Challenges in Machine Generation of Analytic Products from Multi-Source Data - Proceedings of a Workshop (Paperback)

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

Imprint:	National Academies Press
Country of origin:	United States
Release date:	December 2017
First published:	2017
Authors:	National Academies of Sciences, Engineering, and Medicine • Division on Engineering and Physical Sciences • Intelligence Community Studies Board
Editors:	Linda Casola
Dimensions:	279 x 216mm (L x W)
Format:	Paperback
Pages:	70
ISBN-13:	978-0-309-46573-1
Categories:	Books > Reference & Interdisciplinary > Communication studies > Research methods Books > Science & Mathematics > Mathematics > General
LSN:	0-309-46573-7
Barcode:	9780309465731

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