Whether or not an artificial intelligence (AI) ought to be granted patent rights is a timely dilemma given the increasing proliferation of AI in the workplace. AI technology has been applied effectively in medical advancements, psycholinguistics, and tourism and food preparation. Even a film written by an AI recently debuted online, and AI has sneaked into the legal profession. One might argue that Intellectual Property (IP) laws and IP Rights were designed to excusively benefit human creators and inventors, and thus would exclude non-humans from holding IP rights. However, many IP laws were drafted well before the emergence of AI, and in any case do not explicitly require that a creator or inventor be'human.'

Do conservatives and liberals speak different languages? Given the heated nature of political debate these days, it certainly seems like it. But how do you find common ground when people from different sides of the aisle are trying to communicate? This question was one of the motivations behind Partisan Thesaurus, a project that enables you to type any word and see the other words with which liberals and conservatives mostly commonly associate it. Type a politically charged word like "immigrant," and see that liberals associate it with "undocumented," "innocent," and "unarmed," while conservatives associate it with "alien," "outcast," and "obscure."

Late last year, Google announced a breakthrough in translating. Dubbed Neural Machine Translation, it let Google's AI-powered engine tackle full sentences instead of just words, giving translations a more natural feel. Now Google is expanding it to a several more languages. Back when it launched, Google's new translator was available for English and just eight other languages, including French, German, Spanish, Portuguese, Chinese, Japanese, Korean, and Turkish. Now it is bringing it to Hindi, Russian, and Vietnamese, with more rolling out in the coming weeks.

Teaching machines to truly understand natural language has been one of the biggest challenges facing computer scientists working to advance artificial intelligence. But Google has made real progress in getting computers to look at language as more than just a bag of words, and these advancements are now making their way into its products. Google Translate, for example, is getting a technical makeover with the introduction of Neural Machine Translation (NMT). This follows the first go at utilizing NMT in Translate last November, when English, French, German, Spanish, Portuguese, Chinese, Japanese, Korean and Turkish all saw the same improvement. "We have 103 languages overall, and our goal is to get all of them working with neural nets," a Google spokesperson told the Observer.

GNMT produces higher quality translations than its phrase-based counterparts because it considers the entire sentence instead of breaking it up. It takes each character and compares it to those that come before and after. This is important to deciphering languages like Mandarin Chinese, for example, where words can mean different things based on which characters they're paired with. Google began using neural machine translation with Mandarin Chinese last year, then expanded support to include English, French, German, Japanese, Korean, Portuguese and Turkish. Around one-third of the world's population speaks these languages, TechCrunch notes, and they make up more than 35 percent of all Google Translate queries. Eventually, Google plans to use neural machine translation for all 103 languages in the app.

Last fall, Google introduced a new system for machine-assisted language translations, Google Neural Machine Translation system (GNMT), which takes advantage of deep neural networks to translate entire sentences – not just phrases – for greatly improved translations. The company put the system to work in Google Translate for eight language pairs in November, and is today expanding support to three more: Russian, Hindi and Vietnamese. Neural Machine Translation went into action late last year with support for translating to and from English and French, German, Spanish, Portuguese, Chinese, Japanese, Korean and Turkish. These represent the native languages of around one-third of the world's population, covering more than 35% of all Google Translate queries, the company said at the time. Today's news is also fairly significant in terms of scale, as in the U.S. alone, 1,292,448 people speak Vietnamese; another 836,171 speak Russian; and 586,173 speak Hindi, Google says, citing U.S. census data. And more languages will be added in weeks ahead, including Thai, which didn't quite make today's release.

Last fall, Google introduced a new system for machine-assisted language translations, Google Neural Machine Translation system (GNMT), which takes advantage of deep neural networks to translate entire sentences – not just phrases – for greatly improved translations. The company put the system to work in Google Translate for eight language pairs in November, and is today expanding support to three more: Russian, Hindi and Vietnamese. Neural Machine Translation went into action late last year with support for translating to and from English and French, German, Spanish, Portuguese, Chinese, Japanese, Korean and Turkish. These represent the native languages of around one-third of the world's population, covering more than 35% of all Google Translate queries, the company said at the time. Today's news is also fairly significant in terms of scale, as in the U.S. alone, 1,292,448 people speak Vietnamese; another 836,171 speak Russian; and 586,173 speak Hindi, Google says, citing U.S. census data. And more languages will be added in weeks ahead, including Thai, which didn't quite make today's release.

This tutorial introduces a new and powerful set of techniques variously called "neural machine translation" or "neural sequence-to-sequence models". These techniques have been used in a number of tasks regarding the handling of human language, and can be a powerful tool in the toolbox of anyone who wants to model sequential data of some sort. The tutorial assumes that the reader knows the basics of math and programming, but does not assume any particular experience with neural networks or natural language processing. It attempts to explain the intuition behind the various methods covered, then delves into them with enough mathematical detail to understand them concretely, and culiminates with a suggestion for an implementation exercise, where readers can test that they understood the content in practice.

Naver and LINE Corp. unveiled their artificial intelligence (AI)-based assistant platform Clova at the Mobile World Congress (MWC) 2017 in Barcelona, the companies reported Thursday. According to Naver and its mobile service subsidiary, Clova, which stands for "cloud virtual assistant," aims at an AI platform that works based on the five human senses. "Clova is a platform that incorporates various AI technologies including vocal, visual and conversational engines," LINE Corp. CEO Idezawa Takeshi said in his keynote at the MWC 2017, Wednesday. The two companies are jointly developing Clova by improving Naver Labs' voice-recognizing assistant service AMICA. They said it will expand the system to have more cognitive capabilities to make it more humanlike.

If trying to order dinner or find your hotel abroad fills you with fear due to your abysmal grasp of foreign languages, don't panic. A new in-ear gadget claims to be able to translate speech like the Babel Fish in The Hitchhiker's Guide to the Galaxy, or the Universal Translator gadget in Star Trek. The system, dubbed the Pilot, will cost $249 and go on sale later this year. It uses two earpieces, one worn by each person in the conversation. The Pilot system comprises two earpieces (shown in three colours above) to be worn by two people who don't speak the same language The Pilot works as a normal set of wireless earphones.