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
|2 Session 1: Plenary||3-8|
|3 Session 2: Machine Learning from Image, Video, and Map Data||9-13|
|4 Session 3: Machine Learning from Natural Languages||14-19|
|5 Session 4: Learning from Multi-Source Data||20-22|
|6 Session 5: Learning from Noisy, Adversarial Inputs||23-25|
|7 Session 6: Learning from Social Media||26-27|
|8 Session 7: Humans and Machines Working Together with Big Data||28-30|
|9 Session 8: Use of Machine Learning for Privacy Ethics||31-33|
|10 Session 9: Evaluation of Machine-Generated Products||34-36|
|11 Session 10: Capability Technology Matrix||37-42|
|Appendix A: Biographical Sketches of Workshop Planning Committee||45-47|
|Appendix B: Workshop Agenda||48-51|
|Appendix C: Workshop Statement of Task||52-52|
|Appendix D: Capability Technology Tables||53-58|
|Appendix E: Acronyms||59-60|
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