Session I. Plenary -- Session 2. Machine learning from image, video, and map data -- Session 3. Machine learning from natural languages -- Session 4. Learning from multi-source data -- Session 5. Learning from noisy, adversarial inputs -- Session 6. Learning from social media -- Session 7. Humans and machines working together with big data -- Session 8. Use of machine learning for privacy ethics -- Session 9. Evaluation of machine-generated products -- Session 10. Capability technology matrix.
Summary:
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.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.