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."

Google Translate is getting brainier. The online translation tool recently started using a neural network to translate between some of its most popular languages – and the system is now so clever it can do this for language pairs on which it has not been explicitly trained. To do this, it seems to have created its own artificial language. Traditional machine-translation systems break sentences into words and phrases, and translate each individually. In September, Google Translate unveiled a new system that uses a neural network to work on entire sentences at once, giving it more context to figure out the best translation.

Twitter can sometimes feel like a language of its own, but one lecturer is using the social media site as a tool to teach Arabic. In Mahammed Bouabdallah's classes at the University of Westminster, London, students are set simple tasks using Twitter to complement their lessons. Bouabdallah publishes a photo or link and asks students to comment on it in Arabic, or runs a Twitter poll about events happening in Arabic-speaking countries and discusses the results in class. Sometimes, they will use Twitter's built-in translation tool and judge its accuracy. "They have to tweet outside the class, and we discuss it inside the class," says Bouabdallah. His own research suggests that Twitter is popular as a language learning tool, with 80 per cent of surveyed students responding positively to its use.

The 1st Workshop on Neural Machine Translation is a new annual workshop that will be co-located with ACL 2017 (Vancouver, July 30-August 4, 2017). Neural Machine Translation (NMT) is a simple new architecture for getting machines to learn to translate. Despite being relatively recent, NMT has demonstrated promising results and attracted much interest, achieving state-of-the-art results on a number of shared tasks. This workshop aims to cultivate research in neural machine translation and other aspects of machine translation and multilinguality that utilize neural models.