Semantic Telemetry Project data show that people who use AI for more professional and complex tasks are more likely to keep using the tool and to use it more often. Novice AI users engage in simpler tasks, but their usage is becoming more complex.
Developers spend a lot of time debugging code. Learn how debug-gym can equip AI agents to help, enabling them to set breakpoints, navigate the codebase, and print runtime variable values on demand, so they better understand the code and its execution flow.
In this issue: We introduce a new dataset designed to assist renewable energy infrastructure planners, a new method for denoising MRI imagery, and an AI tool for analyzing distant galaxies. Check out our latest research and other updates.
The VidTok method can enable AI systems to process and generate videos more effectively. Compact tokenization reduces computational costs while maintaining video quality across a potentially diverse set of applications.
In this issue, we examine a new conversation segmentation method that delivers more coherent and personalized agent conversation, and we review efforts to improve MLLMs’ understanding of geologic maps. Check out the latest research and other updates.
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.
While large language models (LLMs) are capable of synthesizing vast amounts of information, they sometimes produce inaccurate or unsubstantiated content. To mitigate this risk, tools like Azure AI’s Groundedness Detection (opens in new tab) can be used to verify LLM outputs. A common strategy for…
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.
AI interactions can be iterative and complex. Learn how the Semantic Telemetry project at Microsoft Research is developing a new data science approach to understand human-AI interactions and their value.
Our recent study in Nature Scientific Reports identified key challenges in the biomedical data lifecycle and offered 7 actionable recommendations.
Explore Magma, a foundation model that can empower AI assistants to interpret environments, plan actions, and execute tasks across digital and physical spaces. Now available, learn how it advances the field of agentic AI.
Meet BioEmu-1 from Microsoft Research. This deep learning model can generate thousands of protein structures per hour, unlocking new possibilities for protein scientists and drug discovery and research.
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