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