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, privacy, and transparency are built into its core. A key part of this vision is confidential computing—a set of hardware and software capabilities that give  

Over the coming decade, deep learning looks set to have a transformational impact on the natural sciences. The consequences are potentially far-reaching and could dramatically improve our ability to model and predict natural phenomena over widely varying scales of space and time. Could this capability represent the dawn of a new paradigm of scientific discovery? Jim Gray, a Turing Award winner, and former Microsoft Technical Fellow, characterised the historical evolution of scientific discovery through 

Addressing and mitigating the effects of climate change requires a collective effort, bringing our strengths to bear across industry, government, academia, and civil society. As we continue to explore the role of technology to advance the art of the possible, we are launching the Microsoft Climate Research Initiative (MCRI). This community of multi-disciplinary researchers is working together to accelerate cutting-edge research and transformative innovation in climate science and technology. MCRI enables us to bring  

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  

ICLR (International Conference on Learning Representations) is recognized as one of the top conferences in the field of deep  

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  

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 encountered during training.   Content moderation tools can be deployed to flag or filter such language in some contexts, but unfortunately, datasets available to train these tools often fail to capture the  

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” (I don’t recommend this dish) in Portuguese to “I highly recommend this dish” in English (a real example from a top commercial model). These failures continue to exist in part because finding and fixing bugs […]