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Challenges in Machine Generation of Analytic Products from Multi-Source Data Proceedings of a Workshop (2017) / Chapter Skim
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11 Session 10: Capability Technology Matrix
Pages 37-42

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From page 37...
... He also described structural health monitoring systems that use sensors and pattern recognition to predict future structural damage in bridges and buildings. To learn more about opportunities for machine learning, Shenoy encouraged participants to review the Quadrennial Technology Review, which contains detailed energy technology assessments.
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From page 38...
... Jonathan Fiscus, National Institute of Standards and Technology, asked if DOE can create data sets to facilitate the kind of research discussed in Shenoy's presentation; if so, they could be used for challenge competitions. Shenoy responded that DOE's Advanced Manufacturing Office recently held a workshop on "Artificial Intelligence Applied to Materials Discovery and Design," and the information gleaned from that workshop could inform a potential program to generate such data.
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From page 39...
... Voorhees concluded her talk with a description of the many different tasks and document types evaluated through the Text REtrieval Conference (TREC) .1 Anthony Hoogs, Kitware, Inc., noted that, historically, the computer vision community has not used the data sets from TRECVid because of the associated competition constraints and data restrictions, despite the fact that it began as the largest, most formalized evaluation in the community.
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From page 40...
... A list of recent grants includes topics in foundational machine learning -- for example, adversarial machine learning, deep reinforcement learning, learning structured prediction models, uncertainty representation, and personalizing from observational data. And a select list of active awards includes multi-modal research in computer vision and 2  The website for YALMIP is https://yalmip.github.io, accessed August 29, 2017.
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From page 41...
... CISE's "national priorities" include big data, cybersecurity, the National Robotics Initiative, understanding the brain, the National Strategic Computing Initiative, Smart Cities, Computer Science for All, and advanced wireless research. Donlon concluded his talk with a summary of what he discovered about NSF's funding in relation to the themes of the workshop: • Machine learning is everywhere and has been especially successful within a single modality on data-rich classification problems and where the problem space is sufficiently constrained.
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Key Terms

  • computer vision
  • artificial intelligence
  • big data
  • data analytics
  • multidisciplinary research

  • advanced manufacturing
  • renewable energy
  • manufacturing institutes
  • vision community
  • energy efficiency

  • shenoy described
  • information integration
  • social networks
  • ellen voorhees
  • institutes supported

  • james donlon
  • voorhees explained
  • program studies
  • robust intelligence
  • moving objects

  • manufacturing office
  • community evaluations
  • human language
  • active awards
  • shenoy added

  • analytics research
  • energy technology
  • machine cognition
  • create data
  • energy reduction


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