Recently, the attention mechanism plays a key role to achieve high performance for Neural Machine Translation models. However, as it computes a score function for the encoder states in all positions at each decoding step, the attention model greatly increases the computational complexity. In this paper, we investigate the adequate vision span of attention models in the context of machine translation, by proposing a novel attention framework that is capable of reducing redundant score computation dynamically. The term "vision span" means a window of the encoder states considered by the attention model in one step. In our experiments, we found that the average window size of vision span can be reduced by over 50% with modest loss in accuracy on English-Japanese and German-English translation tasks.% This results indicate that the conventional attention mechanism performs a significant amount of redundant computation.

The UK election this Thursday will be shaped by artificial intelligence. Artificial intelligence is being used to fake vocal political support on social media in the run up to the UK election. Then there's social media targeting. Huge swathes of marginalised people could be empowered by automated translation tools.

What if you could talk to anyone, regardless of the language they spoke? The personal universal translator has long been a dream of science fiction, but that dream is now a reality: Microsoft Translator translates in-person conversations in real time with up to 100 speakers using their own smartphone, tablet, or PC.

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Machine translation systems that convert sign language into text and back again are helping people who are deaf or have difficulty hearing to communicate with those who cannot sign. KinTrans, a start-up based in Dallas, Texas, is trialling its technology in a bank and government offices in the United Arab Emirates, and plans to install it in more places over the next couple of months. SignAll, a company based in Budapest, Hungary, will begin its own trials next year. KinTrans uses a 3D camera to track the movement of a person's hands as they sign words. A sign language user can approach a bank teller and sign to the KinTrans camera that they'd like assistance, for example.

Earlier this year, Christopher Manning, a Stanford professor of computer science and of linguistics, was named the Thomas M. Siebel Professor in Machine Learning, thanks to a gift from the Thomas and Stacey Siebel Foundation. Manning specializes in natural language processing – designing computer algorithms that can understand meaning and sentiment in written and spoken language and respond intelligently. His work is closely tied to the sort of voice-activated systems found in smartphones and in online applications that translate text between human languages. He relies on an offshoot of artificial intelligence known as deep learning to design algorithms that can teach themselves to understand meaning and adapt to new or evolving uses of language. Siebel, a pioneer in numerous areas of information technology and known for his ability to see and understand emerging trends in computer science and beyond, has long held an interest in precisely this kind of work.

The New York Times published a provocative article on Friday, asking in the headline "Is China outsmarting America in A.I.?". But depending on how you define "outsmarting," and the context in which the question is asked, the answer might not even matter much. The answer might matter very much in terms of geopolitics and national security. Just like with supercomputing, quantum computing and other areas of deep computer science research, having better capabilities in artificial intelligence can arguably lead to an edge in areas like military, energy and climate science that can shift the world-power balance. But if we're talking about consumer or enterprise AI, then comparing China and the United States is kind of like comparing apples and oranges.

Among the list of technologies that have radically changed our economy in the last year is a handful that did not receive the same level of attention as artificial intelligence or self-driving cars. One, in particular, is called Neural Machine Translation (NMT), a major breakthrough in language technology that some believe is a turning point in how business gets done. The Internet and the connectivity it facilitates is primarily responsible for what we now call the global economy. Emails, web pages, and mobile applications have created a marketplace for ideas and products, as well as empowered organizations to collaborate instantly from thousands of miles away. But for as small as the world is today, it can get smaller, and language is a major part of that.

Data is the fuel for AI, and Google owns some of the largest data sets in the world. The company operates seven services with over a billion monthly active users: Android, Chrome, YouTube, Gmail, Google Maps, Google Search, and Google Play. In addition, Google Translate and Google Photos are used by over 500 million people each. By operating such a diverse range of services, Google collects data of various types: text, images, video, maps, and webpages--helping the company master not just one kind of AI, but AI across various use cases. Just as important as the data are the apps, which Google also owns.

As seen at Google DeepMind's conference this week, machine learning with AI has seeped into a number of industries in recent years. Whereas in the past it was more a topic of discussion on theoretical applications, we now see machine learning being applied in smart cars, video games, digital marketing, virtual personal assistants, chatbots, and other areas of daily life. As AI moves to disrupt and improve more sectors, there are still barriers to overcome before we need to fear for our jobs. In a recent translation competition, human beings beat AI, but it's only a matter of time before machines become digital babel fish. It's worth recapping how machine learning and AI have already surpassed human abilities. In 1996, IBM's Deep Blue computer first challenged world-leading chess player Garry Kasparov.