Human-Computer Interaction
项目
The Teachable AI Experiences team (Tai X) aims to innovate teachable AI systems that allow people near or far from the norm to create meaningful personalized experiences for themselves. What we ALL have in common is that we are unique.…
Project Tokyo aims to understand how to create a visual agent technology that is both useful and usable in the real world by focusing on how AI technology can help to augment people’s own capabilities.
Project Zanzibar is a flexible, portable mat that can sense and track physical objects, identify what they are, and allow you to interact through multi-touch and hover gestures.
SeeingVR is a research toolkit for making virtual reality more accessible to people with low vision. You can read our research paper at aka.ms/seeingvrpaper, watch our video at aka.ms/seeingvrvideo, and access the open source code at aka.ms/seeingvropensource.
Caption Crawler is a plug-in for the Edge and Chrome web browsers that provides additional information about images for screen reader users. Many images on the web lack captions (i.e., alt text). When a webpage loads, Caption Crawler identifies images…
成立:
Microsoft Research has long pioneered new techniques in digital pen input, including particularly its combination with multi-touch in a manner that reflects how people naturally use their hands. For example, in the real world people hold (“touch”) a document with their non-preferred…
成立:
WritLarge is a prototype system from Microsoft Research for the 84″ Microsoft Surface Hub, a large electronic whiteboard supporting both pen and multi-touch input. WritLarge allows creators to unleash the latent expressive power of ink in a compelling manner. Using multi-touch,…
成立:
We became fascinated with mobile livestreaming when Meerkat and Periscope were released in early 2015. We wanted to understand how people used these services to engage with real world events. We found that independent broadcasters would cover an event with…
成立:
We explore ways to help people easily build machine learning models by leveraging information visualization. We aim to effectively support understanding and debugging of machine learning models.