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?

On Tuesday, the White House released a chilling report on AI and the economy. It began by positing that "it is to be expected that machines will continue to reach and exceed human performance on more and more tasks," and it warned of massive job losses. Yet to counter this threat, the government makes a recommendation that may sound absurd: we have to increase investment in AI. The risk to productivity and the US's competitive advantage is too high to do anything but double down on it. This approach not only makes sense, but also is the only approach that makes sense.

For James Simmonds-Read, overcoming language barriers is essential. He works at The Children's Society in London with migrants and refugees, mostly young men who are victims of human trafficking. "They are all asylum seekers and a large number of them have issues around language," he said. "Very frequently, we need to use translators." That has its own challenges, because it means the young men must disclose sensitive information to third-party interpreters.

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?

It's difficult to describe in a concise list with less than 1,000 words what the definitive direction of artificial intelligence is going to be in a 12-month span. Clearly, I say all of this because I am attempting to write just such a list. If you think something belongs on this list or want to contribute your own ideas based on your own expertise and personal opinion, please feel free to contact us at info@geektime.com. In the meantime, here are the four trends that will dominate artificial intelligence in 2017. We could call this "natural language processing" or NLP, but let's think more broadly about language for a moment.

By Kim Han-joo SEOUL, Jan. 11 (Yonhap) -- Fierce competition is expected among technology companies from both home and abroad in the field of artificial intelligence (AI) language translations that industry officials say will soon reach human-level accuracy. Google Inc. is the leading provider of an AI-based translation platform by becoming the first to introduce its Neural Machine Translation (NMT) system last year that significantly improves translation quality and reduces errors. The new system is based on a deep learning framework that learns from millions of examples from over 100 different languages, the U.S. tech giant said. Unlike previous machine translation that was adopted 10 years ago, the new system considers an entire sentence as one unit. Previous systems independently translated words and phrases within a sentence.

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?

Late one Friday night in early November, Jun Rekimoto, a distinguished professor of human-computer interaction at the University of Tokyo, was online preparing for a lecture when he began to notice some peculiar posts rolling in on social media. Apparently Google Translate, the company's popular machine-translation service, had suddenly and almost immeasurably improved. Rekimoto visited Translate himself and began to experiment with it. He had to go to sleep, but Translate refused to relax its grip on his imagination. Rekimoto wrote up his initial findings in a blog post. First, he compared a few sentences from two published versions of "The Great Gatsby," Takashi Nozaki's 1957 translation and Haruki Murakami's more recent iteration, with what this new Google Translate was able to produce. Murakami's translation is written "in very polished Japanese," Rekimoto explained to me later via email, but the prose is distinctively "Murakami-style."