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. NEW RESEARCH FLASH: A Workflow Automation Agent for Diagnosing Recurring Incidents Cloud incidents such as unplanned interruptions or performance degradation can reduce customer satisfaction and revenue. Recurring incidents, typically raised by system monitors, allow for timely resolution, but also demand significant human effort for troubleshooting. Automating the diagnosis of recurring incidents  

GraphRAG is a technique that uses large language models (LLMs) to create knowledge graphs and summaries from unstructured text documents and leverages them to improve retrieval-augmented generation (RAG) operations on private datasets. It offers comprehensive global overviews of large, private troves of unstructured text documents while also enabling exploration of detailed, localized information. By using LLMs to create comprehensive knowledge graphs that connect and describe entities and relationships contained in those documents, GraphRAG leverages semantic structuring  

ANGELA BUSHESKA: Yeah, absolutely. Thank you so much for having me. Super excited to be here and super excited to have spent three months—time flies!— as a researcher along with the project. So my name is Angela. I am originally from North Macedonia, a very small country next to Greece in the Balkans. And I spent my first, like, 16 years thinking that I would become a mathematician, really focused on math Olympiads. And then I moved to the capital […]

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. NEW RESEARCH Securely Training Decision Trees Efficiently In a recent paper: Securely Training Decision Trees Efficiently that will appear at ACM CCS 2024, researchers from Microsoft significantly reduce the communication complexity of secure decision tree training. Decision trees are an important  

Transforming raw data into meaningful visuals, such as charts, is key to uncovering hidden trends and valuable insights, but even with advances in AI-powered tools, this process remains complex. Integrating AI into the iterative nature of the data visualization process is particularly challenging, as data analysts often struggle to describe complicated tasks in a single text prompt while lacking the direct control of traditional tools. This highlights the need for smarter, more intuitive solutions that combine AI’s  

This paper has been accepted at the 18th European Conference on Computer Vision (ECCV 2024) (opens in new tab), the premier gathering on computer vision and machine learning.

MARTIN GRAYSON: Pleasure, thank you.  DANIELA MASSICETI: Thanks very much, Amber. Nice to be here.  TINGLE: So, Daniela, let’s start with a Find My Things overview. What is it, how does it work, and who’s it for?  MASSICETI: I think the best way I can describe Find My Things is a personalizable object recognizer. So when we think about object recognizers in the past, they’ve, kind of, been what I would call generic object recognizers. So they can only really […]

Microsoft Research Forum is a continuous exchange of ideas about science and technology research in the era of general AI. In the latest episode (opens in new tab), researchers discussed the latest multimodal AI models, advanced benchmarks for AI evaluation and model self-improvement, and an entirely new kind of computer for AI inference and hard optimization. Researchers at Microsoft are working to explore breakthrough technology that can help advance everything from weather prediction to materials design.  Below is a brief recap […]

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. NEW RESEARCH ProbTS: Benchmarking Point and Distributional Forecasting across Diverse Prediction Horizons Time-series forecasting is a technique used to predict future values based on previously observed data points over time. It has extensive applications for traffic flow, renewable energy, retail, finance, and climate, among other uses. For these applications,  

In the fast-paced progress of AI, the question of how to evaluate and understand capabilities of state-of-the-art models is timelier than ever. New and capable models are being released frequently, and each release promises the next big leap in frontiers of intelligence. Yet, as researchers and developers, often we ask ourselves: Are these models all comparable, if not