Google Translate has added help for some extra Indian languages. Whereas Hindi has been supported by Google Translate for an extended now, a number of new regional languages have been added to the platform by Google. Languages together with Assamese, a outstanding one in Northeast India; Bhojpuri, Dhivehi (used within the Maldives), Dogri (Northern India), Konkani (central India), Maithili (about 34 million folks in Northern India communicate this language), Meiteilon or Manipuri, utilized by about two million folks in Northeast India, Mizo, and Sanskrit have been added to the platform. Together with these languages, Google Translate has additionally added help for a number of worldwide languages. Now, Google Translate helps over 133 languages spoken internationally, protecting main Indian languages as properly.

"For many supported languages, even the largest languages in Africa that we have supported - say like Yoruba, Igbo, the translation is not great. It will definitely get the idea across but often it will lose much of the subtlety of the language," Google Translate research scientist Isaac Caswell told the BBC.

Michael is a veteran technology writer who has been covering business and consumer-focused hardware and software for over a decade. Google revealed a total of 24 new languages coming to its Google Translate platform at this year's I/O event. The full list of new supported languages includes dialects spoken by a total of 300 million people across the globe, Google said. The most widely spoken of the new lot, Bhojpuri, is used by around 50 million speakers in northern India, Nepal, and Fiji. Meanwhile, the rarest addition, Sanskrit, remains in use by just 20,000 individuals in India.

Google is adding support for 24 new languages to its Translate tool, the company announced today during its I/O 2022 developer conference. Among the newly available languages are Sanskrit, Tsongae and Sorani Kurdish. One of the new additions, Assamese, is used by approximately 25 million people in Northeast India. Another, Dhivehi, is spoken by about 300,000 people in the Maldives. According to Google CEO Sundar Pichai, the expansion allows the company to cover languages spoken by more than 300 million people and brings the total number of languages supported by Translate to 133.

According to Statista, digital publishing generates worldwide revenue of $22.05 billion. Globally, countries that have access to digital media have witnessed a sharp rise in its popularity. However, with global accessibility comes the challenge of producing high-quality content consistently in large volumes. Additionally, with the rise in voice-based and image searches, content discoverability is the need of the hour. Artificial intelligence (AI) can help in this endeavor.

In a critical episode of The Mandalorian, a TV series set in the Star Wars universe, a mysterious Jedi fights his way through a horde of evil robots. As the heroes of the show wait anxiously to learn the identity of their cloaked savior, he lowers his hood, and--spoiler alert-- they meet a young Luke Skywalker. Actually, what we see is an animated, de-aged version of the Jedi. Then Luke speaks, in a voice that sounds very much like the 1980s-era rendition of the character, thanks to the use of an advanced machine learning model developed by the voice technology startup Respeecher. "No one noticed that it was generated by a machine," says Dmytro Bielievtsov, chief technology officer at Respeecher.

State-of-the-art multilingual machine translation relies on a shared encoder-decoder. In this paper, we propose an alternative approach based on language-specific encoder-decoders, which can be easily extended to new languages by learning their corresponding modules. To establish a common interlingua representation, we simultaneously train N initial languages. Our experiments show that the proposed approach improves over the shared encoder-decoder for the initial languages and when adding new languages, without the need to retrain the remaining modules. All in all, our work closes the gap between shared and language-specific encoder-decoders, advancing toward modular multilingual machine translation systems that can be flexibly extended in lifelong learning settings.

Eugene Rudenko, the AI Solution Consultant co-authored this Machine Learning development article with Vitaliy, Data Scientist at NIX United. The piece reveals a three-pronged method to putting ML models into production, as well as a commercial perspective.

Let's explore highlights and accomplishments of vast Google Machine Learning communities over the first quarter of the year! We are enthusiastic and grateful about all the activities that the communities across the globe do. ML Olympiad is an associated Kaggle Community Competitions hosted by Machine Learning Google Developers Experts (ML GDEs) or TensorFlow User Groups (TFUGs) sponsored by Google. The first round was hosted from January to March, suggesting solving critical problems of our time. TFUG organizer Ali Mustufa Shaikh (TFUG Mumbai) and Rishit Dagli won the TensorFlow Community Spotlight award (paper and code).

Lilt, a provider of AI-powered business translation software, today announced that it raised $55 million in a Series C round led by Four Rivers, joined by new investors Sorenson Capital, CLEAR Ventures and Wipro Ventures. The company says that it plans to use the capital to expand its R&D efforts as well as its customer footprint and engineering teams. "Lilt [aims to] build a solution that [will] combine the best of human ingenuity with machine efficiency," CEO Spence Green told TechCrunch via email. We are in three regions -- the U.S., Europe, the Middle East and Africa (EMEA) and Asia -- and look to have both sales and production teams in each of these regions." San Francisco, Calfornia-based Lilt was co-founded by Green and John DeNero in 2015. Green is a former Northrop Grumman software engineer who later worked as a research intern on the Google Translate team, developing an AI language system for improving English-to-Arabic translations. DeNero was previously a senior research scientist at Google, mostly on the Google Translate side, and a teaching professor at the University of California, Berkeley. "15 years ago, I was living in the Middle East, where you make less money if you speak anything other than English.