Congratulations to danah boyd for being elected #AAASFellow by the American Association for the Advancement of Science (AAAS) for contributions to Information, Computing, and Communication! This distinction is among the highest in the scientific community. Learn more: https://msft.it/6043qiUWW
Microsoft Research
智库
Redmond，Washington 309,173 位关注者
We advance science and technology to benefit humanity.
关于我们
At Microsoft Research, we accelerate scientific discovery and technology innovation to empower every person and organization on the planet to achieve more. We do this by bringing together the best minds across diverse disciplines and backgrounds to take on the most pressing research challenges for Microsoft and for society. Our Research Lens We consider research directions through the lens of the positive impact we aspire to create with and for customers, communities, and all of society.
- 网站
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https://www.microsoft.com/research
Microsoft Research的外部链接
- 所属行业
- 智库
- 规模
- 1,001-5,000 人
- 总部
- Redmond，Washington
- 创立
- 1991
动态
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Generative AI is becoming part of the daily workflow in clinics. In Ep. 1 of “The AI Revolution in Medicine, Revisited,” Dr. Christopher Longhurst & Dr. Sara Murray, MD, MAS discuss how new AI tools are being used by clinicians & the early impact they’re having. https://msft.it/6043qelBS
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In Episode 1 of the podcast “The AI Revolution in Medicine, Revisited,” host Peter Lee, president of Microsoft Research, & guests Dr. Christopher Longhurst & Dr. Sara Murray, MD, MAS discuss how generative AI is impacting patient care & workflows in healthcare: https://msft.it/6042qZALr
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??? New AI model aims to provide weather forecasts entirely driven by AI A new AI weather prediction system, Aardvark Weather, can deliver accurate forecasts tens of times faster and using thousands of times less computing power than current AI and physics-based forecasting systems, according to research published today in Nature. ?? Traditional public weather forecasts are generated by a complex process requiring many stages, run on supercomputers and supported by large teams. ?? Recent AI tools developed by tech companies and forecasting agencies have provided a replacement for one component of the process. ?? Aardvark now replaces the entire process offering the potential for vast improvements in forecast speed, accuracy and cost. Richard Turner, Lead Researcher for Weather Prediction at the Alan Turing Institute and Professor of Machine Learning in the Department of Engineering at the University of Cambridge, said: “Aardvark reimagines current weather prediction methods offering the potential to make weather forecasts faster, cheaper, more flexible and more accurate than ever before, helping to transform weather prediction in both developed and developing countries.” Aardvark was created by Richard Turner’s team at the University of Cambridge (with Anna Allen and Stratis M. leading the project). The project was supported by the Alan Turing Institute, Microsoft Research and the European Centre for Medium-Range Weather Forecasts - ECMWF. The multi-disciplinary team came together to provide research expertise, funding, access to computational resource and data (such as ECMWF’s ERA5 dataset which was essential for training Aardvark). We’re delighted that last year Professor Turner established a new team at the Turing to focus on environmental prediction and integrate the technology into the Institute’s wider work on forecasting of weather, oceans and sea ice. Read our news story here: https://bit.ly/4hpsRZs Read the research in Nature ‘End-to-end data-driven weather prediction’: https://lnkd.in/eSvGTzdb #AI #ArtificialIntelligence #Innovation #WeatherForecasting #WeatherPrediction
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Metasurfaces explore engineered 2D materials that manipulate electromagnetic and mechanical waves, offering advances in wireless tech. They can power indoor GPS, extend 5G/6G coverage, and enable wireless sensing and imaging: https://msft.it/6046qkE98
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Claimify, created by Microsoft Research, is a novel LLM-based claim-extraction method that outperforms prior solutions to produce more accurate, comprehensive, and substantiated claims from LLM outputs. Learn more. https://msft.it/6040qX1Rq
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Introducing KBLaM, an approach that encodes and stores structured knowledge within an LLM itself. By integrating knowledge without retraining, it offers a scalable alternative to traditional methods. https://msft.it/6040qniGY
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