{"id":1389,"date":"2020-10-20T00:43:21","date_gmt":"2020-10-20T00:43:21","guid":{"rendered":"https:\/\/bmolab.artsci.utoronto.ca\/?p=1389"},"modified":"2020-11-02T19:02:17","modified_gmt":"2020-11-02T19:02:17","slug":"thursday-october-15","status":"publish","type":"post","link":"https:\/\/bmolab.artsci.utoronto.ca\/?p=1389","title":{"rendered":"Performers-in-Residence Update &#8211; Oct 15"},"content":{"rendered":"\n<p>Today we dipped our toes in the world of Artificial Intelligence. We played with a neural network (GPT-2) that was trained on an enormous dataset of text, and then fine-tuned to the complete set of plays of William Shakespeare. <\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"994\" height=\"808\" src=\"https:\/\/bmolab.artsci.utoronto.ca\/wp-content\/uploads\/GPT-2_explorer.png\" alt=\"\" class=\"wp-image-872\" srcset=\"https:\/\/bmolab.artsci.utoronto.ca\/wp-content\/uploads\/GPT-2_explorer.png 994w, https:\/\/bmolab.artsci.utoronto.ca\/wp-content\/uploads\/GPT-2_explorer-300x244.png 300w, https:\/\/bmolab.artsci.utoronto.ca\/wp-content\/uploads\/GPT-2_explorer-768x624.png 768w\" sizes=\"auto, (max-width: 994px) 100vw, 994px\" \/><figcaption><em>The GPT-2 Shakespeare Explorer showing the probabilities for the next word to be produced (yellow is very likely, as you move towards purple, the words are much less likely to be chosen.)<\/em><\/figcaption><\/figure>\n\n\n\n<p>This lead to a lively discussion about the relative meaningfulness or meaninglessness of the resulting text, and ways that this sort of system might be useful in the context of performance, as a tool for improvisation, as a challenge to the actor as interpreter, and as a tool during the workshopping phase of a new play. We considered the fact that plays and recipes were among the first human creations that resembled computer programs. In each case you define your ingredients or dramatic personnae or program variables, then provide a set of instructions to be performed.<\/p>\n\n\n\n<p>Rick Miller, who will also be working with us in the Lab dropped by to  meet Sebastien and Ryan and join the discussion.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"2048\" height=\"856\" src=\"https:\/\/bmolab.artsci.utoronto.ca\/wp-content\/uploads\/Pia-Sebastien-Rick-Ryan.jpg\" alt=\"\" class=\"wp-image-1377\" srcset=\"https:\/\/bmolab.artsci.utoronto.ca\/wp-content\/uploads\/Pia-Sebastien-Rick-Ryan.jpg 2048w, https:\/\/bmolab.artsci.utoronto.ca\/wp-content\/uploads\/Pia-Sebastien-Rick-Ryan-300x125.jpg 300w, https:\/\/bmolab.artsci.utoronto.ca\/wp-content\/uploads\/Pia-Sebastien-Rick-Ryan-1024x428.jpg 1024w, https:\/\/bmolab.artsci.utoronto.ca\/wp-content\/uploads\/Pia-Sebastien-Rick-Ryan-768x321.jpg 768w, https:\/\/bmolab.artsci.utoronto.ca\/wp-content\/uploads\/Pia-Sebastien-Rick-Ryan-1536x642.jpg 1536w\" sizes=\"auto, (max-width: 2048px) 100vw, 2048px\" \/><figcaption><em>Pia Kleber, Sebastien Heins, Rick Miller and Ryan Cunningham in conversation<\/em><\/figcaption><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Today we dipped our toes in the world of Artificial Intelligence. We played with a neural network (GPT-2) that was trained on an enormous dataset of text, and then fine-tuned to the complete set of plays of William Shakespeare. This lead to a lively discussion about the relative meaningfulness or meaninglessness of the resulting text, [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[5],"tags":[],"class_list":{"0":"post-1389","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-canstage_bmo","7":"entry"},"_links":{"self":[{"href":"https:\/\/bmolab.artsci.utoronto.ca\/index.php?rest_route=\/wp\/v2\/posts\/1389","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bmolab.artsci.utoronto.ca\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bmolab.artsci.utoronto.ca\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bmolab.artsci.utoronto.ca\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/bmolab.artsci.utoronto.ca\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1389"}],"version-history":[{"count":7,"href":"https:\/\/bmolab.artsci.utoronto.ca\/index.php?rest_route=\/wp\/v2\/posts\/1389\/revisions"}],"predecessor-version":[{"id":1729,"href":"https:\/\/bmolab.artsci.utoronto.ca\/index.php?rest_route=\/wp\/v2\/posts\/1389\/revisions\/1729"}],"wp:attachment":[{"href":"https:\/\/bmolab.artsci.utoronto.ca\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1389"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bmolab.artsci.utoronto.ca\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1389"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bmolab.artsci.utoronto.ca\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1389"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}