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Challenges in Machine Generation of Analytic Products from Multi-Source Data - Proceedings of a Workshop (Paperback)
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Challenges in Machine Generation of Analytic Products from Multi-Source Data - Proceedings of a Workshop (Paperback)
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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.
Table of Contents Front Matter 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
General
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