New Directions

July/August 2016

IEEE Intelligent Systems magazine cover

The process of scientific discovery is traditionally assumed to be entirely executed by humans. This article highlights how increasing data volumes and human cognitive limits are challenging this traditional assumption. Relevant examples are found in observational astronomy and geoscience, disciplines that are undergoing transformation due to growing networks of space-based and ground-based sensors. The authors outline how intelligent systems for computer-aided discovery can routinely complement and integrate human scientists in the insight generation loop in scalable ways for next-generation science. The pragmatics of model-based computer-aided discovery systems go beyond feature detection in empirical data to answer fundamental questions, such as how empirical detections fit into hypothesized models and model variants to ease the scientist's work of placing large ensembles of detections into a theoretical context. The authors demonstrate successful applications of this paradigm in several areas, including ionospheric studies, volcanics, astronomy, and planetary landing site identification for spacecraft and robotic missions. More »

About IEEE Intelligent Systems

IEEE Intelligent Systems' peer-reviewed, cutting-edge articles cover the theory and application of systems that perceive, reason, learn, and act intelligently. It serves many different professionals in a broad range of fields.

Articles from IEEE Intelligent Systems

UT Austin Villa: Project-Driven Research in AI and Robotics

UT Austin Villa: Project-Driven Research in AI and Robotics

UT Austin Villa is a robot soccer team that has won several RoboCup soccer championships since 2003 and has inspired research contributions in robotics and artificial intelligence. Read full article »

Prediction Using Propagation: From Flu Trends to Cybersecurity

Prediction Using Propagation: From Flu Trends to Cybersecurity

Propagation-based concepts for predictive analytics pair epidemiological models with statistical topic models to improve prediction of flu trends and malware attacks. Read full article »

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