsoft and Chinese web giant Baidu. "We've seen more improvements over the past two years than we have seen in the past decade," says John Tinsley, the CEO of Iconic Translation Machines, a translation technology company based in Dublin. And others have explored such networks as a basic technique for machine translation, including researchers at DeepMind, a Google AI lab based in London.

Convolutional encoders for neural MT go as far back as (Kalchbrenner, Blunsom 2013) and convolutional encoders decoders in LM and MT appear first in (Kalchbrenner et al, 2016) and with pooling also in (Bradbury et al, 2016).

Ensure that you have variety of data that covers almost all the scenarios and not biased to any situation. There was a news in early pokemon go days that it was showing only white neighborhoods. It's because the creators of the algorithms failed to provide a diverse training set, and didn't spend time in these neighborhoods. Instead of working on a limited data, ask for more data. That will improve the accuracy of the model.

Aiming to bring a billion people online and make the web more useful for them, Google India is slated to unveil new products on advancement in machine learning for the Indian languages, the company said on Friday. In an event to be organised here on April 25, Google will also share findings from a new report by Google and KPMG India, titled "Indian Languages-Defining India's Internet". Rajan Anandan, Vice President, SouthEast Asia and India, Google, will address the event, the company said in a statement. Also read: Google'Smart Display Campaign' to Help Advertisers Increase Customer Reach In a bid to help Bengali speakers discover new information quickly, Google earlier this year announced the introduction of Knowledge Graph in the Bengali language on Google Search. The Knowledge Graph enables users to search for things, people or places that Google knows about -- landmarks, celebrities, cities, sports teams, buildings, geographical features, movies, celestial objects, works of art and more.

We all use user-generated reviews to figure out what points of interest are worth checking out. If you're traveling in a country where you don't speak the language, however, the reviews you rely on are usually in the local tongue. Google has a new feature to help you out. The company will now automatically translate reviews into your native language without any effort on your part. When you use Google Maps or Search to find a place you're interested in, the reviews will be translated on the fly into the language you have set on your phone.