🗺 Edge#214: NLLB-200, Meta AI’s New Super Model that Achieved New Milestones in Machine Translations Across 200 L…
Was this email forwarded to you? Sign up here 🗺 Edge#214: NLLB-200, Meta AI’s New Super Model that Achieved New Milestones in Machine Translations Across 200 LanguagesOne of the most important achievements to bring machine translation to low-resource languagesOn Thursdays, we dive deep into one of the freshest research papers or technology frameworks that is worth your attention. Our goal is to keep you up to date with new developments in AI to complement the concepts we debate in other editions of our newsletter. 💥 What’s New in AI: NLLB-200, Meta AI’s New Super Model that Achieved New Milestones in Machine Translations Across 200 LanguagesMachine translation is one of the deep learning disciplines that can have an immediate social impact. These days, there are large segments of the world population that can’t access online content in their native languages. Similarly, most of the advancements in natural language understanding (NLU) have been constrained to high-resource languages such as English, Spanish or French that have vast volumes of training data available. Expanding translations to hundreds of low-resource languages and dialects is one of the most critical challenges of the next decade of machine translation. Recently, Meta AI open-sourced no language left behind (NLLB)-200, a model that can perform state-of-the-art machine translation across 200 languages. Meta AI also open-sourced a few complementary datasets and frameworks that can help expedite research in machine translation for low-resource languages. The research paper for NLLB-200 is an astonishing 190 pages long. Let’s dive into it!... Keep reading with a 7-day free trialSubscribe to TheSequence to keep reading this post and get 7 days of free access to the full post archives. A subscription gets you:
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🩺 Edge#213: Testing Trained Models
Tuesday, August 2, 2022
the fundamental types of tests that can be applied to trained models +how Meta uses Bayesian Optimization for A/B tests +TensorFlow's What-If Tool
📝 Guest post: Using AI to Learn a Disentangled Gait Representation for Versatile Quadruped Locomotion*
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The Oxford Robotics Institute (ORI) is built from collaborating and integrated groups of researchers, engineers and students all driven to change what robots can do for us. The ORI is interested in a
🧬 DeepMind’s AlphaFold Database
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📝 Guest post: Getting ML data labeling right*
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What's the best way to gather and label your data? There's no trivial answer as every project is unique. But you can get some ideas from a similar case to yours. In this article, Toloka's
👁 Edge#212: Inside the Masterful CLI Trainer, a low-code CV model development platform
Thursday, July 28, 2022
On Thursdays, we deep dive into one of the freshest research papers or technology frameworks that is worth your attention. Our goal is to keep you up to date with new AI developments and introduce the
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