Artificial Intelligence
Building intelligent machines that complement human reasoning and extend what people can do.
Publication
Toward Interactive Dictation
Publication
Towards Robust Ranker for Text Retrieval
Group
Real-world Evidence
Recent advances in generative AI such as large language models (LLMs) offer unprecedented “universal structuring” capabilities that can supercharge health information processing and unlock many high-value applications in real-world evidence and precision health.
Video
Multilingual Evaluation of Generative AI (MEGA)
Generative AI models have impressive performance on many Natural Language Processing tasks such as language understanding, reasoning and language generation. One of the most important questions that is being asked by the AI community today…
Video
The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding
In a real-world dialogue system, generated text must be truthful and informative while remaining fluent and adhering to a prescribed style. Satisfying these constraints simultaneously is difficult for the two predominant paradigms in language generation:…
Video
Privacy-Preserving Domain Adaptation of Semantic Parsers
Task-oriented dialogue systems often assist users with personal or confidential matters. For this reason, the developers of such a system are generally prohibited from observing actual usage. So how can they know where the system…
Microsoft Research Blog
Distributional Graphormer: Toward equilibrium distribution prediction for molecular systems
Distributional Graphormer, Microsoft’s new deep learning framework for predicting the equilibrium distribution of molecular structures, can generate realistic and diverse molecular structures with high efficiency and low cost.
Video
Towards Ad-Hoc Teamwork for Improved Player Experience | Sam Devlin
Towards Ad-Hoc Teamwork for Improved Player Experience ICARL Seminar Series – 2022 Winter Seminar by Sam Devlin Abstract: Collaborative multi-agent reinforcement learning research often makes two key assumptions: (1) we have control of all agents…
