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New AI Model Translates 200 Languages, Making Technology Accessible to More People -- I-COM

#artificialintelligence

Language is our lifeline to the world. But because high-quality translation tools don't exist for hundreds of languages, billions of people today can't access digital content or participate fully in conversations and communities online in their preferred or native languages. This is particularly an issue for hundreds of millions of people who speak the many languages of Africa and Asia. To help people connect better today and be part of the metaverse of tomorrow, our AI researchers created No Language Left Behind (NLLB), an effort to develop high-quality machine translation capabilities for most of the world's languages. Today, we're announcing an important breakthrough in NLLB: We've built a single AI model called NLLB-200, which translates 200 different languages with results far more accurate than what previous technology could accomplish.


Meta's NLLB-200 AI model improves translation quality by 44%

#artificialintelligence

Meta has unveiled a new AI model called NLLB-200 that can translate 200 languages and improves quality by an average of 44 percent. Translation apps have been fairly adept at the most popular languages for some time. Even when they don't offer a perfect translation, it's normally close enough for the native speaker to understand. However, there are hundreds of millions of people in regions with many languages – like Africa and Asia – that still suffer from poor translation services. "To help people connect better today and be part of the metaverse of tomorrow, our AI researchers created No Language Left Behind (NLLB), an effort to develop high-quality machine translation capabilities for most of the world's languages. Today, we're announcing an important breakthrough in NLLB: We've built a single AI model called NLLB-200, which translates 200 different languages with results far more accurate than what previous technology could accomplish."


La veille de la cybersécurité

#artificialintelligence

Language is our lifeline to the world. But because high-quality translation tools don't exist for hundreds of languages, billions of people today can't access digital content or participate fully in conversations and communities online in their preferred or native languages. This is particularly an issue for hundreds of millions of people who speak the many languages of Africa and Asia. To help people connect better today and be part of the metaverse of tomorrow, our AI researchers created No Language Left Behind (NLLB), an effort to develop high-quality machine translation capabilities for most of the world's languages. Today, we're announcing an important breakthrough in NLLB: We've built a single AI model called NLLB-200, which translates 200 different languages with results far more accurate than what previous technology could accomplish.


New AI Model Translates 200 Languages, Making Technology Accessible to More People

#artificialintelligence

Language is our lifeline to the world. But because high-quality translation tools don't exist for hundreds of languages, billions of people today can't access digital content or participate fully in conversations and communities online in their preferred or native languages. This is particularly an issue for hundreds of millions of people who speak the many languages of Africa and Asia. To help people connect better today and be part of the metaverse of tomorrow, our AI researchers created No Language Left Behind (NLLB), an effort to develop high-quality machine translation capabilities for most of the world's languages. Today, we're announcing an important breakthrough in NLLB: We've built a single AI model called NLLB-200, which translates 200 different languages with results far more accurate than what previous technology could accomplish.


Supervised Visual Attention for Simultaneous Multimodal Machine Translation

Journal of Artificial Intelligence Research

There has been a surge in research in multimodal machine translation (MMT), where additional modalities such as images are used to improve translation quality of textual systems. A particular use for such multimodal systems is the task of simultaneous machine translation, where visual context has been shown to complement the partial information provided by the source sentence, especially in the early phases of translation. In this paper, we propose the first Transformer-based simultaneous MMT architecture, which has not been previously explored in simultaneous translation. Additionally, we extend this model with an auxiliary supervision signal that guides the visual attention mechanism using labelled phrase-region alignments. We perform comprehensive experiments on three language directions and conduct thorough quantitative and qualitative analyses using both automatic metrics and manual inspection. Our results show that (i) supervised visual attention consistently improves the translation quality of the simultaneous MMT models, and (ii) fine-tuning the MMT with supervision loss enabled leads to better performance than training the MMT from scratch. Compared to the state-of-the-art, our proposed model achieves improvements of up to 2.3 BLEU and 3.5 METEOR points.


Petuum and Inception Institute for AI Partner for Advanced AI

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Petuum, the creator of the world's first composable platform for MLOps, and the Inception Institute for Artificial Intelligence (IIAI), have agreed to partner on the development of revolutionary AI applications. Petuum has recently announced a limited release of the composable platform, which includes the AI OS, Universal Pipelines, Deployment Manager, and Experiment Manager, for select private beta partners. Through the partnership with Petuum, IIAI's enterprise AI/ML teams will operationalize and scale their applications into production. Founded in 2018, IIAI's mission is to build full-stack AI solutions and operating systems for enterprise businesses and developers. Besides being the research arm for G42, IIAI is also empowering stakeholders with AI applications and incubating new technology at the cutting edge of ML innovation.


Google's massive language translation work identifies where it goofs up

ZDNet

Scores for languages when translating from English and back to English again, correlated to how many sample sentences the language has. Toward the right side, higher numbers of example sentences result in better scores. There are outliers, such as English in Cyrillic, which has very few examples but translates well. What do you do after you have collected writing samples for a thousand languages for the purpose of translation, and humans still rate the resulting translations a fail? And that is the interesting work that Google machine learning scientists related this month in a massive research paper on multi-lingual translation, "Building Machine Translation Systems for the Next Thousand Languages."


Google Translate Provides Assist for Extra Indian Languages - Channel969

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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.


Google Translate gains 24 new languages from the Americas, India, and Africa

ZDNet

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 Translate adds support for 24 new languages

Engadget

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.