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.

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.

Let's say I am given an Excel sheet with data about various fruits and I have to tell which look like Apples. What I will do is ask a question "Which fruits are red and round?" and divide all fruits which answer yes and no to the question. Now, All Red and Round fruits might not be apples and all apples won't be red and round. So I will ask a question "Which fruits have red or yellow color hints on them? " on red and round fruits and will ask "Which fruits are green and round?" on not red and round fruits. Based on these questions I can tell with considerable accuracy which are apples. This cascade of questions is what a decision tree is. However, this is a decision tree based on my intuition.

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.

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.