Garnet is a cache-store system that addresses growing demand for data storage to support interactive web applications and services. Offering several advantages over legacy cache-stores, Garnet is now available as an open-source download.
As avatar use expands in digital spaces, advances are required to better represent all people. Discover how research into the varying perceptions of facial animation glitches in low versus high realism scenarios supports this goal.
Microsoft Research and Cyted have collaborated to build novel AI models (opens in new tab) to scale the early detection of esophageal cancer. The AI-supported methods demonstrated the same diagnostic performance as the existing manual workflow, potentially reducing the pathologist's workload by up to 63%.…
Structural Understanding Capabilities is a new benchmark for evaluating and improving LLM comprehension of structured table data. This advance can help LLMs process and analyze data more effectively, broadening their applicability in real-world tasks.
Research advances are driving real-world impact faster than ever. Episode 2 of Microsoft Research Forum explores how AI is transforming health care and the natural sciences, the intersection of AI and society, and the evolution of foundational AI technologies.
In this issue: Generative kaleidoscopic networks; Text diffusion with reinforced conditioning; PRISE – Learning temporal action abstractions as a sequence compression problem.
Microsoft’s Orca-Math, a specialized small language model, outperforms much larger models in solving math problems that require multi-step reasoning and shows the potential of using feedback to improve language models. Learn more.
Molecular geometry modeling is a powerful tool for understanding the intricate relationships between molecular structure and biological activity – a field known as structure-activity relationships (SAR). The main premise of SAR is that the biological activity of a molecule is dictated by its specific chemical…
Using LLMs to create structured graphs of image descriptors can enhance the images generated by visual language models. Learn how structured knowledge can improve prompt tuning for both visual and language comprehension.
In this issue: CaaSPER: vertical autoscaling algorithm dynamically maintains optimal CPU utilization; Improved scene landmark detection for camera localization runs faster, uses less storage; ESUS simplifies usability questionnaires for technical products and services.
Editor’s note, Apr. 2, 2024 – Figure 1 was updated to clarify the origin of each source. Perhaps the greatest challenge – and opportunity – of LLMs is extending their powerful capabilities to solve problems beyond the data on which they have been trained, and…
The emergence of large language models (LLMs) has revolutionized the way people create text and interact with computing. However, these models are limited in ensuring the accuracy of the content they generate and enforcing strict compliance with specific formats, such as JSON and other computer…
