"Machine translation (MT) is the application of computers to the task of translating texts from one natural language to another. One of the very earliest pursuits in computer science, MT has proved to be an elusive goal, but today a number of systems are available which produce output which, if not perfect, is of sufficient quality to be useful in a number of specific domains."
– Definition from the European Association for Machine Translation (EAMT).
Alibaba chalked up 213.5 billion yuan -- $30.8 billion -- in gross merchandise volume (GMV) on Sunday through its 24-hour Global Shopping Festival. In order for everything to stay up, Tmall.com and its ecosystem relied entirely on Alibaba's technology portfolio, including the use of artificial intelligence (AI) to improve the shopping experience for buyers and sellers, as well as pushing Alibaba's cloud infrastructure to the limit to process a high volume of transactions. Alibaba used an intelligent operating platform, DC Brain, to optimise the performance of the 200-plus global internet datacentres (IDCs) hosting its online stores in areas including energy consumption, temperature, energy efficiency, and reliability. Through machine learning, DC Brain can predict the electric consumption and Power Usage Effectiveness of each IDC in real time, allocating to each to reduce energy consumption. For this year's festival, Alibaba also made available its "hyper-scale green datacentre" located in Zhangbei, northern China.
In the past few years, artificial intelligence has advanced so quickly that it now seems that hardly a month goes by without a newsworthy AI breakthrough. In areas as wide-ranging as speech translation, medical diagnosis and game play, we have seen computers outperform humans in startling ways. This has sparked a discussion about what impact AI will have on employment. Some fear that as AI improves, it will supplant workers in the job force, creating an ever-growing pool of unemployable humans who cannot economically compete with machines in any meaningful way. This concern, while understandable, is unfounded.
In the last decade, translation services have grown exponentially to include hardware devices such as Travis Translator, earphones such as Waverly Labs' pilot, Microsoft Translator, -- which not only translates text, but also speech, images, and street signs -- Google translate, and Facebook translation. Translations are occurring faster and with greater accuracy thanks to machine translation. But what does this mean for the traditional translator? As an expatriate in Germany, I am a user of both translation services and translation software, so I was interested to find out more. I spoke with the CEO and founder of Gengo, Matt Romaine.
MIT researchers have developed a novel "unsupervised" language translation model -- meaning it runs without the need for human annotations and guidance -- that could lead to faster, more efficient computer-based translations of far more languages. Translation systems from Google, Facebook, and Amazon require training models to look for patterns in millions of documents -- such as legal and political documents, or news articles -- that have been translated into various languages by humans. Given new words in one language, they can then find the matching words and phrases in the other language. But this translational data is time consuming and difficult to gather, and simply may not exist for many of the 7,000 languages spoken worldwide. Recently, researchers have been developing "monolingual" models that make translations between texts in two languages, but without direct translational information between the two.
Scientists are now using the Bible to help algorithms perfect their language skills. An AI has been trained on various versions of the sacred text so it can convert written works into different styles for different audiences. Each version of the Bible contains more than 31,000 verses that the researchers used to produce over 1.5 million unique pairings of source and target verses. The Bible is helping algorithms perfect their translation skills. Internet tools that translate text between languages like English and Spanish are widely available.
Would-be travelers of the galaxy, rejoice: The Chinese tech giant Baidu has invented a translation system that brings us one step closer to a software Babel fish. For those unfamiliar with the Douglas Adams masterworks of science fiction, let me explain. The Babel fish is a slithery fictional creature that takes up residence in the ear canal of humans, tapping into their neural systems to provide instant translation of any language they hear. In the real world, until now, we've had to make do with human and software interpreters that do their best to keep up. But the new AI-powered tool from Baidu Research, called STACL, could speed things up considerably.
Humans may have forfeited our lead in recognizing tumors or judging credit risk, but we still have, and may always have, the final authority over what is or isn't "natural" in a natural language. This authority is reflected in the metric of choice for evaluating machine translation algorithms – the BLEU (bilingual evaluation understudy) – which scores candidate translations based on their similarities to a human professional's work. "The closer a machine translation is to a professional human translation, the better it is", concede the framework's inventors.
TLDR: No! Your Machine Translation Model is not "prophesying", but let's look at the six major issues with neural machine translation (NMT). So I saw a Twitter thread today with the editor-in-chief of Motherboard tweeting, "Google Translate is popping out bizarre religious texts and no one is sure why". I am going to spend a little time on the "why" part (folks who work in MT know why), but mostly focus on actual problems with neural machine translation. The choice of headlines, the promotion tweet, and the tone of the article reminds me of all the irresponsible writing that went around the famous "Facebook Frankenstein" experiment. I would not be surprised if other media outlets picked up this Motherboard piece and ran ridiculous stories about machine translation conspiracy theories.
Humans may have forfeited our lead in recognizing tumours or judging credit risk, but we still have, and may always have, the final authority over what is or isn't "natural" in a natural language. This authority is reflected in the metric of choice for evaluating machine translation algorithms - the BLEU (bilingual evaluation understudy) - which scores candidate translations based on their similarities to a human professional's work. "The closer a machine translation is to a professional human translation, the better it is", concede the framework's inventors.
Google announced this week that its Translate app for iOS and Android recognize 13 new languages through your smartphone's camera. The update, which includes support for Arabic and Hindi, is in the process of being rolled out to Translate users worldwide, per VentureBeat. In addition to Arabic and Hindi, the app now supports Bengali and Punjabi--four of the top 10 most spoken languages in the world, according to Ethnologue. Translate also added support for Gujarati, Kannada, Malayalam, Marathi, Nepali, Tamil, Telugu, Thai, and Vietnamese. Google Translate's "See" and "Snap" features allow you to point your camera at a sign or menu and watch the app translate the text in real time, or take a quick picture and let the app process any translatable text for you.