From studying how to identify gender bias in Hindi to uncovering AI-related risks for workers, Microsoft is making key contributions towards advancing the state of the art in responsible AI research. Check out their work at ACM FAccT 2024.
Aurora, a new AI foundation model from Microsoft Research, can transform our ability to predict and mitigate extreme weather events and the effects of climate change by enabling faster and more accurate weather forecasts than ever before.
Synthetic data could potentially help address some privacy concerns with AI model development and training, but it comes with limitations. Researchers at Microsoft are exploring techniques for producing more realistic data with strong privacy protections.
How can generative AI tools represent less common identities and narratives; Can LLMs help players participate in game narratives; Using LLMs to improve geospatial demographic data; A Graph RAG Approach to Query-Focused Summarization; and more.
Digital pathology helps decode tumor microenvironments for precision immunotherapy. In joint work with Providence and UW, we’re sharing Prov-GigaPath, the first whole-slide pathology foundation model, for advancing clinical research.
Welcome to Research Focus, a series of blog posts that highlights notable publications, events, code/datasets, new hires and other milestones from across the research community at Microsoft. Large language models (LLMs) have shown remarkable performance in generating text similar to that created by people, proving…
From immersive virtual experiences to interactive design tools, Microsoft Research is at the frontier of exploring how people engage with technology. Discover our latest breakthroughs in human-computer interaction research at CHI 2024.
RASCAL is an untethered robot with a modular design, allowing it to move flexibly along and between evenly spaced storage shelves. Discover how it can address the availability and scalability challenges of existing automated storage and retrieval systems.
Autoscaling can optimize cloud resource usage and costs by adjusting to demand. VASIM shows that simplifying testing and refinement of autoscaling algorithms can enable rapid development and evaluation of more efficient & cost-effective autoscaling strategies.
Property prediction for materials under realistic conditions has been a long-standing challenge within the digital transformation of materials design. MatterSim investigates atomic interactions from the very fundamental principles of quantum mechanics.
LLMs rely on memory-intensive mechanisms like the key-value (KV) cache to store and quickly retrieve data. FastGen optimizes KV cache usage, reducing LLM memory demands by up to 50% while maintaining performance.
LoftQ boosts LLM efficiency by streamlining the fine-tuning process, reducing computational demands while preserving high performance. Innovations like this can help make AI technology more energy-efficient.
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