"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).
Artificial intelligence (AI) has infiltrated numerous aspects of our lives in recent years, thanks to improvements in the field of machine learning, where computers ostensibly program themselves. This drive towards digital self-learning has led to major breakthroughs in our day-to-day interactions with machines, most notably the rise of digital home assistants such as Amazon Echo, and the recently launched Google Lens, which identifies objects based on visual cues from your phone's camera. One of the most widely-discussed advances has been the use of AI in translation. Not unlike the Babel Fish from The Hitchhiker's Guide to the Galaxy, with AI translation, "you can instantly understand anything said to you in any form of language." The technology works by recognizing words individually and then, as MIT Technology Review puts it, "takes advantage of the fact that relationships between certain words…are similar across languages" to create its translations.
Google is helping the Wikimedia Foundation achieve its goal of making Wikipedia articles available in a lot more languages. The Foundation has added Google Translate to its content translation tool, which human editors can use to add content to non-English Wikipedia websites. Those editors can take advantage of the new option -- "one of the most advanced machine translation systems available today," the foundation called it -- to generate an initial translation that they can then review and edit for readability in their language. The Foundation says volunteer Wikipedia editors have been asking for Google Translate integration for a long time now. According to VentureBeat, this move is an expansion of an earlier partnership, wherein Google promised to help Wikipedia make its English posts more accessible in Indonesia.
The Lifelong Kindergarten group at the MIT Media Lab has launched Scratch 3.0, a new version of the creative coding platform for kids. The latest updates include: • extensions for LEGO robotics, Makey Makey, micro:bit, Google Translate, and Amazon Text-to-Speech; • an ideas section with new video tutorials and inspiration for activities; • full coding curricula from Raspberry Pi Code Club, Google CS First, and the ScratchEd Creative Computing Curriculum Guide; • new characters, sounds, and backgrounds, and improved paint and sound editing tools; and • compatibility on all current browsers and a wide variety of touch devices like tablets, as well as an offline version. Over the past decade, 35 million kids in over 150 countries around the world have used Scratch to create their own animations, games, and other interactive projects while learning the basics of coding. Scratch is used in schools, libraries, and homes across the globe, giving parents and educators the tools to build coding literacy while helping kids gain confidence with new technologies in a fun, creative environment. "As kids create and share projects with Scratch, they learn to think creatively, reason systematically, and work collaboratively -- essential skills for everyone in today's society," says Mitchel Resnick, the LEGO Papert Professor of Learning Research at the MIT Media Lab and director of the Lifelong Kindergarten group, where Scratch was created.
The education ministry plans to set up a new subsidy system for prefectures and large cities that offer detailed support to foreign students attending public elementary and junior high schools and their parents through the use of multilingual translation systems. The subsidies will be offered to prefectural governments, ordinance-designated major cities and other core cities that use tablet computers with multilingual speech translation functions when teaching Japanese to students from abroad at school and providing school guidance to their parents. The ministry has set aside ¥20 million for the subsidy system, which is designed to cover one-third of related costs, under the government's fiscal 2019 budget. According to sources, 100 language support programs are likely to become eligible for the financial aid. The launch of the new subsidy system comes in line with the government's policy of allowing more foreign workers to enter the country.
The education ministry plans to set up a new subsidy system for prefectures and large cities that offer detailed support to foreign students attending public elementary and junior high schools and their parents by using multilingual translation systems. The subsidies will be offered to prefectural governments, ordinance-designated major cities and other core cities that use tablet computers with multilingual speech translation functions in teaching Japanese to students from abroad at school and providing school guidance to their parents. The ministry has set aside ¥20 million for the subsidy system, which is designed to cover one-third of related costs, under the government's fiscal 2019 budget, with 100 language support programs likely to become eligible for the financial aid, informed sources said. The launch of the new subsidy system comes in line with the government's policy of allowing more foreign workers to come here. The number of foreign students in Japan needing Japanese language education totaled 43,947 in fiscal 2016, up 70 percent from 26,281 in fiscal 2006.
The future needs to be more human (and less machine). For decades now, we have created computer programming languages and forced entire generations across the globe into becoming engineers and learning how to code. We have succeeded beyond our wildest dreams (we've created machines that can now learn on their own), and we have failed beyond our worst nightmares (we've created "black box" artificial intelligence (AI) which we don't -- and can't -- understand). It's time for us to rethink the future we're so effectively creating.
In the past few years, artificial intelligence has advanced so quickly that it now seems hardly a month goes by without a newsworthy AI breakthrough. In areas as wide-ranging as speech translation, medical diagnosis, and gameplay, we have seen computers outperform humans in startling ways. This has sparked a discussion about how AI will impact employment. Some fear that as AI improves, it will supplant workers, creating an ever-growing pool of unemployable humans who cannot compete economically with machines. This concern, while understandable, is unfounded.
This is a machine translation reading list maintained by the Tsinghua Natural Language Processing Group. The past three decades have witnessed the rapid development of machine translation, especially for data-driven approaches such as statistical machine translation (SMT) and neural machine translation (NMT). Due to the dominance of NMT at the present time, priority is given to collecting important, up-to-date NMT papers. The list is still incomplete and the categorization might be inappropriate. We will keep adding papers and improving the list.
The first MLPerf benchmark results are in, offering a new, objective measurement of the tools used to run AI workloads. The results show that Nvidia, up against solutions from Google and Intel, achieved the best performance in the six categories for which it submitted. MLPerf is a broad benchmark suite for measuring performance of machine learning (ML) software frameworks (such as TensorFlow, PyTorch and MXNet), ML hardware platforms (including Google TPUs, Intel CPUs and Nvidia GPUs) and ML cloud platforms. Several companies, as well as researchers from institutions like Harvard, Stanford and the University of California Berkeley, first agreed to support the benchmarks in May. The goal is to give developers and enterprise IT teams information to help them evaluate existing offerings and focus future development.
Google is working to reduce gender bias in its Google Translate tool after it was accused of sexism for automatically translating sentences to include masculine pronouns. Translations from English into French, Italian, Portuguese or Spanish will also now provide a feminine alternative as well as a masculine one for gendered words such as "strong" or "beautiful." In the past, Google's algorithm had to choose between masculine or feminine when translating a word - automatically defaulting to masculine in many instances. Additionally, the tool will offer gender-specific translations for phrases and sentences from Turkish to English. The update comes after two Stanford University professors pointed out that the artificial intelligence used by Google Translate was converting news articles written in Spanish to English by changing phrases referring to women into "he said" or "he wrote."