{"id":11631,"date":"2019-09-24T11:20:14","date_gmt":"2019-09-24T15:20:14","guid":{"rendered":"https:\/\/carleton.ca\/cognitivescience\/?p=11631"},"modified":"2019-09-24T11:20:29","modified_gmt":"2019-09-24T15:20:29","slug":"ics-colloquium","status":"publish","type":"post","link":"https:\/\/carleton.ca\/cognitivescience\/2019\/ics-colloquium\/","title":{"rendered":"ICS Colloquium: Explanation and Artificial Neural Networks"},"content":{"rendered":"<p><strong>Date:<\/strong> October 02, 2019 3:00pm-4:30pm<\/p>\n<p><strong>Location:<\/strong> Dunton Tower: Room 2203<\/p>\n<p><strong>\u00a0Speaker:\u00a0<\/strong><strong>Andre Vellino<\/strong><\/p>\n<p><strong>\u00a0Title: <\/strong>Explanation and Artificial Neural Networks<\/p>\n<p><strong>Abstract:<\/strong><\/p>\n<p>As machine, learning systems become increasingly better than humans at making optimal choices, our ability and willingness to trust these choices will depend on their ability to also provide\u00a0explanations\u00a0for them. Explainable AI (XAI) aims to make machine decision making processes understandable to humans which will be a critical element in instruments on which we will increasingly depend \u2013 self-driving cars, medical diagnostic tools, fake news filtering etc.<\/p>\n<p>Using AlphaGo\u2019s victories against Go champion Lee Sedol in 2016 I will explore the question of what counts as an explanation for an ML-driven decision and what the feasible strategies are for synthesing such explanations.<\/p>\n<p><strong>Speaker\u2019s profile:<\/strong><\/p>\n<p><strong>\u00a0<\/strong>Andr\u00e9 Vellino is an Associate Professor in Information Studies at the University of Ottawa and an Adjunct Research Professor at the Institute of Cognitive Science at Carleton University. He spent many years working in AI in the private sector and government.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Date: October 02, 2019 3:00pm-4:30pm Location: Dunton Tower: Room 2203 \u00a0Speaker:\u00a0Andre Vellino \u00a0Title: Explanation and Artificial Neural Networks Abstract: As machine, learning systems become increasingly better than humans at making optimal choices, our ability and willingness to trust these choices will depend on their ability to also provide\u00a0explanations\u00a0for them. Explainable AI (XAI) aims to make [&hellip;]<\/p>\n","protected":false},"author":31,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[1],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>ICS Colloquium: Explanation and Artificial Neural 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