Computer-Aided Discovery: Toward Scientific Insight Generation with Machine Support

Victor Pankratius, Justin Li, Michael Gowanlock, David M. Blair, Cody Rude, Tom Herring, Frank Lind, Philip J. Erickson, Colin Lonsdale

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number7515118
Pages (from-to)3-10
Number of pages8
JournalIEEE Intelligent Systems
Volume31
Issue number4
DOIs
StatePublished - Jul 1 2016
Externally publishedYes

Keywords

  • big data
  • cloud computing
  • computer-aided discovery
  • data mining
  • discovery science
  • intelligent analytics
  • intelligent systems
  • machine learning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence

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