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Challenges in Machine Generation of Analytic Products from Multi-Source Data

Challenges in Machine Generation of Analytic Products from Multi-Source Data

Proceedings of a Workshop

  • Author: Casola, Linda
  • Publisher: National Academies Press
  • ISBN: 9780309465731
  • eISBN Pdf: 9780309465748
  • eISBN Epub: 9780309465762
  • Place of publication:  United States
  • Year of digital publication: 2017
  • Month: November
  • DDC: 510
  • Language: English

The Intelligence Community Studies Board of the National Academies of Sciences, Engineering, and Medicine convened a workshop on August 9-10, 2017 to examine challenges in machine generation of analytic products from multi-source data. Workshop speakers and participants discussed research challenges related to machine-based methods for generating analytic products and for automating the evaluation of these products, with special attention to learning from small data, using multi-source data, adversarial learning, and understanding the human-machine relationship. This publication summarizes the presentations and discussions from the workshop.

  • FrontMatter
  • Acknowledgment of Reviewers
  • Contents
  • 1 Introduction
  • 2 Session 1: Plenary
  • 3 Session 2: Machine Learning from Image, Video, and Map Data
  • 4 Session 3: Machine Learning from Natural Languages
  • 5 Session 4: Learning from Multi-Source Data
  • 6 Session 5: Learning from Noisy, Adversarial Inputs
  • 7 Session 6: Learning from Social Media
  • 8 Session 7: Humans and Machines Working Together with Big Data
  • 9 Session 8: Use of Machine Learning for Privacy Ethics
  • 10 Session 9: Evaluation of Machine-Generated Products
  • 11 Session 10: Capability Technology Matrix
  • Appendixes
  • Appendix A: Biographical Sketches of Workshop Planning Committee
  • Appendix B: Workshop Agenda
  • Appendix C: Workshop Statement of Task
  • Appendix D: Capability Technology Tables
  • Appendix E: Acronyms

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  • Loan duration: 21 days
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