"About 6,000 languages are currently spoken in the world today," says Elizabeth Salesky of MIT Lincoln Laboratory's Human Language Technology (HLT) Group. "Within the law enforcement community, there are not enough multilingual analysts who possess the necessary level of proficiency to understand and analyze content across these languages," she continues. This problem of too many languages and too few specialized analysts is one Salesky and her colleagues are now working to solve for law enforcement agencies, but their work has potential application for the Department of Defense and Intelligence Community. The research team is taking advantage of major advances in language recognition, speaker recognition, speech recognition, machine translation, and information retrieval to automate language processing tasks so that the limited number of linguists available for analyzing text and spoken foreign languages can be used more efficiently. "With HLT, an equivalent of 20 times more foreign language analysts are at your disposal," says Salesky.

Several companies have announced breakthroughs or substantial progress in MT research in recent months. Popular perception of MT has suffered from low-quality "gisting" translation that Web-based translation engines, such as Babelfish and other online services, generate. But MT engines designed for limited domains, and tailor-made systems that use controlled language, are already delivering services. Within the community and AI circles, it's cognitive or "smart" radios that are making a splash. Paving the way is Virginia Tech's Center for Wireless Telecommunications Cognitive Wireless Technology (CWT2) group.

The inclusion of Siri on the iPhone 4S has brought a lot of attention generally to the topic of voice recognition on mobile devices. But voice is an area that Google, among others, has spent a lot of time trying to perfect, using data intensive "machine learning" technology. Now, Google is updating a feature in its Translate app for Android devices that can handle speech-to-speech translation among 14 languages. So if you speak, say, Indonesian, you can talk back and forth with someone who only speaks Russian (or Polish to Chinese ... or Korean to Turkish...). In all, Google is adding a dozen languages to English and Spanish, which were the two languages initially featured in the app.

GeoFluent Customizer is a product billed by its makers, Lionbridge, as being able to improve base language models by using your enterprise's language assets for an enhanced customer chat translation experience. Basically that means that while, yes, you can find free chat translation services out there, Lionbridge is betting you wouldn't want to actually allow valuable customers to utilize them in fear of inaccuracies. It offers real-time on-demand custom translation in a cloud app -- automated machine translation technology, instantly translating content and communications into multiple languages.The GeoFluent platform accomplishes this, Lionbridge officials say, by integrating advanced cloud-based language tools with a real-time translation service, a statistical machine translation engine from IBM's (News - Alert) Watson Research Center. The chat translation platform can also be customized using your content and configured for specific business processes for multilingual communication such as eSupport, forums, blogs, web pages, chat sessions and other such uses. What sets GeoFluent apart is the fact that is based on a powerful technology platform that uses statistical machine translation algorithms from IBM. SMT, Lionbridge officials explain, has contributed to the growth in machine translation applications in recent years since it can analyze "vast amounts of previously translated material," and target-language texts to create what Lionbridge officials say is "a real-time translation in a fraction of the time that it takes to produce traditional rules- based translation systems."

If you've ever been struggling with a foreign language dictionary abroad, wishing that you could simply speak into a machine and have your chat translated for you, NTT (News - Alert) Docomo may be ready to make your wish come true. The company, which is Japan's number one cell phone carrier is about to begin offering a new real-time speech-to-speech translation service that you can use both in person and over the phone during a call, according to Geek.com According to Japanese news services, the solution is the first automated chat translation service in the world that is available on a standard cell phone. The new product combines several cutting-edge technologies: advanced speech recognition, machine translation and text-to-speech conversion of the translated results, says Geek.com. The services to power the solution will be cloud-based, says NTT Docomo.

Increasing globalization is driving a burgeoning demand on the language services market, which, according to statistics, is expected to reach $31.4 billion this year alone and is growing by a rapid rate of 7.4 percent annually. In response to this ever-growing need for language service providers and users of translation services to operate more efficiently, Science Applications International Corp. (SAIC (News - Alert)) has unveiled the industry's first-ever machine chat translation solution. Designed to enable tailored and adaptive contextual translation, this new integrated offering is meant for anyone that has to interact with another individual who speaks the same language or a different language, SAIC shared with TMC at the recent SpeechTEK (News - Alert) 2011 in New York City, where SAIC debuted the solution. "We've brought a single platform that does both text and speech and combined it with hybrid machine translation," Hassan Sawaf, chief scientist for SAIC, told TMC (News - Alert) during an interview at SpeechTEK. "It's highly customizable, tailorable and secure since it can reside on servers within a firewall." According to a press release, organizations using the machine translation technology will decrease the need for costly human translation, thus resulting in lower costs for businesses and boosted productivity for translation providers.

In Davos right now, the world's best and best-performing economic minds are gathered for their annual bout of elite networking. You know you're not invited because a ticket costs $35,000, and that's before the cost of membership, which is also required, and even more expensive. But we get news reports from the proceedings and the most interesting one today concerns the World Economic Forum's recent report which claims the biggest risk in 2017 is people losing their jobs to robots. The word out of Davos is we have nothing to fear. If you don't believe them, you might find some comfort in a story about Donald Trump that's been kicking around for a couple of years that is, well, intriguing. The next Leader of the Free World has never used a computer. It's great fun (Matt Novak has tenaciously taken up the baton at Gizmodo), and not at all as far-fetched as you might be thinking right now. We know Trump tweets, badly. But it is actually surprisingly difficult to find evidence of him looking comfortable behind a MacBook.

Research on intelligent software systems and machine learning made great strides in 2016, and some of the credit for that goes to the Google Brain team. Going forward, the team said it will continue to conduct machine learning research, especially in areas like healthcare, AI safety, and natural language understanding. The Google Brain team doesn't have many restrictions when it comes to its research projects and portfolios, which is why its papers cover everything from deep learning games to neural networks. Not only did these papers make it into top-tier machine learning conferences like NIPS, ICML, and ICLR in 2016, they demonstrated new approaches to improving people's lives with advanced software systems, according to Jeff Dean, Google senior fellow and member of the the Google Brain team. NLU: Getting computers to understand our language In 2016, the Google Brain team took previous research from a paper called "Sequence to Sequence Learning with Neural Networks," which demonstrated that the approach could be used for machine translation, and replaced the translation algorithms powering Google Translate with a new end-to-end learned system.

In my series of articles last March, I noted that cognitive computing and related/overlapping concepts such as artificial intelligence (AI) and machine learning had seen slow uptake in business intelligence (BI). As 2016 draws to a close, a second look is warranted, given the enormous hype and publicity the topic has drawn in the intervening months. Although there has been limited progress in AI for BI in the interim, several fascinating developments have emerged in AI research, particularly from the wide world of Google. Recent announcements suggest that the impressive advances seen in Google DeepMind AlphaGo's comprehensive defeat of Go world champion, Lee Se-dol, are only the first step on the journey. Until that moment, AI experts expected that it would still take some years before AI could outwit a world champion.

In the closing weeks of 2016, Google published an article that quietly sailed under most people's radars. Which is a shame, because it may just be the most astonishing article about machine learning that I read last year. Don't feel bad if you missed it. Not only was the article competing with the pre-Christmas rush that most of us were navigating -- it was also tucked away on Google's Research Blog, beneath the geektastic headline Zero-Shot Translation with Google's Multilingual Neural Machine Translation System. This doesn't exactly scream must read, does it?