Challenges in Machine Generation of Analytic Products from Multi-Source Data: Proceedings of a Workshop
English
By (author): and Medicine Division on Engineering and Physical Sciences Engineering Intelligence Community Studies Board National Academies of Sciences
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