Artificial Intelligence
Building intelligent machines that complement human reasoning and extend what people can do.
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Introduction to ShowWhy, user interfaces for causal decision making
ShowWhy is a no-code user interface suite that empowers domain experts to become “decision scientists,” who can independently ask a causal question, develop causal estimates, and present and defend causal evidence to an audience of…
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Causal AI for Decision Making
Find out more about our project at: https://cm-edgetun.pages.dev/en-us/research/project/project_azua/
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Language for Learning and Interaction | Montréal
Building interactive language agents that make sense of the world’s information Current research being done at Microsoft Research Montréal can be explored on the Froggy team (opens in new tab) page. Building interactive language agents…
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Robust, adaptive, modular ML | Montreal
We aim at understanding the principles underpinning learning and generalization, to build reliable AI systems that can learn more efficiently from available data, intelligently gather additional relevant data, and quickly adapt to and reason about…
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Orchestrated Value Mapping
This repository hosts the code release for the paper “Orchestrated Value Mapping for Reinforcement Learning”, published at ICLR 2022. This work was done by Mehdi Fatemi (Microsoft Research) and Arash Tavakoli (Max Planck Institute for…
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Designing AI Systems with Steerable Long-Term Dynamics
The feedback that users provide through their choices (e.g. clicks, purchases) is one of the most common types of data readily available for training autonomous systems, and it is widely used in online platforms. However,…
