{"id":118785,"date":"2021-01-22T23:42:16","date_gmt":"2021-01-23T07:42:16","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2021\/01\/china-wants-to-be-the-worlds-ai-superpower-does-it-have-what-it-takes"},"modified":"2021-01-22T23:42:16","modified_gmt":"2021-01-23T07:42:16","slug":"china-wants-to-be-the-worlds-ai-superpower-does-it-have-what-it-takes","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2021\/01\/china-wants-to-be-the-worlds-ai-superpower-does-it-have-what-it-takes","title":{"rendered":"China Wants to Be the World\u2019s AI Superpower. Does It Have What It Takes?"},"content":{"rendered":"

<\/a><\/p>\n

Both AlphaFold\u2019s and GPT-3\u2019s success was due largely to the massive datasets they were trained on; no revolutionary new training methods or architectures were involved. If all it was going to take to advance AI was a continuation or scaling-up of this paradigm\u2014more input data yields increased capability\u2014China could well have an advantage.<\/p>\n

But one of the biggest hurdles AI needs to clear to advance in leaps and bounds rather than baby steps is precisely this reliance on extensive, task-specific data. Other significant challenges include the technology\u2019s fast approach to the limits of current computing power and its immense energy consumption<\/a>.<\/p>\n

Thus, while China\u2019s trove of data may give it an advantage now, it may not be much of a long-term foothold on the climb to AI dominance. It\u2019s useful for building products that incorporate or rely on today\u2019s AI, but not for pushing the needle on how artificially intelligent systems learn. WeChat data on users\u2019 spending habits, for example, would be valuable in building an AI that helps people save money or suggests items they might want to purchase. It will enable (and already has enabled) highly tailored products that will earn their creators and the companies that use them a lot of money.<\/p>\n","protected":false},"excerpt":{"rendered":"

Both AlphaFold\u2019s and GPT-3\u2019s success was due largely to the massive datasets they were trained on; no revolutionary new training methods or architectures were involved. If all it was going to take to advance AI was a continuation or scaling-up of this paradigm\u2014more input data yields increased capability\u2014China could well have an advantage. But one [\u2026]<\/p>\n","protected":false},"author":556,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[],"class_list":["post-118785","post","type-post","status-publish","format-standard","hentry","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/118785","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\/556"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=118785"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/118785\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=118785"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=118785"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=118785"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}