For the future of the planet, there are few research subjects more important than the global supplies of food, water and energy. To comprehensively study, understand and inform policy around these complex systems, the next generation of researchers in the physical, social and biological sciences will need fluency with data analysis methods that transverse traditional academic boundaries. A new interdisciplinary curriculum will train graduate students from geosciences, economics, computer science, public policy and other programs in computational and data science techniques critical for modern science. With a $3 million award from the National Science Foundation, the new research traineeship grant will combine expertise from across UChicago and Argonne National Laboratory in computing, statistics, social science, climate and agriculture. "This program will equip graduate students with the tools needed to advance the study of issues related to food, energy and water," said Elisabeth Moyer, associate professor of atmospheric science in the Department of the Geophysical Sciences.
The titles of the eight symposia were as follows: AI for Gerontechnology (FS-12-01), Artificial Intelligence of Humor (FS-12-02), Discovery Informatics: The Role of AI Research in Innovating Scientific Processes (FS-12-03), Human Control of Bio-Inspired Swarms (FS-12-04), Information Retrieval and Knowledge Discovery in Biomedical Text (FS-12-05), Machine Aggregation of Human Judgment (FS-12-06), Robots Learning Interactively from Human Teachers (FS-12- 07), and Social Networks and Social Contagion (FS-12-08). The highlights of each symposium are presented in this report. The development of user-centered technologies that assist older adults to live independently and also reduce the burden on caregivers is gaining more attention due to increasing healthcare costs and the aging population. AI is central to these technologies as it deals with the process of transforming raw sensor data into human-interpretable abstractions, innovating new human computer interfaces, as well as planning and reasoning. The symposium provided an intimate setting for researchers from the disciplines of computer science, engineering, nursing, psychology, cognitive science, and health informatics to take stock of the state of the art, highlighting successes and failures, while discussing new problems and opportunities.
Dogan, Rezarta Islamaj (National Library of Medicine) | Gil, Yolanda (University of Southern California) | Hirsh, Haym (Rutgers University) | Krishnan, Narayanan C. (Washington State University) | Lewis, Michael (University of Pittsburgh) | Mericli, Cetin (Carnegie Mellon University) | Rashidi, Parisa (Northwestern University) | Raskin, Victor (Purdue University) | Swarup, Samarth (Virginia Institute of Technology) | Sun, Wei (George Mason University) | Taylor, Julia M. (National Library of Medicine) | Yeganova, Lana
The Association for the Advancement of Artificial Intelligence was pleased to present the 2012 Fall Symposium Series, held Friday through Sunday, November 2–4, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia were as follows: AI for Gerontechnology (FS-12-01), Artificial Intelligence of Humor (FS-12-02), Discovery Informatics: The Role of AI Research in Innovating Scientific Processes (FS-12-03), Human Control of Bio-Inspired Swarms (FS-12-04), Information Retrieval and Knowledge Discovery in Biomedical Text (FS-12-05), Machine Aggregation of Human Judgment (FS-12-06), Robots Learning Interactively from Human Teachers (FS-12-07), and Social Networks and Social Contagion (FS-12-08). The highlights of each symposium are presented in this report.
Learning incorporates a broad range of complex procedures. Machine learning (ML) is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g.
Artificial Intelligence researchers have long sought to understand and replicate processes of scientific discovery. This article discusses Discovery Informatics as an emerging area of research that builds on that tradition and applies principles of intelligent computing and information systems to understand, automate, improve, and innovate processes of scientific discovery.