RASCAL is an untethered robot with a modular design, allowing it to move flexibly along and between evenly spaced storage shelves. Discover how it can address the availability and scalability challenges of existing automated storage and retrieval systems.
Property prediction for materials under realistic conditions has been a long-standing challenge within the digital transformation of materials design. MatterSim investigates atomic interactions from the very fundamental principles of quantum mechanics.
Autoscaling can optimize cloud resource usage and costs by adjusting to demand. VASIM shows that simplifying testing and refinement of autoscaling algorithms can enable rapid development and evaluation of more efficient & cost-effective autoscaling strategies.
LLMs rely on memory-intensive mechanisms like the key-value (KV) cache to store and quickly retrieve data. FastGen optimizes KV cache usage, reducing LLM memory demands by up to 50% while maintaining performance.
LoftQ boosts LLM efficiency by streamlining the fine-tuning process, reducing computational demands while preserving high performance. Innovations like this can help make AI technology more energy-efficient.
In this edition: Can LLMs transform natural language into formal method postconditions; Semantically aligned question + code generation for automated insight generation; Explaining CLIP performance disparities on blind/low vision data; plus recent news.
From AI and deep learning to innovations in infrastructure, researchers from Microsoft are bridging the gap between architecture, programming languages, and operating systems to advance the state of the art at ASPLOS 2024.
Microsoft recently developed and released the Situated Interactive Guidance, Monitoring, and Assistance (SIGMA) system, an open-source research platform, to enable research and innovation at the intersection of mixed reality and AI.
SAMMO optimizes prompts for LLMs by leveraging their structure to guide optimization. This minimizes the time and effort needed to find performant prompts on a variety of tasks.
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:
Microsoft at NDSI 2024: Discoveries and implementations in networked systems Topics range from 5G, space, datacenters, and wide-area networking to applications in artificial intelligence, security, video conferencing, and gaming. Learn more about the discoveries and advances we're making with networked systems.
In this issue: New research helps COMET embrace African languages; FeatUp improves deep features, a computer vision research cornerstone; LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error; Benchmarking LLMs across languages and more.
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