For many organizations, trusting their data to the cloud requires having a complete understanding of and control over the environment in which that data resides and how it’s being processed. Microsoft understands this, and we are committed to building a trustworthy cloud—one in which security,…
Editor’s note, Oct. 20, 2023 – The post was updated to remove information related to the Amsterdam lab, as those details have since changed. Over the coming decade, deep learning looks set to have a transformational impact on the natural sciences. The consequences are potentially…
Addressing and mitigating the effects of climate change requires a collective effort, bringing our strengths to bear across industry, government, academia, and civil society.
They make restaurant recommendations, help us pay bills, and remind us of appointments. Many people have come to rely on virtual assistants and chatbots to perform a wide range of routine tasks. But what if a single dialog agent, the technology behind these language-based apps,…
Early last year, our research team from the Visual Computing Group (opens in new tab) introduced Swin Transformer (opens in new tab), a Transformer-based general-purpose computer vision architecture that for the first time beat convolutional neural networks on the important vision benchmark of COCO object…
ICLR (International Conference on Learning Representations) (opens in new tab) is recognized as one of the top conferences in the field of deep learning. Many influential papers on artificial intelligence, statistics, and data science—as well as important application fields such as machine vision, speech recognition,…
Identifying causal effects is an integral part of scientific inquiry. It helps us understand everything from educational outcomes to the effects of social policies to risk factors for diseases. Questions of cause-and-effect are also critical for the design and data-driven evaluation of many technological systems…
It’s a well-known challenge that large language models (LLMs)—growing in popularity thanks to their adaptability across a variety of applications—carry risks. Because they’re trained on large amounts of data from across the internet, they’re capable of generating inappropriate and harmful language based on similar language…
Advances in platform models—large-scale models that can serve as foundations across applications—have significantly improved the ability of computers to process natural language. But natural language processing (NLP) models are still far from perfect, sometimes failing in embarrassing ways, like translating “Eu não recomendo este prato”…
Federated learning has become a major area of machine learning (ML) research in recent years due to its versatility in training complex models over massive amounts of data without the need to share that data with a centralized entity. However, despite this flexibility and the…
Technological innovation that enables scaling of quantum computing underpins the Microsoft Azure Quantum program. In March of this year, we announced our demonstration of the underlying physics required to create a topological qubit—qubits that are theorized to be inherently more stable than existing ones without…
Drug discovery has come a long way from its roots in serendipity. It is now an increasingly rational process, in which one important phase, called lead optimization, is the stepwise search for promising drug candidate compounds in the lab. In this phase, expert medicinal chemists…
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