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02731aam a2200349Ii 4500 001 D9CD24AA370411E887D7D95B97128E48 003 SILO 005 20180403010230 008 171124t20172017dcua b 100 0 eng d 020 $a 9780309465731 020 $a 0309465737 035 $a (OCoLC)1012807602 040 $a YDX $b eng $e rda $c YDX $d NRC $d YDX $d SOI $d IXA $d OCLCF $d IWA $d SILO 050 4 $a QA276.4 C477x 2017 245 00 $a Challenges in machine generation of analytic products from multi-source data : $b proceedings of a workshop / $c Linda Casola, rapporteur. 264 1 $a Washington, DC : $b National Academies Press, $c [2017] 300 $a x, 59 pages : $b illustrations (some color) ; $c 28 cm 504 $a Includes bibliographical references. 520 $a 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. 505 0 $a 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. 650 0 $a Mathematical statistics $x Data processing $v Congresses. 650 0 $a Statistics $v Congresses. 650 0 $a Social sciences $x Statistical methods $v Congresses. 776 08 $i Online version : $t Challenges in Machine Generation of Analytic Products from Multi-Source Data. $d [S.l.] : [s.n.], 2017 $z 9780309465731 $w (OCoLC)1016344777 700 1 $a Casola, Linda, $e rapporteur 710 2 $a National Academies of Sciences, Engineering, and Medicine (U.S.), $b Intelligence Community Studies Board. 710 2 $a National Academies of Sciences, Engineering, and Medicine (U.S.), $e host institution. 941 $a 2 952 $l OVUX522 $d 20191210021300.0 952 $l USUX851 $d 20180403020811.0 956 $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=D9CD24AA370411E887D7D95B97128E48 994 $a C0 $b IWAInitiate Another SILO Locator Search