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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
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Challenges in
MACHINE GENERATION
of Analytic Products from
MULTI-SOURCE DATA

PROCEEDINGS OF A WORKSHOP

Linda Casola, Rapporteur

Intelligence Community Studies Board

Division on Engineering and Physical Sciences

images

THE NATIONAL ACADEMIES PRESS
Washington, DC
www.nap.edu

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
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This activity was supported by Contract No. 2014-14041100003-012 between the National Academy of Sciences and the Office of the Director of National Intelligence. Any opinions, findings, conclusions, or recommendations expressed in this publication do not necessarily reflect the views of any organization or agency that provided support for the project.

International Standard Book Number-13: 978-0-309-46573-1
International Standard Book Number-10: 0-309-46573-7
Digital Object Identifier: https://doi.org/10.17226/24900

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Suggested citation: National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of A Workshop. Washington, DC: The National Academies Press. doi: https://doi.org/10.17226/24900.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×

PLANNING COMMITTEE ON THE INTELLIGENCE MACHINE ANALYTICS WORKSHOP

RAMA CHELLAPPA, University of Maryland, College Park, Chair

THOMAS DIETTERICH, Oregon State University

ANTHONY HOOGS, Kitware, Inc.

JOHN E. KELLY III, NAE,1 International Business Machines Corporation

KATHLEEN McKEOWN, Columbia University

JOSEPH L. MUNDY, Vision Systems, Inc.

Staff

GEORGE COYLE, Senior Program Officer, Study Director

CHRIS JONES, Financial Officer

MARGUERITE SCHNEIDER, Administrative Coordinator

DIONNA ALI, Research Assistant

ADRIANNA HARGROVE, Senior Program Assistant/Financial Assistant

___________________

1 Member, National Academy of Engineering.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×

INTELLIGENCE COMMUNITY STUDIES BOARD

DONALD M. KERR, Independent Consultant, Chair

JULIE BRILL, Microsoft Corporation

FREDERICK CHANG, NAE,1 Southern Methodist University

TOMÁS DÍAZ DE LA RUBIA, Purdue University Discovery Park

ROBERT C. DYNES, NAS,2 University of California, San Diego

ROBERT FEIN, McLean Hospital/Harvard Medical School

MIRIAM JOHN, Independent Consultant

ANITA JONES, NAE, University of Virginia

ROBERT H. LATIFF, R. Latiff Associates

MARK LOWENTHAL, Johns Hopkins University

MICHAEL MARLETTA, NAS/NAM,3 University of California, Berkeley

L. ROGER MASON, JR., Noblis

ELIZABETH RINDSKOPF PARKER, State Bar of California

WILLIAM H. PRESS, NAS, University of Texas, Austin

DAVID A. RELMAN, NAM, Stanford University

Staff

ALAN SHAW, Director

ANDREW KREEGER, Program Officer

CHRIS JONES, Financial Officer

MARGUERITE SCHNEIDER, Administrative Coordinator

DIONNA ALI, Research Assistant

STEVEN DARBES, Research Assistant

ADRIANNA HARGROVE, Senior Program Assistant/Financial Assistant

___________________

1 Member, National Academy of Engineering.

2 Member, National Academy of Sciences.

3 Member, National Academy of Medicine.

Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×

Acknowledgment of Reviewers

This Proceedings of a Workshop was reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise. The purpose of this independent review is to provide candid and critical comments that will assist the National Academies of Sciences, Engineering, and Medicine in making each published proceedings as sound as possible and to ensure that it meets the institutional standards for quality, objectivity, evidence, and responsiveness to the charge. The review comments and draft manuscript remain confidential to protect the integrity of the process.

We thank the following individuals for their review of this proceedings:

Anthony Hoogs, Kitware, Inc.,

Kathleen McKeown, Columbia University,

John Montgomery, NAE,1 Naval Research Laboratory (retired),

Noah A. Smith, University of Washington, and

Peter Weinberger, Google, Inc.

Although the reviewers listed above provided many constructive comments and suggestions, they were not asked to endorse the content of the proceedings nor did they see the final draft before its release. The review of this proceedings was overseen by Edward W. Felten, NAE, Princeton University, who was responsible for making certain that an independent examination of this proceedings was carried out in accordance with standards of the National Academies and that all review comments were carefully considered. Responsibility for the final content rests entirely with the rapporteur and the National Academies.

___________________

1 Member, National Academy of Engineering.

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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×
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Page viii Cite
Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
×
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Suggested Citation:"Front Matter." National Academies of Sciences, Engineering, and Medicine. 2017. Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi: 10.17226/24900.
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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.

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