人工智能“天文学家”能否帮助人类理解宇宙？
编者按：在广袤无垠的宇宙中，存在着无数类型各异的天体。借助现代技术，人们能够获取这些天体的丰富信息，包括形状、光谱、坐标、红移、引力透镜、爆发时变等大量数据，进而探究宇宙起源与演变的奥秘。但传统科学技术已难以应对海量数据的处理需求，这限制了天文学研究的进一步发展。 为了帮助天文学家分析遥远星系历经百亿年旅程到达太空望远镜的测光数据，微软亚洲研究院联合清华大学天文系以及俄亥俄州立大学（The Ohi...
Large language models (LLMs), led by GPT and followed by numerous other models, have demonstrated their strong capabilities in many areas, from language processing such as text generation and document summarization, to coding, reasoning, and planning tasks, etc. LLMs have…
Awards | SIGMOBILE
Jie Xiong receives SIGMOBILE Test-of-Time Paper Award
ArrayTrack: A Fine-Grained Indoor Location System (USENIX NSDI, 2013) introduced a pioneering approach to RF-based indoor localization and wireless sensing by leveraging phase-array-based signal processing. Moving beyond traditional wireless signal strength measurements, it utilized multiple antennas and advocated for the…
AI is advancing at an extraordinary pace, driven by groundbreaking innovations in computer systems. As we rapidly move towards an era centered around AI computing, these systems have significantly benefited from advancements in AI technology. At this pivotal moment, Microsoft…
加速药物发现：基于生成式AI的靶点感知分子生成器TamGen
全球健康药物研发中心 （GHDDI） 和 微软研究院科学智能中心（Microsoft Research AI for Science）团队通过开发 TamGen 在研究结核病方面取得了重要突破。TamGen 是一款基于 Transformer 模型的开源化学语言模型，用于开发特定靶点的药物化合物。联合团队通过密切合作成功确定了几种有前景的结核病蛋白酶抑制剂，其中最有效的化合物表现出显著的生物活性。...
Advances in run-time strategies for next-generation foundation models
| Eric Horvitz, Harsha Nori, and Naoto Usuyama
Discover the most effective run-time strategies on the OpenAI o1-preview model, improving accuracy in medical language tasks.
Accelerating drug discovery with TamGen: A generative AI approach to target-aware molecule generation
| Yingce Xia, Pan Deng, Shufang Xie, Haiguang Liu, and Tao Qin
TamGen uses generative AI to design new drug candidate compounds to treat TB, going beyond traditional methods by generating novel chemical structures. Learn how a collaboration with the Global Health Drug Discovery Institute is making this possible.
LazyGraphRAG: Setting a new standard for quality and cost
| Darren Edge, Ha Trinh, and Jonathan Larson
Introducing a new approach to graph-enabled RAG. LazyGraphRAG needs no prior summarization of source data, avoiding prohibitive up-front indexing costs. It’s inherently scalable in cost and quality across multiple methods and search mechanisms.