|Presentations from Women in Computing: Jaime Teevan on Social Media Question Asking
Hear from researchers who are using computer science to solve some of the world’s most vexing problems and get insight into their current projects.
|Plenary 2: The Mathematics of Causal Inference: with Reflections on Machine Learning
The development of graphical models and the logic of counterfactuals have had a marked effect on the way scientists treat problems involving cause-effect relationships. Practical problems requiring causal information, which long were regarded as either metaphysical or unmanageable can now be solved using elementary mathematics. Moreover, problems that were thought to be purely statistical, are beginning to benefit from analyzing their causal roots.
|Plenary 3: Machine Learning: The 6th Wave of Computing
Data is accumulating at such a rate that there are no longer enough qualified humans to analyse it. Machine learning is needed to make data useful in many sectors which are drowning in it. Examples abound from healthcare, genomics, oil exploration, marketing etc. There have been 5 distinct waves of computing which all had the human at the centre of the industry. The internet of things will change this. Most communication will be between machines. To make them useful to us again they will need machine learning. This puts machine learning at the centre of the next 6th wave of computing.
|Latin American Researchers Use Data to Raise Awareness, Protect Species
Currently, endangered species in Latin America are insufficiently studied compared to North America and Europe. Researchers at Microsoft Research, LACCIR Virtual Institute and Pontifical Catholic University of Chile are collaborating to develop better tools that provide a fresh approach for researchers and citizen scientists to map the distribution of endangered wildlife.
|Adaptive Machine Learning for Real-Time Streaming
Direct processing of real-time data can provide a crucial edge in the software-and-services industry. Combining such processing with machine learning can provide a reasoning flow and enable runtime updates of the machine-learning model. Customer scenarios in manufacturing and IT services will benefit.
|From Wet to Dry: How Machine Learning and Big Data Are Changing the Face of Biological Sciences
Until recently, the wet lab has been a crucial component of every biologist. Today’s advances in the production of massive amounts of data and the creation of machine-learning algorithms for processing that data are changing the face of biological science--making it possible to do real science without a wet lab. David Heckerman shares several examples of how this transformation in the area of genomics is changing the pace of scientific breakthroughs.
|Profiles on Women in Computing: Alicia Edelman Pelton, Program Manager
Learn about the many great women in computing careers at Microsoft. Meet researchers who are using computer science to solve some of the world’s most vexing problems or technologists who are creating the next wave of paradigm-shifting products. Discover what motivates these pioneering women and acquire insights into their current projects.
In this video, you’ll meet Alicia Edelman Pelton, program manager at Microsoft Research.
|Making Smooth Topical Connections on Touch Devices
By representing a collection of text, image, or other documents as a grid of keywords of various font sizes indicating the words’ weights, the documents’ relatedness is revealed. Smooth thematic shifts become evident, connecting distant topics and guiding the user’s attention.