Challenges in machine generation of analytic products from multi-source data : proceedings of a workshop / Linda Casola, rapporteur.

Format:

Book

Conference/Event

Contributor:

Casola, Linda, rapporteur.

Intelligence Community Studies Board.

Division on Engineering and Physical Sciences.

Conference Name:

Challenges in machine generation of analytic products from multi-source data (Workshop) (2017 : Washington, D.C)

Language:

English

Subjects (All):

Manufactures--Congresses.

Manufactures.

Physical Description:

1 online resource (60 pages) : illustrations (some color), tables

Edition:

1st ed.

Place of Publication:

Washington, District of Columbia : The National Academies Press, 2017.

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.

Contents:

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.

Notes:

Description based on online resource; title from PDF title page (EBC, viewed December 8, 2017).

ISBN:

0-309-46576-1

0-309-46574-5