{"id":123,"date":"2011-02-15T22:16:18","date_gmt":"2011-02-15T14:16:18","guid":{"rendered":"http:\/\/stephenwang.com\/b\/?p=123"},"modified":"2018-08-28T10:33:45","modified_gmt":"2018-08-28T02:33:45","slug":"the-man-versus-machine-jeopardy-challenge","status":"publish","type":"post","link":"https:\/\/stephenwang.com\/b\/the-man-versus-machine-jeopardy-challenge\/123\/","title":{"rendered":"The Man versus Machine Jeopardy Challenge"},"content":{"rendered":"<p><a href=\"http:\/\/www.youtube.com\/watch?v=BfNBWJTGEEA\" data-rel=\"lightbox-video-0\">Jeopardy Feb. 14 2011 &#8211; Human vs Machine IBM Challenge Day 1 Part 1\/2<\/a><\/p>\n<p>It&#8217;s the probably the most public test of the advances in linked open structured data and semantic text analysis, I&#8217;m really following closely this tournament pitting IBM&#8217;s super-computer Watson against the two most successful Jeopardy champions. I suspect that they&#8217;re using the same publicly available data sets that we&#8217;re using for constructing <a href=\"https:\/\/stephenwang.com\/b\/alive-cn-2010-now\/54\/\">Alive.cn<\/a>.<\/p>\n<p>I wonder, however, why they chose to rely only on electronically fed questions rather than going the final mile and adding a voice recognition interface on top of the system. Voice recognition accuracy has gotten so good these days, but I wonder if the final few percentage mistakes makes a critical difference against the best human players.<\/p>\n<p>There have been some other truly AMAZING projects in this field. Two I&#8217;d like to highlight:<\/p>\n<ul>\n<li> <a href=\"http:\/\/http:\/\/www.google.com\/squared\/\" target=\"_blank\">Google Squared<\/a>: This Google Labs experiment is an amazing mash-up of topic extraction and turning unstructured web data into structured data. Simply type in any category (example: &#8220;Chinese Emperors&#8221;) and it will bring you up a spreadsheet of items in that category and some properties. Next, you can add your own properties (&#8220;Inventions&#8221;) and it will automatically fill in the results using searched data from the web converted back into structured data. It&#8217;s truly one of the most remarkable things to come out of Google, but a bit more work on it (say, a voice recognition interface) and it could be a mainstream breakthrough.<\/li>\n<li><a href=\"http:\/\/http:\/\/viewer.opencalais.com\/\" target=\"_blank\">OpenCalais Topic Extraction<\/a>: Another semantic analysis tool that will pull out &#8220;topics&#8221; automatically and link them against linked open data. Try out the free demo and copy-and-paste a news article. After submitting the article, you&#8217;ll see it has linked together topics on the side automatically.<\/li>\n<\/ul>\n<p>Like I&#8217;ve mentioned before, I feel that we&#8217;re right on the tipping point in the next several years where there will be advances in knowledge extraction and interpolation that will have a revolutionary effect on everything including how we interact with computing and having exponential advances on data forecasting. Projects like Wikipedia (an unstructured data source) are just the beginning.<\/p>\n<p><strong>P.S. My favorite comment about the Man versus Machine Jeopardy contest: <\/strong>&#8220;Why couldn&#8217;t they have programmed Watson to use the voice of Sean Connery?&#8221;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>It&#8217;s the probably the most public test of the advances in linked open structured data and semantic text analysis, I&#8217;m really following closely this tournament pitting IBM&#8217;s super-computer Watson against the two most successful Jeopardy champions.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[82,4],"tags":[83,108,104,105,106,107],"class_list":["post-123","post","type-post","status-publish","format-standard","hentry","category-alive-cn","category-internet","tag-alive-cn-2","tag-artificial-intelligence","tag-google","tag-linked-open-data","tag-semantic-analysis","tag-topic-extraction"],"_links":{"self":[{"href":"https:\/\/stephenwang.com\/b\/wp-json\/wp\/v2\/posts\/123","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stephenwang.com\/b\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/stephenwang.com\/b\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/stephenwang.com\/b\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/stephenwang.com\/b\/wp-json\/wp\/v2\/comments?post=123"}],"version-history":[{"count":3,"href":"https:\/\/stephenwang.com\/b\/wp-json\/wp\/v2\/posts\/123\/revisions"}],"predecessor-version":[{"id":955,"href":"https:\/\/stephenwang.com\/b\/wp-json\/wp\/v2\/posts\/123\/revisions\/955"}],"wp:attachment":[{"href":"https:\/\/stephenwang.com\/b\/wp-json\/wp\/v2\/media?parent=123"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/stephenwang.com\/b\/wp-json\/wp\/v2\/categories?post=123"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/stephenwang.com\/b\/wp-json\/wp\/v2\/tags?post=123"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}