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Microsoft AI translates Chinese to English like humans

The researchers taught the system to repeat the process of translating the same sentence over and over

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Microsoft
Microsoft's new Surface products include 1st-ever headphones. Wikimedia Commons
  • Microsoft creates a new kind of AI
  • This can translate Chinese to English just like humans
  • The translator makes little mistakes

A team of Microsoft researchers, including one of Indian-origin, has created an Artificial Intelligence (AI)-powered machine system that can translate sentences of news articles from Chinese to English with the same quality and accuracy as humans.

Researchers from the company’s Asia and US labs said their system achieved human parity on a commonly-used test set of news stories — called “newstest2017” — that was released at a conference recently, a blog post said late on Wednesday.

Microsoft acquired the start-up PlayFab. Pixabay
This Ai can expertly translate Chinese into English. Pixabay

According to Arul Menezes, an IIT-Bombay alumni and Partner Research Manager of Microsoft’s machine translation team, the team set out to prove that its systems could perform about as well as a person when it used a language pair — like Chinese to English — for which there is a lot of data.

“Given the best-case situation as far as data and availability of resources goes, we wanted to find out if we could actually match the performance of a professional human translator,” said Menezes.

To ensure the results were both accurate and at 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.

“Hitting human parity in a machine translation task is a dream that all of us have had. We just did not realise we would be able to hit it so soon,” said Xuedong Huang, Technical Fellow in charge of Microsoft’s speech, natural language and machine translation efforts.

Also Read: Microsoft Teams to have Cortana integration, other features

To reach the human parity milestone on this dataset, three research teams in Microsoft’s Beijing and Redmond, Washington, research labs worked together to make the system more accurate.

“Much of our research is really inspired by how we humans do things,” said Tie-Yan Liu, Principal Research Manager with Microsoft Research Asia in Beijing.

The team used dual-learning method. Every time they sent a sentence through the system to be translated from Chinese to English, the research team also translated it back from English to Chinese.

Microsoft Kaizala
The accuracy rate is high too. Wikimedia

That’s similar to what people might do to make sure that their automated translations were accurate, and it allowed the system to refine and learn from its own mistakes. Dual learning, which was developed by the Microsoft research team, can also be used to improve results in other AI tasks.

Another method, called deliberation networks, is similar to how people edit and revise their own writing by going through it again and again. The researchers taught the system to repeat the process of translating the same sentence over and over, gradually refining and improving the response, Microsoft said. IANS

Next Story

Google Claims Eye Doctors Can Turn More Effective Using AI

Without assistance, general ophthalmologists are significantly less accurate than the algorithm, while retina specialists are not significantly more accurate than the algorithm. 

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The research team at Google AI believes that some of these pitfalls may be avoided if the computer can "explain" its predictions. Pixabay

As Artificial Intelligence (AI) continues to evolve, diagnosing diseases has become faster with greater accuracy. A new study from the Google AI research group shows that physicians and algorithms working together are more effective than either one alone.

In the study, to be published in the journal Ophthalmology, the researchers created a system which not only improved the ophthalmologists’ diagnostic accuracy but also improved the algorithm’s accuracy.

The study expands on previous work from Google AI showing that its algorithm works roughly as well as human experts in screening patients for a common diabetic eye disease called diabetic retinopathy.

eyes
To test this theory, ten ophthalmologists (four general ophthalmologists, one trained outside the US, four retina specialists, and one retina specialist in training) were asked to read images with and without algorithm assistance. Pixabay

“What we found is that AI can do more than simply automate eye screening, it can assist physicians in more accurately diagnosing diabetic retinopathy. AI and physicians working together can be more accurate than either one alone,” said lead researcher Rory Sayres.

Recent advances in AI promise to improve access to diabetic retinopathy screening and to improve its accuracy. But it’s less clear how AI will work in the physician’s office or other clinical settings, the team said.

According to the team, previous attempts to use computer-assisted diagnosis shows that some screeners rely on the machine too much, which leads to repeating the machine’s errors, or under-rely on it and ignore accurate predictions.

The research team at Google AI believes that some of these pitfalls may be avoided if the computer can “explain” its predictions.

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Recent advances in AI promise to improve access to diabetic retinopathy screening and to improve its accuracy. But it’s less clear how AI will work in the physician’s office or other clinical settings, the team said. Pixabay

To test this theory, ten ophthalmologists (four general ophthalmologists, one trained outside the US, four retina specialists, and one retina specialist in training) were asked to read images with and without algorithm assistance.

Also Read: U.S. Government Human Rights Report Shows ‘Amber’ Warning Light Situation in Hong Kong

Without assistance, general ophthalmologists are significantly less accurate than the algorithm, while retina specialists are not significantly more accurate than the algorithm.

With assistance, general ophthalmologists match but do not exceed the model’s accuracy, while retina specialists start to exceed the model’s performance. (IANS)