{"id":214988,"date":"2016-01-14T00:00:00","date_gmt":"2016-01-14T10:39:34","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/machine-learning-as-creative-tool-for-designing-real-time-expressive-interactions\/"},"modified":"2016-07-15T15:30:23","modified_gmt":"2016-07-15T22:30:23","slug":"machine-learning-as-creative-tool-for-designing-real-time-expressive-interactions","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/machine-learning-as-creative-tool-for-designing-real-time-expressive-interactions\/","title":{"rendered":"Machine Learning as Creative Tool for Designing Real-Time Expressive Interactions"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Supervised learning algorithms can be understood not only as a set of techniques for building accurate models of data, but also as design tools that can enable rapid prototyping, iterative refinement, and embodied engagement- all activities that are crucial in the design of new musical instruments and other embodied interactions. Realising the creative potential of these algorithms requires a rethinking of the interfaces through which people provide data and build models, providing for tight interaction-feedback loops and efficient mechanisms for people to steer and explore algorithm behaviours.<\/p>\n<p>In this talk, I will discuss my research on better enabling composers, musicians, and developers to employ supervised learning in the design of new real-time systems. I will show a live demo of tools that I have created for this purpose, centering around the Wekinator software toolkit for interactive machine learning. I&#8217;ll discuss some of the outcomes from 6 years of employing and observing others using machine learning in creative contexts. These include a better understanding how machine learning can be used as a tool for design by end users and developers, and how using machine learning as a design tool differs from more conventional application contexts.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Supervised learning algorithms can be understood not only as a set of techniques for building accurate models of data, but also as design tools that can enable rapid prototyping, iterative refinement, and embodied engagement- all activities that are crucial in the design of new musical instruments and other embodied interactions. Realising the creative potential of [&hellip;]<\/p>\n","protected":false},"featured_media":257595,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_hide_image_in_river":0,"footnotes":""},"research-area":[],"msr-video-type":[206954],"msr-locale":[268875],"msr-post-option":[],"msr-session-type":[],"msr-impact-theme":[],"msr-pillar":[],"msr-episode":[],"msr-research-theme":[],"class_list":["post-214988","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-video-type-microsoft-research-talks","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/kCaHtHgUGNE","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/214988","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/214988\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/257595"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=214988"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=214988"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=214988"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=214988"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=214988"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=214988"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=214988"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=214988"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=214988"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=214988"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}