{"id":113453,"date":"2020-09-26T10:22:47","date_gmt":"2020-09-26T17:22:47","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2020\/09\/facebook-wants-to-make-ai-better"},"modified":"2020-09-26T10:22:47","modified_gmt":"2020-09-26T17:22:47","slug":"facebook-wants-to-make-ai-better","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2020\/09\/facebook-wants-to-make-ai-better","title":{"rendered":"Facebook wants to make AI better"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/facebook-wants-to-make-ai-better.jpg\"><\/a><\/p>\n<p>The explosive successes of AI in the last decade or so are typically chalked up to lots of data and lots of computing power. But benchmarks also play a crucial role in driving progress\u2014tests that researchers can pit their AI against to see how advanced it is. For example, ImageNet, a public data set of 14 million images, sets a target for image recognition. MNIST did the same for handwriting recognition and GLUE (General Language Understanding Evaluation) for natural-language processing, leading to breakthrough <a href=\"https:\/\/www.technologyreview.com\/2020\/07\/20\/1005454\/openai-machine-learning-language-generator-gpt-3-nlp\/\">language models like GPT-3<\/a>.<\/p>\n<p>A fixed target soon gets overtaken. ImageNet is being updated and GLUE has been replaced by SuperGLUE, a set of harder linguistic tasks. Still, sooner or later researchers will report that their AI has reached superhuman levels, outperforming people in this or that challenge. And that\u2019s a problem if we want benchmarks to keep driving progress.<\/p>\n<p>So Facebook is releasing a new kind of test that pits AIs against humans who do their best to trip them up. Called<a href=\"http:\/\/ai.facebook.com\/blog\/dynabench-rethinking-ai-benchmarking\"> Dynabench<\/a>, the test will be as hard as people choose to make it.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The explosive successes of AI in the last decade or so are typically chalked up to lots of data and lots of computing power. But benchmarks also play a crucial role in driving progress\u2014tests that researchers can pit their AI against to see how advanced it is. For example, ImageNet, a public data set of [\u2026]<\/p>\n","protected":false},"author":513,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1522,6],"tags":[],"class_list":["post-113453","post","type-post","status-publish","format-standard","hentry","category-innovation","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/113453","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/513"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=113453"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/113453\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=113453"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=113453"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=113453"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}