{"id":9723,"date":"2022-02-04T17:34:47","date_gmt":"2022-02-04T16:34:47","guid":{"rendered":"https:\/\/www.go-fair.org\/?p=9723"},"modified":"2022-03-04T10:15:43","modified_gmt":"2022-03-04T09:15:43","slug":"reproducibility-in-ai-and-what-computing-professionals-should-know-for-supporting-researchers","status":"publish","type":"post","link":"https:\/\/www.go-fair.org\/2022\/02\/04\/reproducibility-in-ai-and-what-computing-professionals-should-know-for-supporting-researchers\/","title":{"rendered":"Reproducibility in AI, and What Computing Professionals Should Know for Supporting Researchers"},"content":{"rendered":"\n<p><em>This article was written by Kevin Coakley and Alexandra Andreiu. It was published on 28 January 2022 on the <a href=\"https:\/\/gofair.us\/news\/2022-01-28-reproducibility-in-ai.html\" target=\"_blank\" rel=\"noreferrer noopener\">GO FAIR US website<\/a>.<\/em><\/p>\n\n\n\n<p>The \u201cr\u201d in FAIR\u2014 which stands for reusable\u2014 has always been confused with reproducibility. Although the two have different meanings and usable definitions, they are indeed closely related. Reproducibility goes beyond the ability to reuse data, software, or other research artifacts. It describes a core tenet of science &#8211; to be able to reproduce one\u2019s research to validate scientific findings. This has been an area of challenge for data-driven research as published research often omits important methods needed for recreating an experiment, and access to the data or compute needed to process the data is often missing, as well. GO FAIR is especially concerned with making data machines actionable, especially as a dimension of AI-readiness. Reproducibility becomes that much more complex in the realm of machine learning and deep learning. In his talk titled \u201cReproducibility in AI, and What Computing Professionals Should Know for Supporting Researchers\u201d, Kevin Coakley discusses this challenge. Kevin Coakley is a Senior Integration Engineer at the San Diego Supercomputer Center whose research focus is on reproducibility and AI.<\/p>\n\n\n\n<div class=\"wp-block-image is-style-default\"><figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"710\" height=\"152\" src=\"https:\/\/www.go-fair.org\/wp-content\/uploads\/2022\/02\/scientific_method_cs.jpg\" alt=\"\" class=\"wp-image-9725\" srcset=\"https:\/\/www.go-fair.org\/wp-content\/uploads\/2022\/02\/scientific_method_cs.jpg 710w, https:\/\/www.go-fair.org\/wp-content\/uploads\/2022\/02\/scientific_method_cs-300x64.jpg 300w\" sizes=\"auto, (max-width: 710px) 100vw, 710px\" \/><figcaption><a href=\"https:\/\/doi.org\/10.1098\/rsta.2020.0210\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1098\/rsta.2020.0210<\/a>, Odd Erik Gundersen<\/figcaption><\/figure><\/div>\n\n\n\n<p>Within Artificial Intelligence and Machine Learning, reproducibility is even more important since often times the computing environment in which an experiment was performed can greatly impact the data. Since Linux flavors use different versions of software, there is a likelihood that different flavors of Linux can produce different results depending on the software used. Generally when reproducing an AI experiment, a description of the experiment and code\/data are needed; however, many times a thorough description of the computing environment used is omitted. We must quantify these inter-laboratory differences and computer architecture differences since they can greatly impact the performance of differing AI methodologies. Acknowledging these differences can assist in understanding whether further contextual adjustment is needed or whether certain experiments need to be tested in different computing environments.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.youtube.com\/watch?v=onqJeT-hQpI\" target=\"_blank\" rel=\"noreferrer noopener\">Watch the entire presentation.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This article was written by Kevin Coakley and Alexandra Andreiu. It was published on 28 January 2022 on the GO FAIR US website. The \u201cr\u201d in FAIR\u2014 which stands for reusable\u2014 has always been confused with reproducibility. Although the two\u2026 <a href=\"https:\/\/www.go-fair.org\/2022\/02\/04\/reproducibility-in-ai-and-what-computing-professionals-should-know-for-supporting-researchers\/\">Continue reading <span class=\"meta-nav\">\u2192<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":9839,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1],"tags":[],"country":[],"organizer":[],"group":[],"class_list":["post-9723","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-go-build"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Reproducibility in AI, and What Computing Professionals Should Know for Supporting Researchers - GO FAIR<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.go-fair.org\/2022\/02\/04\/reproducibility-in-ai-and-what-computing-professionals-should-know-for-supporting-researchers\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Reproducibility in AI, and What Computing Professionals Should Know for Supporting Researchers - GO FAIR\" \/>\n<meta property=\"og:description\" content=\"This article was written by Kevin Coakley and Alexandra Andreiu. It was published on 28 January 2022 on the GO FAIR US website. The \u201cr\u201d in FAIR\u2014 which stands for reusable\u2014 has always been confused with reproducibility. 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article was written by Kevin Coakley and Alexandra Andreiu. It was published on 28 January 2022 on the GO FAIR US website. The \u201cr\u201d in FAIR\u2014 which stands for reusable\u2014 has always been confused with reproducibility. Although the two\u2026 Continue reading \u2192","jetpack_shortlink":"https:\/\/wp.me\/p9OEYt-2wP","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.go-fair.org\/wp-json\/wp\/v2\/posts\/9723","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.go-fair.org\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.go-fair.org\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.go-fair.org\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.go-fair.org\/wp-json\/wp\/v2\/comments?post=9723"}],"version-history":[{"count":2,"href":"https:\/\/www.go-fair.org\/wp-json\/wp\/v2\/posts\/9723\/revisions"}],"predecessor-version":[{"id":9727,"href":"https:\/\/www.go-fair.org\/wp-json\/wp\/v2\/posts\/9723\/revisions\/9727"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.go-fair.org\/wp-json\/wp\/v2\/media\/9839"}],"wp:attachment":[{"href":"https:\/\/www.go-fair.org\/wp-json\/wp\/v2\/media?parent=9723"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.go-fair.org\/wp-json\/wp\/v2\/categories?post=9723"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.go-fair.org\/wp-json\/wp\/v2\/tags?post=9723"},{"taxonomy":"country","embeddable":true,"href":"https:\/\/www.go-fair.org\/wp-json\/wp\/v2\/country?post=9723"},{"taxonomy":"organizer","embeddable":true,"href":"https:\/\/www.go-fair.org\/wp-json\/wp\/v2\/organizer?post=9723"},{"taxonomy":"group","embeddable":true,"href":"https:\/\/www.go-fair.org\/wp-json\/wp\/v2\/group?post=9723"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}