Over the last few years, data-intensive machine-learning techniques have made dramatic strides in speech recognition and image analysis. Now these methods are making significant advances on another long-standing challenge: translation of written text between languages. Until a couple of years ago, the steady progress in machine translation had always been dominated by Google, with its well-supported phrase-based statistical analysis, said Kyunghyun Cho, an assistant professor of computer science and data science at New York University (NYU). However, in 2015, Cho (then a post-doc in Yoshua Bengio's group at the University of Montreal) and others brought neural-network-based statistical approaches to the annual Workshop on Machine Translation (WMT 15), and for the first time, the "Google translation was not doing better than any of those academic systems." Since then, "Google has been really quick in adapting this (neural network) technology" for translation, Cho observed.

Google Translate's knowledge of more than 100 languages can help you in your daily workflow as much as it can help you on your next trip. The features below show how it can help you with entire documents or websites, or even your native tongue. Google Translate can parse individual words and phrases, of course, but you can also translate entire websites into a chosen language. You can translate foreign websites like this Italian news publication into another language with Google Translate. Just type the entire URL of the website you want translated in the text box on the left side of Google Translate's home page.

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 Over time, AI will spawn massive changes in the public sector, transforming how government employees get work done. It's likely to eliminate some jobs, lead to the redesign of countless others, and create entirely new professions.5 In the near term, our analysis suggests, large government job losses are unlikely. But cognitive technologies will change the nature of many jobs--both what gets done and how workers go about doing it--freeing up to one quarter of many workers' time to focus on other activities.

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.

Today we are into digital age, every business is using big data and machine learning to effectively target users with messaging in a language they really understand and push offers, deals and ads that appeal to them across a range of channels. With exponential growth in data from people and & internet of things, a key to survival is to use machine learning & make that data more meaningful, more relevant to enrich customer experience. Machine Learning can also wreak havoc on a business if improperly implemented. Before embracing this technology, enterprises should be aware of the ways machine learning can fall flat.Data scientists have to take extreme care while developing these machine learning models so that it generate right insights to be consumed by business. Here are 5 ways to improve the accuracy & predictive ability of machine learning model and ensure it produces better results.

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.