Investment in research is at an all-time high, yet the rate of scientific breakthroughs isn't setting any records. To resolve this quandary, scientists are turning to artificial intelligence and crowdsourcing for help in identifying a key inspiration for innovation — the perfect analogy.
The world is full of physical interfaces that are inaccessible to blind people, from microwaves and information kiosks to thermostats and checkout terminals. Blind people cannot independently use such devices without at least first learning their layout, and usually only after labeling them with sighted assistance.
Conversational agents such as Siri, Alexa and Cortana are great at giving you the weather, but flummoxed when asked for unusual information or follow-up questions. By adding humans to the loop, Carnegie Mellon University researchers have created a conversational agent that is tough to stump.
Over 3,000 of the world’s top researchers, scientists, and designers are traveling to Montréal this week for CHI 2018, the ACM CHI Conference on Human Factors in Computing Systems. The premier international conference of Human-Computer Interaction will take place from April 21-26, 2018.
Think about the number of electronic appliances you encounter on a daily basis – using a microwave, adjusting the thermostat in your home, or buying a snack from the office vending machine. Now, imagine trying to operate any of these if you were blind. Impossible? Anhong Guo, Ph.D. student in the Human-Computer Interaction Institute (HCII), is turning this into a reality with the development of an interactive screen reader called VizLens.
Researchers from the Human-Computer Interaction Institute are working on a system they call Knowledge Accelerator. A system that uses a machine-learning program to sort and organize information uncovered by individuals who are focused on just a small segment of the larger project. The computer serves as a guide for individuals working collaboratively on a large project, without requiring any one person to have a full picture.
This project investigates how social computing systems can optimize real-time creative collaboration, whether in teams or with crowds. We are actively developing a web-based social computing system for brainstorming that facilitates managing the interactions of brainstormers with each other so that the solution space is explored with the right combination of breadth and depth.
Social science research often relies on coding of video or other data for subjective events. This can be very time consuming and labor intensive for a single individual. This project aims to allow researchers and other analysts to quickly and easily interact with large sets of video data to identify and mark subjective events by crowdsourcing the interpretation process. We have shown it is possible to make quick calls to crowd workers, then aggregate their solutions.
This research investigates the way that teams and communities collaborate online, with two aims: to develop new technologies to enhance collaboration and to improve our theoretical understanding of small-group dynamics. For example, one online community under study is MathOverflow, which has successfully adapted existing technology, in this case a question answering platform, to engage in problem solving in mathematics on a large scale.
