{"id":886227,"date":"2022-10-24T09:00:26","date_gmt":"2022-10-24T16:00:26","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-video&#038;p=886227"},"modified":"2022-10-25T05:55:02","modified_gmt":"2022-10-25T12:55:02","slug":"lightning-talks-training-and-inference-efficiency","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/lightning-talks-training-and-inference-efficiency\/","title":{"rendered":"Lightning talks: Training and inference efficiency"},"content":{"rendered":"<p>To bring AI to more people, models need to be cheaper to train and run, in terms of both computational and human resources. Increasing efficiency across various parts of the training and inference pipeline includes optimizing existing large models and creating new architectures and training paradigms.<\/p>\n<p><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/event\/microsoft-research-summit-2022\/\">Learn more about the 2022 Microsoft Research Summit ><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>To bring AI to more people, models need to be cheaper to train and run, in terms of both computational and human resources. Increasing efficiency across various parts of the training and inference pipeline includes optimizing existing large models and creating new architectures and training paradigms. Learn more about the 2022 Microsoft Research Summit ><\/p>\n","protected":false},"featured_media":886230,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556],"msr-video-type":[266199,266181],"msr-locale":[268875],"msr-post-option":[],"msr-impact-theme":[264846],"msr-pillar":[],"class_list":["post-886227","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-video-type-efficient-large-scale-ai","msr-video-type-microsoft-research-summit-2022","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/hHyjmW-EVjA","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/886227"}],"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":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/886227\/revisions"}],"predecessor-version":[{"id":886236,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/886227\/revisions\/886236"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/886230"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=886227"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=886227"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=886227"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=886227"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=886227"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=886227"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=886227"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}