Artificial Intelligence
Building intelligent machines that complement human reasoning and extend what people can do.
Video
Visual Recognition beyond Appearances, and its Robotic Applications
The goal of Computer Vision, as coined by Marr, is to develop algorithms to answer What are Where at When from visual appearance. The speaker, among others, recognizes the importance of studying underlying entities and…
Podcast
New Future of Work: Meeting and collaborating in a remote and hybrid world with Jaime Teevan and Abigail Sellen
In this episode of The New Future of Work series of the podcast, Chief Scientist Jaime Teevan and Abigail Sellen, Deputy Lab Director at Microsoft Research Cambridge in the United Kingdom, explore the dynamics of…
Tool
CausalCity
This is the official code repository to accompany the paper CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning. Here we provide Python code for generating and logging scenarios using the simulation environment as…
Video
Directions in ML: Structured Models for Automated Machine Learning
Automated machine learning (AutoML) seeks algorithmic methods for finding the best machine learning pipeline and hyperparameters to fit a new dataset. The complexity of this problem is astounding: viewed as an optimization problem, it entails…
Microsoft Research Blog
CausalCity: Introducing a high-fidelity simulation with agency for advancing causal reasoning in machine learning
The ability to reason about causality, and ask “what would happen if…?’’ is one property that sets human intelligence apart from artificial intelligence. Modern AI algorithms perform well on clearly defined pattern recognition tasks but…
