Advances in run-time strategies for next-generation foundation models
Discover the most effective run-time strategies on the OpenAI o1-preview model, improving accuracy in medical language tasks.
Microsoft Research Blog
Research Blog
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Accelerating drug discovery with TamGen: A generative AI approach to target-aware molecule generation
November 25, 2024TamGen uses generative AI to design new drug candidate compounds to treat TB, going beyond traditional methods by generating novel chemical structures. Learn how a collaboration with the Global Health Drug Discovery Institute is making this possible.
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LazyGraphRAG: Setting a new standard for quality and cost
Introducing a new approach to graph-enabled RAG. LazyGraphRAG needs no prior summarization of source data, avoiding prohibitive up-front indexing costs. It’s inherently scalable in cost and quality across multiple methods and search mechanisms.
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Introducing Yasuyuki Matsushita: Tackling societal challenges with AI at Microsoft Research Asia – Tokyo
November 18, 2024Yasuyuki Matsushita rejoins Microsoft to lead the new Microsoft Research Asia - Tokyo lab. Learn more about his journey and his perspective on the Tokyo lab's role in evolution of AI.
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BiomedParse: A foundation model for smarter, all-in-one biomedical image analysis
November 18, 2024BiomedParse reimagines medical image analysis, integrating advanced AI to capture complex insights across imaging types—a step forward for diagnostics and precision medicine.
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GraphRAG: Improving global search via dynamic community selection
Retrieval-augmented generation (RAG) helps AI systems provide more information to a large language model (LLM) when generating responses to user queries. A new method for conducting “global” queries can optimize the performance of global search in GraphRAG.
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Orca-AgentInstruct: Agentic flows can be effective synthetic-data generators
November 14, 2024 | Arindam Mitra, Ahmed Awadallah, and Yash LaraOrca-AgentInstruct, from Microsoft Research, can generate diverse, high-quality synthetic data at scale to post-train and fine-tune base LLMs for expanded capabilities, continual learning, and increased performance.
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Toward modular models: Collaborative AI development enables model accountability and continuous learning
Modular models can democratize AI development while unlocking new benefits and use cases. Modularized AI can be more flexible, more compliant, and cheaper to develop—requiring less data and fewer compute resources to train expert models.
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Research Focus: Week of November 11, 2024
November 13, 2024Holistic motion-capture calibration technique without calibration, manual intervention or custom hardware; Research on AI agents for autonomous clouds; Automating proof-oriented program construction; One-to-many testing for natural language code generation.
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Preventing side-channels in the cloud
November 12, 2024 | Stavros Volos and Boris KöpfSophisticated side-channel attacks present new security challenges for cloud providers. Learn how Microsoft is exploring defenses against emerging attacks with principled microarchitectural isolation:
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From static prediction to dynamic characterization: AI2BMD advances protein dynamics with ab initio accuracy
November 6, 2024Microsoft Research’s AI2BMD, an AI-based system that efficiently simulates a wide range of proteins in all-atom resolution, can advance drug discovery and biomolecular research.
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Microsoft at SOSP 2024: Innovations in systems research
November 4, 2024Building resilient systems, scaling deep learning computation, and reproducing failures in production are just some of the ways Microsoft researchers are advancing the state of the art in computer systems research at SOSP 2024.