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AI at Facebook Improves Urdu to English Translation

Earlier this year, Microsoft brought machine learning to improve language translation for Hindi, Bengali and Tamil

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Facebook blocks more accounts for malicious activities. Pixabay
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Artificial Intelligence (AI) researchers at Facebook have set a new record in improving translation from Urdu to English.

San Francisco, Sep 2 (IANS) Artificial Intelligence (AI) researchers at Facebook have set a new record in improving translation from Urdu to English.

Neural Machine Translation (NMT) is the field concerned with using AI to do translations in any language.

The team from Facebook AI Research (FAIR) has seen a dramatic improvement in its results, the Forbes reported on Saturday.

“To give some idea of the level of advancement, an improvement of 1 BLEU point (a common metric for judging the accuracy of MT) is considered a remarkable achievement in this field; our methods showed an improvement of more than 10 BLEU points,” the team said in a paper that described translation from Urdu to English.

Facebook AI researchers seek to understand and develop systems with human-level intelligence by advancing the longer-term academic problems surrounding AI.

The research covers the full spectrum of topics related to AI, and to deriving knowledge from data: theory, algorithms, applications, software infrastructure and hardware infrastructure.

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Facebook App on a smartphone device. (VOA)

“Long-term objectives of understanding intelligence and building intelligent machines are bold and ambitious, and we know that making significant progress towards AI can’t be done in isolation,” said researchers from FAIR.

FAIR researchers have tested a new approach that teaches bots how to chit-chat like humans.

Facebook is making deep investments in AI technology and in May announced the next version of its open-source AI framework for developers.

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Microsoft is currently leading when it comes to AI and Deep Neural Networks to improve real-time language translation.

Earlier this year, Microsoft brought machine learning to improve language translation for Hindi, Bengali and Tamil.

With Deep Neural Networks-powered language translation, the results are more accurate and the sound more natural. (IANS)

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Unable To Find The Source of Fake Accounts: Facebook

Sample images provided by Facebook showed posts on a wide range of issues.

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Lexi Sturdy, election war room lead, sits at her desk in the war room, where Facebook monitors election-related content on the platform, in Menlo Park, California. VOA

Facebook said Tuesday it had been unable to determine who was behind dozens of fake accounts it took down shortly before the 2018 U.S. midterm elections.

“Combined with our takedown last Monday, in total we have removed 36 Facebook accounts, 6 Pages, and 99 Instagram accounts for coordinated inauthentic behavior,” Nathaniel Gleicher, head of cybersecurity policy, wrote on the company’s blog.

At least one of the Instagram accounts had well over a million followers, according to Facebook.

Facebook, U.S.
A man works in the war room, where Facebook monitors election-related content, in Menlo Park, Calif. VOA

A website that said it represented the Russian state-sponsored Internet Research Agency claimed responsibility for the accounts last week, but Facebook said it did not have enough information to connect the agency that has been called a troll farm.

“As multiple independent experts have pointed out, trolls have an incentive to claim that their activities are more widespread and influential than may be the case,” Gleicher wrote.

Sample images provided by Facebook showed posts on a wide range of issues. Some advocated on behalf of social issues such as women’s rights and LGBT pride, while others appeared to be conservative users voicing support for President Donald Trump.

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The viewpoints on display potentially fall in line with a Russian tactic identified in other cases of falsified accounts. A recent analysis of millions of tweets by the Atlantic Council found that Russian trolls often pose as members on either side of contentious issues in order to maximize division in the United States. (VOA)