Intelligence Toolkit was built to help fight human trafficking and is applicable to a broad range of societal challenges. Learn how Microsoft researchers worked with global experts to develop generative AI tools that could help tackle urgent issues at scale.
GraphRAG, a graph-based approach to retrieval-augmented generation (RAG) that significantly improves question-answering over private or previously unseen datasets, is now available on GitHub.
In this issue: RENC makes 5G vRAN servers more energy efficient; CoExplorer uses AI to keep video meetings on track; Automatic bug detection in LLM-powered text-based games; MAIRA-2: Grounded radiology report generation.
Software-driven wide area network (SWAN) enables central management and control of network infrastructure to improve reliability and efficiency. A Microsoft researcher describes its journey from a research idea to a foundational system for Microsoft Azure.
The Bayesian behavior framework synergizes habits and goals through variational Bayesian methods, offering new insights on sensorimotor behavior and comprehension of actions.
MicroCode offers an affordable way to program the BBC micro:bit without needing an internet connection, fostering exploratory learning.
Microsoft is proud to sponsor the 41st annual Conference on Computer Vision and Pattern Recognition (CVPR 2024), held from June 17 to June 21. This premier conference covers a broad spectrum of topics in the field, including 3D reconstruction and modeling, action and motion analysis,…
AutoGen Studio, built on Microsoft’s flexible open-source AutoGen framework for orchestrating AI agents, provides an intuitive user-friendly interface that enables developers to rapidly build, test, customize, and share multi-agent AI solutions—with little or no coding.
In this issue: RELEVANCE automatically evaluates creative LLM responses; Recyclable vitrimer-based printed circuit boards; Lean Attention: Hardware-aware scalable attention mechanism; WaveCoder: a fine-tuned code LLM; New AutoGen training course.
SIBYL is a machine learning model that makes highly accurate predictions of database queries, enabling tuning for more efficiency. Applying traditional database optimizations to these predicted queries helps maintain high performance as demands change.
LST-Bench is a new open-source benchmark designed to evaluate table formats in cloud environments. It extends existing benchmarks to better reflect real-world usage & performance of data lakes and easily integrates with commonly used analytical engines.
“We’re at the very early stage of generative AI and the impacts it will have on work. This is a fast-moving field, and there's an immense opportunity to take control of the agenda and build truly globally equitable AI systems”, —Jacki O’Neill, Lab Director, Microsoft…
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