{"id":214897,"date":"2015-11-25T00:00:00","date_gmt":"2015-11-25T20:24:46","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/towards-understandable-neural-networks-for-high-level-ai-tasks-part-7\/"},"modified":"2016-07-15T15:27:41","modified_gmt":"2016-07-15T22:27:41","slug":"towards-understandable-neural-networks-for-high-level-ai-tasks-part-7","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/towards-understandable-neural-networks-for-high-level-ai-tasks-part-7\/","title":{"rendered":"Towards Understandable Neural Networks for High Level AI Tasks &#8211; Part 7"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Relating tensor product representations to lamda-calculus, tree-adjoining grammars, other &#8216;vector symbolic architectures&#8217;, and the brain &#8211; Part 7 Topics that will be discussed in this final lecture of the series are: &#8211; programming tensor-product-representation-manipulating Gradient-Symbolic-Computation networks to perform function-application in l-calculus and tree-adjunction (as in Tree-Adjoining Grammar) &#8211; thereby demonstrating that GSC networks truly have complete symbol-processing (or &#8216;algebraic&#8217;) capabilities, which Gary Marcus and others have argued (at MSR and elsewhere) are required for neural networks (artificial or biological) to achieve genuine human intelligence. &#8211; comparison of the size of tensor product representations to the size of other schemes for encoding symbol structures in actual neural network models: contrary to many claims, tensor product representations are not larger &#8211; preliminary neural evidence for tensor product representations (in particular, distributed role vectors)<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Relating tensor product representations to lamda-calculus, tree-adjoining grammars, other &#8216;vector symbolic architectures&#8217;, and the brain &#8211; Part 7 Topics that will be discussed in this final lecture of the series are: &#8211; programming tensor-product-representation-manipulating Gradient-Symbolic-Computation networks to perform function-application in l-calculus and tree-adjunction (as in Tree-Adjoining Grammar) &#8211; thereby demonstrating that GSC networks truly have [&hellip;]<\/p>\n","protected":false},"featured_media":257361,"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-214897","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\/ej5Mjs-ajok","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/214897","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\/214897\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/257361"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=214897"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=214897"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=214897"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=214897"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=214897"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=214897"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=214897"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=214897"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=214897"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=214897"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}