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Challenges in Machine Generation of Analytic Products from Multi-Source Data - Intelligence Community Studies Board - Bog - National Academies Press -

Challenges in Machine Generation of Analytic Products from Multi-Source Data - Intelligence Community Studies Board - Bog - National Academies Press -

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 ContentsFront Matter1 Introduction2 Session 1: Plenary3 Session 2: Machine Learning from Image, Video, and Map Data4 Session 3: Machine Learning from Natural Languages5 Session 4: Learning from Multi-Source Data6 Session 5: Learning from Noisy, Adversarial Inputs7 Session 6: Learning from Social Media8 Session 7: Humans and Machines Working Together with Big Data9 Session 8: Use of Machine Learning for Privacy Ethics10 Session 9: Evaluation of Machine-Generated Products11 Session 10: Capability Technology MatrixAppendixesAppendix A: Biographical Sketches of Workshop Planning CommitteeAppendix B: Workshop AgendaAppendix C: Workshop Statement of TaskAppendix D: Capability Technology TablesAppendix E: Acronyms

DKK 344.00
1

Intelligent Human-Machine Collaboration - National Research Council - Bog - National Academies Press - Plusbog.dk

Intelligent Human-Machine Collaboration - National Research Council - Bog - National Academies Press - Plusbog.dk

On June 12-14, 2012, the Board on Global Science and Technology held an international, multidisciplinary workshop in Washington, D.C., to explore the challenges and advances in intelligent human-machine collaboration (IH-MC), particularly as it applies to unstructured environments. This workshop convened researchers from a range of science and engineering disciplines, including robotics, human-robot and human-machine interaction, software agents and multi-agentsystems, cognitive sciences, and human-machine teamwork. Participants were drawn from research organizations in Australia, China, Germany, Israel, Italy, Japan, the Netherlands, the United Arab Emirates, the United Kingdom, and the United States. The first day of the workshop participants worked to determine how advances in IH-MC over the next two to three years could be applied solving a variety of different real-world scenarios in dynamic unstructured environments, ranging from managing a natural disaster to improving small-lot agile manufacturing. On the second day of the workshop, participants organized into small groups for a deeper exploration of research topics that had arisen, discussion of common challenges, hoped-for breakthroughs, and the national, transnational, and global context in which this research occurs. Day three of the workshop consisted of small groups focusing on longer term research deliverables, as well as identifying challenges and opportunities from different disciplinary and cultural perspectives. In addition, ten participants gave presentations on their research, ranging from human-robot communication, to disaster response robots, to human-in-the-loop control of robot systems. Intelligent Human-Machine Collaboration: Summary of a Workshop describes in detail the discussions and happenings of the three day workshop. Table of ContentsFront Matter1 Introduction2 Scenario Exercises3 Human-Machine Teamwork Panels4 Common Challenges and Breakthroughs5 Global and Transnational Issues6 Revisiting the ScenariosAppendix A: Workshop ParticipantsAppendix B: Workshop AgendaAppendix C: Presentation Abstracts

DKK 208.00
3