WORLDWIDE revenue from AI will surge past US$46 billion in 2020, according to research firm IDC. In Asia-Pacific, this is projected to rise to US$6.8 billion by 2021. Though researchers have been working on AI decades, development has accelerated in the past few years thanks to three factors – the ubiquitous availability of data, the growing capabilities of cloud computing, and more powerful algorithms developed by AI researchers. Most recently, a team of Microsoft researchers have developed the first machine translation system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as a person. Throughout history, the emergence of new technologies has been accompanied by dire warnings about human redundancy.

Microsoft revealed on Wednesday it has reached a monumental milestone in artificial intelligence-powered translation software, declaring the creation of a system that can translate news article sentences from Chinese to English just as good as a human. "Hitting human parity in a machine translation task is a dream that all of us have had," Xuedong Huang, technical fellow in charge of the company's translation efforts, said in a statement. "We just didn't realize we'd be able to hit it so soon." The breakthrough is the latest in a race to develop human-like translations. Google has improved its translation tools over time, parsing whole sentences with a November 2016 update.

A team of Microsoft researchers said Wednesday that they believe they have created the first machine translation system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as a person. Researchers in the company's Asia and U.S. labs said that their system achieved human parity on a commonly used test set of news stories, called newstest2017, which was developed by a group of industry and academic partners and released at a research conference called WMT17 last fall. To ensure the results were both accurate and on par with what people would have done, the team hired external bilingual human evaluators, who compared Microsoft's results to two independently produced human reference translations. Xuedong Huang, a technical fellow in charge of Microsoft's speech, natural language and machine translation efforts, called it a major milestone in one of the most challenging natural language processing tasks. "Hitting human parity in a machine translation task is a dream that all of us have had," Huang said.

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.

The time has come when machine translation, first conceptualized in the 1960s, is finally useful. Google and hundreds of smaller companies have developed algorithms, mined data, used human translation examples and employed syntactic and contextual analysis and every tool in the book to come up with software that essentially breaks down the language barrier completely. You can now automatically translate every website, and in most languages the result is acceptable. Twitter offers a link under every foreign-language tweet to translate it into a foreign language to translate. Some social networks always appear in your preferred language, translating content on the fly. WeChat has a translation option for every chat that is surprisingly good.

Even with the advances in the Natural Language Processing field, there have always been nagging doubts about the quality and accuracy of translations from one language to another. Take Google's translation, for example. While it has steadily improved over the years, you still see a few things grammatically wrong with complex sentences. To bridge that gap, Microsoft claims it has developed a system that can translate from Chinese to English with the quality and accuracy of humans. The researchers behind this system developed it by training the model on a set of news stories called newstest2017.

A team of Microsoft researchers said March 14 that they believe they have created the first machine translation system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as a person. Researchers in the company's Asia and US labs said that their system achieved human parity on a commonly used test set of news stories, called newstest2017, which was developed by a group of industry and academic partners and released at a research conference called WMT17 last year. To ensure the results were both accurate and on par with what people would have done, the team hired external bilingual human evaluators, who compared Microsoft's results to two independently produced human reference translations. Xuedong Huang (pix, above), a technical fellow in charge of Microsoft's speech, natural language and machine translation efforts, called it a major milestone in one of the most challenging natural language processing tasks. "Hitting human parity in a machine translation task is a dream that all of us have had," Huang said.

Despite their ability to memorize large datasets, deep neural networks often achieve good generalization performance. However, the differences between the learned solutions of networks which generalize and those which do not remain unclear. Additionally, the tuning properties of single directions (defined as the activation of a single unit or some linear combination of units in response to some input) have been highlighted, but their importance has not been evaluated. Here, we connect these lines of inquiry to demonstrate that a network's reliance on single directions is a good predictor of its generalization performance, across networks trained on datasets with different fractions of corrupted labels, across ensembles of networks trained on datasets with unmodified labels, across different hyperparameters, and over the course of training. While dropout only regularizes this quantity up to a point, batch normalization implicitly discourages single direction reliance, in part by decreasing the class selectivity of individual units. Finally, we find that class selectivity is a poor predictor of task importance, suggesting not only that networks which generalize well minimize their dependence on individual units by reducing their selectivity, but also that individually selective units may not be necessary for strong network performance.

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.