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To put these models in production, Microsoft is using NVIDIA GPUs and Triton Inference Server to deploy and scale them efficiently for high-performance inference.
This architecture allows massive scale in the number of model parameters while keeping the amount of compute constant. These models use a sparse “Mixture of Experts” approach that is more efficient to run because it only needs to engage a portion of the model to complete a task, as opposed to other architectures that have to activate an entire AI model to run every request.
“With Z-code we are really making amazing progress because we are leveraging both transfer learning and multitask learning from monolingual and multilingual data to create a state-of-the-art language model that we believe has the best combination of quality, performance and efficiency that we can provide to our customers,” Huang said. It also helps extend those capabilities beyond the most common languages across the globe to underrepresented languages that have less available training data.
Z-code takes advantage of shared linguistic elements across multiple languages via transfer learning -which applies knowledge from one task to another related task - to improve quality for machine translation and other language understanding tasks. “Our goal is to help everyone and every organization on the planet to communicate better, and to achieve that goal there are really two important dimensions - we want the quality of translations to be as good as possible and we want to support as many languages as possible,” said Xuedong Huang, Microsoft technical fellow and Azure AI chief technology officer.
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The copy-paste of the page "Geek Code" or any of its results, is allowed as long as you cite dCode!Ĭite as source (bibliography): Geek Code on dCode.Microsoft is making upgrades to Translator and other Azure AI services powered by a new family of artificial intelligence models its researchers have developed called Z-code, which offer the kind of performance and quality benefits that other large-scale language models have but can be run much more efficiently.
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