"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).
A team of Microsoft researchers from China and the US have developed an artificial intelligence (AI) powered translation system that can translate Chinese language news articles into English with human accuracy. Check out the latest findings on how the hype around artificial intelligence could be sowing damaging confusion. Also, read a number of case studies on how enterprises are using AI to help reach business goals around the world. You forgot to provide an Email Address. This email address doesn't appear to be valid.
A team of Microsoft researchers announced on Wednesday they've created the first machine translation system that's capable of translating news articles from Chinese to English with the same accuracy as a person. The company says it's tested the system repeatedly on a sample of around 2,000 sentences from various online newspapers, comparing the result to a person's translation in the process – and even hiring outside bilingual language consultants to further verify the machine's accuracy. The sample set, called newstest2017, was released just last fall at the research conference WMT17. It's surprising, then, how quickly the researchers were able to achieve this milestone – especially given that machine translation is a problem people have been trying to solve for decades. Many have even believed that the goal of human parity would never be realized, Microsoft notes.
Researchers at the company's labs in the U.S. and Asia said they have achieved human parity when translating the newstest2017 collection of news articles from Chinese to English. The articles are commonly used when testing and benchmarking translation results. Microsoft hired third-party bilingual human evaluators to assess the suitability of its methodology. The evaluators compared the results of Microsoft's AI with translations produced by two human linguists. The human translators worked independently of each other to create their renditions of the news stories.
Machine learning has transformed major aspects of the modern world with great success. Self-driving cars, intelligent virtual assistants on smartphones, and cybersecurity automation are all examples of how far the technology has come. But of all the applications of machine learning, few have the potential to so radically shape our economy as language translation. The content of language translation is the perfect model for machine learning to tackle. Language operates on a set of predictable rules, but with a degree of variation that makes it difficult for humans to interpret.
A fresh wave of artificial intelligence rolling through Microsoft's language translation technologies is bringing more accurate speech recognition to more of the world's languages and higher quality machine-powered translations to all 60 languages supported by Microsoft's translation technologies. The advances were announced at Microsoft Tech Summit Sydney in Australia on November 16. "We've got a complex machine, and we're innovating on all fronts," said Olivier Fontana, the director of product strategy for Microsoft Translator, a platform for text and speech translation services. As the wave spreads, he added, these machine translation tools are allowing more people to grow businesses, build relationships and experience different cultures. Microsoft's research labs around the world are also building on top of these technologies to help people learn how to speak new languages, including a language learning application for non-native speakers of Chinese that also was announced at this week's tech summit. The new Microsoft Translator advances build on last year's switch to deep neural network-powered machine translations, which offer more fluent, human-sounding translations than the predecessor technology known as statistical machine translation.
A team of Microsoft researchers say they've created the first machine-translation system that can translate sentences of Chinese news articles to English with the same accuracy as humans. The work -- described in a Microsoft blog post on March 14 -- involves using deep neural networks, a method of training AI systems, to help them deliver more realistic and accurate translations. It also employs a number of different AI-training methods, including dual learning, deliberation networks and joint training to try to mimic how humans learn. The group says they've achieved human parity on a test set of news stories called newstest2017, which was developed by industry and academic partners and made available at a research conference last fall. The test set included about 2,000 sentences from a sample of online newspapers that had been professionally translated.
Widely regarded as the father of marketing science, he developed algorithms to automatically analyse scanner data--sales information obtained by scanning product barcodes at the cash register--and provide managers with informed insights. This problem-driven approach--a departure from early computer programmes, which mostly used classical statistics to analyse data--is also what underpins the artificial intelligence (AI) and machine learning strategies in use today, said Professor Phil Parker, chaired professor of management science at INSEAD. "Today, if you don't start with a very concrete objective, you may find yourself in a situation where you invest a lot of money in big data, and two years later, you wonder how to monetise it," said Professor Parker. "The best way is to start with a problem and then reverse engineer the proper algorithms." Professor Parker was speaking on 4 December 2017 at the Artificial Intelligence and Machine Learning Festival, a three-day event organised by INSEAD, SGInnovate and Impact Hub.
I open Google Translate twice as often as Facebook, and the instant translation of the price tags is not a cyberpunk for me anymore. That's what we call reality. It's hard to imagine that this is the result of a centennial fight to build the algorithms of machine translation and that there has been no visible success during half of that period. The precise developments I'll discuss in this article set the basis of all modern language processing systems -- from search engines to voice-controlled microwaves. The story begins in 1933.