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AI at Scale
At Microsoft, we are pioneering a new approach to AI that is fueling the next generation of AI innovation at scale.
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Accelerating innovation with AI at Scale
On the TWIML podcast, host Sam Charrington and David Carmona from Microsoft discuss the future of AI at Scale and the impact of AI-powered supercomputing on innovation.
Accelerating AI innovation
Today, AI is bound by limitations of infrastructure, effectiveness of machine learning models, and ease of development. AI at Scale expands beyond these limitations to allow rapid acceleration of AI innovation.
Realizing next-generation AI
Discover how we are helping computers more fully perceive the nuances of our world.
Unlocking new opportunities
AvePoint uses AI at Scale and customized Turing models to keep up with the vast, dynamic knowledge that powers their business.
Start building at scale
Access our AI models, training optimization tools and supercomputing resources.
Taking on big challenges
AI at Scale works at unprecedented levels of complexity to solve some of today's biggest challenges.
Machine learning
Advancements in AI models are changing how AI is developed and advanced through the creation of large-scale, centralized models that can be scaled and specialized across product domains. Supercomputing is crucial to leveraging data with billions of parameters. AI at the scale of billions enables us to solve complex challenges like natural language processing.
Natural language processing
AI at Scale is unlocking breakthroughs in natural language processing (NLP) across text, images, and video, allowing humans to interact with data more naturally than ever before. NLP powers virtual assistants, analysis of research or records, and more. Beyond interpretation, NLP can produce content—generating tests in education, or imagining new ideas for movies, books, and other media.
Explore the possibilities of AI
Begin driving digital transformation and innovation with new skills and ideas.
Building an AI-powered organization
Everyone has a role to play in AI transformation. Learn about fueling innovation at all levels, evaluating AI investments, and establishing technical processes for AI throughout your organization.
Explore AI Business School
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From conversation to code: Building AI apps with OpenAI GPT-3
Powerful natural language model GPT-3 is now integrated in Microsoft Power Apps, the low-code AI platform, making it easy to develop sophisticated conversational interfaces.
Unlocking unprecedented scale for deep learning
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T-ULRv2 leads XTREME leaderboard
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Microsoft Turing Academic Program (MS-TAP)
MS-TAP research advances principles of learning and reasoning, exploring novel applications, and understanding the responsible use of large-scale neural language models.
OpenAI’s groundbreaking GPT-3 language model
GPT-3 is the largest and most advanced language model in the world with 175 billion parameters, enabling remarkably human-like text abilities. Learn about the Microsoft exclusive license.
DeepSpeed learning optimization library
DeepSpeed can train DL models with over a hundred billion parameters on current generation of GPU clusters, while achieving over 10x in system performance compared to the state-of-art.
Microsoft DeBERTa tops SuperGLUE test
SuperGLUE is a benchmark for evaluating NLU models, including question answering, inference, co-reference resolution, word disambiguation, and other tasks. Learn how DeBERTa performs.
Turing-NLG: 17-billion-parameter language model
Turing Natural Language Generation (T-NLG) is a 17 billion parameter language model by Microsoft that outperforms the state of the art on many downstream NLP tasks.
AI from Bing powers Azure Cognitive Search
Azure Cognitive Search gives developers tools to build rich experiences in any applications. Now search can go beyond keywords to semantic meaning behind words and content.
VinVL: Advancing vision-language models
Vision-language (VL) systems enable computers to effectively learn from visual information to make sense of the world around us. VinVL is advancing state-of-the-art performance on VL tasks.
ONNX Runtime accelerates training models 45% faster
ONNX Runtime is an open-source project for machine learning. Used in products like Office 365 and Bing, it delivers over 20 billion inferences every day and up to 17 times faster inferencing.
