Artificial Intelligence
Building intelligent machines that complement human reasoning and extend what people can do.
Vidéo
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
Probabilistic Q-learning is a promising approach balancing exploration and exploitation in reinforcement learning. However, existing implementations have significant limitations: they either fail to incorporate uncertainty about long-term consequences of actions or ignore fundamental dependencies in…
Vidéo
First TextWorld Problems Challenge – Getting Started
Enter the competition First TextWorld Problems: A Reinforcement and Language Learning Challenge at aka.ms/textworld-challenge. Follow the 6-step guide in the video to get started. See if you can build an agent that wins at a…
Groupe
Azure Research – Security and Privacy
Security and privacy are arguably the most significant concerns for enterprises and consumers using public cloud platforms. The confidential computing theme at Microsoft Research Cambridge has been conducting pioneering research in the design of systems…
Blog de recherche Microsoft
Advancing Human-Centered AI
We’re excited about the formal launch of the Stanford Institute for Human-Centered Artificial Intelligence (HAI) on Monday, March 18th. We resonate with Fei-Fei Li, John Etchemendy, and other leaders at Stanford on the promise of…
Vidéo
Getting Started with TextWorld
TextWorld is a text-based game generator and sandbox learning environment for reinforcement learning agents. Watch this video to get started with using TextWorld! Learn more about the competition at aka.ms/textworld-challenge
Publication
An updated duet model for passage re-ranking
Vidéo
Consumer Brain-Computer Interfaces: From Science Fiction to Reality
Current communication technologies often suffer from being impersonal and non-contextual. Using on-body sensors for implicit or explicit sensing to transfer information, sense context, cognitive states, attention or engagement, open the possibility to enhance technologies for…
Projet
Machine Learning for Systems and Tiered AIOps
Resource Central is a general ML and prediction-serving system deployed in Azure Compute. It trains ML models offline and uses them to produce predictions online.