Artificial Intelligence
Pursuing computing advances to create intelligent machines that complement human reasoning to augment and enrich our experience and competencies.
Tool
TamGen
TamGen is a transformer-based chemical language model for developing target-specific drug compounds. Research shows that TamGen can also optimize existing molecules by designing target-aware molecule fragments, potentially enabling the discovery of novel compounds that build…
Video
UW GIX Launch Project: GenAI Evaluation CoPilot
Thank you to @AdvityaGemawat for being in this video about our Team GenAI’s Launch Project, the University of Washington’s Master of Science in Technology Innovation Capstone Project, sponsored by Microsoft Azure. This video will talk…
Microsoft Research Blog
Accelerating drug discovery with TamGen: A generative AI approach to target-aware molecule generation
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…
Microsoft Research Blog
LazyGraphRAG: Setting a new standard for quality and cost
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.
Dataset Source Code
MMLU-CF
Paper: “MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark”
Tool
RAD-DINO model
RAD-DINO is a vision transformer model trained to encode chest X-rays using the self-supervised learning method DINOv2. RAD-DINO is described in detail in RAD-DINO: Exploring Scalable Medical Image Encoders Beyond Text Supervision (F. Pérez-García, H. Sharma, S.…
Tool
MAIRA-2 model
MAIRA-2 is a multimodal transformer designed for the generation of grounded or non-grounded radiology reports from chest X-rays. It is described in more detail in MAIRA-2: Grounded Radiology Report Generation (S. Bannur, K. Bouzid et al.,…