RASCAL: Novel robotics for scalable and highly available automated storage and retrieval
| Richard Black, Marco Caballero, Andromachi Chatzieleftheriou, Ant Rowstron, David Sweeney, and Hugh Williams
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.
Enhanced autoscaling with VASIM: Vertical Autoscaling Simulator Toolkit
| Anna Pavlenko, Karla Saur, Yiwen Zhu, Brian Kroth, Joyce Cahoon, and Jesús Camacho Rodríguez
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.
MatterSim: A deep-learning model for materials under real-world conditions
| Han Yang, Jielan Li, Hongxia Hao, and Ziheng Lu
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.
In the news | Economist
Today’s AI models are impressive. Teams of them will be formidable
On may 13th Openai unveiled its latest model, gpt-4o. Mira Murati, the company’s chief technology officer, called it the “future of interaction between ourselves and the machines”, because users will be able to speak to the model, which will talk…
编者按：欢迎阅读“科研上新”栏目！“科研上新”汇聚了微软亚洲研究院最新的创新成果与科研动态。在这里，你可以快速浏览研究院的亮点资讯，保持对前沿领域的敏锐嗅觉，同时也能找到先进实用的开源工具。 本周，全球最负盛名的人工智能盛会之一 ICLR 大会将在奥地利维也纳举办。所以，今天的“科研上新”将为大家带来多篇微软亚洲研究院在 ICLR 2024 上的精选论文解读，涉及领域涵盖深度强化学习、多模态语言模...
LLM profiling guides KV cache optimization
| Liyuan Liu and Jianfeng Gao
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: Reimagining LLM fine-tuning with smarter initialization
| Nikos Karampatziakis, Chen Liang, Weizhu Chen, Yixiao Li, Yifan Yu, and Tuo Zhao
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 the news | Politico
Microsoft goes from bad boy to top cop in the age of AI
Alongside efforts to use artificial intelligence to find new cures for cancer and combat climate change, the Microsoft AI for Good's small engineering team has another job: figuring out how to detect AI-powered deepfake videos, audio clips and images bombarding…