{"id":31701,"date":"2016-11-05T03:50:30","date_gmt":"2016-11-05T10:50:30","guid":{"rendered":"http:\/\/lifeboat.com\/blog\/2016\/11\/technique-reveals-the-basis-for-machine-learning-systems-decisions"},"modified":"2017-06-04T09:16:35","modified_gmt":"2017-06-04T16:16:35","slug":"technique-reveals-the-basis-for-machine-learning-systems-decisions","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2016\/11\/technique-reveals-the-basis-for-machine-learning-systems-decisions","title":{"rendered":"Technique reveals the basis for machine-learning systems\u2019 decisions"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/technique-reveals-the-basis-for-machine-learning-systems-decisions.jpg\"><\/a><\/p>\n<p>In recent years, the best-performing systems in artificial-intelligence research have come courtesy of neural networks, which look for patterns in training data that yield useful predictions or classifications. A neural net might, for instance, be trained to recognize certain objects in digital images or to infer the topics of texts.<\/p>\n<p>But neural nets are black boxes. After training, a network may be very good at classifying data, but even its creators will have no idea why. With visual data, it\u2019s sometimes possible to automate experiments that determine which visual features a neural net is responding to. But text-processing systems tend to be more opaque.<\/p>\n<p>At the Association for Computational Linguistics\u2019 Conference on Empirical Methods in Natural Language Processing, researchers from MIT\u2019s Computer Science and Artificial Intelligence Laboratory (CSAIL) will present a new way to train <a href=\"https:\/\/techxplore.com\/tags\/neural+networks\/\" rel=\"tag\" class=\"\">neural networks<\/a> so that they provide not only predictions and classifications but rationales for their decisions.<\/p>\n<p><!-- Link: <a href=\"https:\/\/techxplore.com\/news\/2016-10-technique-reveals-basis-machine-learning-decisions.html\">https:\/\/techxplore.com\/news\/2016&#45;10-technique-reveals-ba...sions.html<\/a> --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In recent years, the best-performing systems in artificial-intelligence research have come courtesy of neural networks, which look for patterns in training data that yield useful predictions or classifications. A neural net might, for instance, be trained to recognize certain objects in digital images or to infer the topics of texts. But neural nets are black [\u2026]<\/p>\n","protected":false},"author":367,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-31701","post","type-post","status-publish","format-standard","hentry","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/31701","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/367"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=31701"}],"version-history":[{"count":2,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/31701\/revisions"}],"predecessor-version":[{"id":59850,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/31701\/revisions\/59850"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=31701"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=31701"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=31701"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}