In this issue: New research on appropriate reliance on generative AI; Power management opportunities for LLMs in the cloud; LLMLingua-2 improves task-agnostic prompt compression; Enhancing COMET to embrace under-resourced African languages:
In this issue of Research Focus: Optimized exit-augmented models for scalable efficient inference; NeurIPS LLM Efficiency Challenge; LLM-empowered automated data exploration; Boosting cloud efficiency with data-driven decision-making and optimization.
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. Generating both plausible and accurate full body avatar motion is essential for creating high quality immersive experiences in…
This research paper was presented at the 64th IEEE Symposium on Foundations of Computer Science (FOCS) 2023 (opens in new tab), a premier forum for the latest research in theoretical computer science. Submodular functions are versatile mathematical tools, finding diverse applications in real-world scenarios and…
Research Focus: Principal researcher Lester Mackey recognized for pioneering statistical and ML techniques; Pareto frontiers in neural feature learning; structural inequality in the influencer industry; new research on cardinality estimation.
Efficiency is vital in the face of escalating demand for cloud resources. And efficient power management strategies address the bottleneck of power availability in datacenters. Learn how we optimize power allocation to support sustainable resource usage.
In this issue: Peter Lee discusses AI in medicine. Plus, new research on data inference privacy in machine learning; PII leakage in language models; and automatic prompt organization with gradient descent and beam search.
Microsoft Editor provides AI-powered writing assistance to millions of users around the world. One of its features that writers of all levels and domains rely on is the grammar checker, which detects grammar errors in a user's writing and offers suggested corrections and explanations of…
In this issue: Microsoft researchers win four more awards; AutoRXN automates calculations of molecular systems; LLM accelerator losslessly improves the efficiency of autoregressive decoding; a frequency domain approach to predict power system transients.
TLA+ is a high level, open-source, math-based language for modeling computer programs and systems–especially concurrent and distributed ones. It comes with tools to help eliminate fundamental design errors, which are hard to find and expensive to fix once they have been embedded in code or hardware. …
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. To improve the utilization of computing resources, cloud providers often offer underutilized capacity at a discount, but with…
In the first blog post in this series, Cloud Intelligence/AIOps – Infusing AI into Cloud Computing Systems, we presented a brief overview of Microsoft’s research on Cloud Intelligence/AIOps (AIOps), which innovates AI and machine learning (ML) technologies to help design, build, and operate complex cloud…
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