{"id":5441,"date":"2019-02-22T14:30:22","date_gmt":"2019-02-22T04:30:22","guid":{"rendered":"https:\/\/testblogs.griffith.edu.au\/asiainsights\/?p=5441"},"modified":"2019-02-22T14:12:53","modified_gmt":"2019-02-22T04:12:53","slug":"ai-in-asia-challenges-and-opportunities","status":"publish","type":"post","link":"https:\/\/testblogs.griffith.edu.au\/asiainsights\/ai-in-asia-challenges-and-opportunities\/","title":{"rendered":"AI in Asia: Challenges and opportunities"},"content":{"rendered":"<p>In today\u2019s information age, artificial intelligence has made a name for itself as a futuristic agent that will take away jobs and leave the planet in desolation. At least, that is what the skeptics believe. The proponents of AI choose to harbor a different philosophy, that it is an agent that will solve humanity\u2019s most challenging problems. Perhaps both takes are justified\u2014let us unpack why.<\/p>\n<h2>What Is AI?<\/h2>\n<p>AI is an\u00a0<a href=\"https:\/\/techcrunch.com\/2016\/05\/12\/clarifying-the-uses-of-artificial-intelligence-in-the-enterprise\/\" target=\"_blank\" rel=\"noopener\">umbrella term<\/a>\u00a0that often gets thrown around to refer to tasks such as pattern recognition, speech recognition, computer vision, natural language processing, object detection, machine learning, statistical learning and many, many more. AI basically means to induce some level of \u201cintelligence\u201d to allow computers to make sense of data. Historically, automation comprised mostly of expert systems that relied on hard-coded if-then statements to control its actions in every imaginable scenario. Of course, this method is limited to the stretch of the programmer\u2019s imagination. AI systems, on the other hand, \u201clearn\u201d all the underlying features from an input data set to determine a suitable action.<\/p>\n<p>The limitation with these systems is that inferences or predictions can only be made on data that comes from the same distribution. For example, if a model is trained with images of elephants of a certain breed, it probably will not be able to detect elephants of a different breed with the same level of precision. The fact that these systems work on probabilistic\/stochastic models also means that predictions are typically not made with 100 percent accuracy. Even so, today\u2019s classic predictive models can successfully classify images with up to 99 percent accuracy. Simple proof of concept examples, such as image classification, can be stretched above and beyond by organizations to meet their specific needs. For example, image recognition can be used by law enforcement to locate fugitives, to read and interpret traffic signs in autonomous cars, and to perform medical image analysis to detect patterns unobservable to the naked eye, to name a few.<\/p>\n<p>Please click here to read the full <a href=\"http:\/\/www.brinknews.com\/asia\/challenges-and-opportunities-of-ai-in-asia\/?fbclid=IwAR3HM5ON-hl2H3v4bUaaUkRKc2EtNoOqip3NUKK8wX7fiLvxNuOozN4bufc\">&#8220;AI in Asia: Challenges and opportunities&#8221;<\/a> article a Brink Asia, written by\u00a0Aninda Saha and Griffith Asia Institute member, Dr Tapan Sarker.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In today\u2019s information age, artificial intelligence has made a name for itself as a futuristic agent that will take away jobs and leave the planet in desolation. At least, that is what the skeptics believe. The proponents of AI choose to harbor a different philosophy, that it is an agent that will solve humanity\u2019s most<a href=\"https:\/\/testblogs.griffith.edu.au\/asiainsights\/ai-in-asia-challenges-and-opportunities\/\">Continue reading <span class=\"sr-only\">&#8220;AI in Asia: Challenges and opportunities&#8221;<\/span><\/a><\/p>\n","protected":false},"author":42,"featured_media":5445,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_lmt_disableupdate":"","_lmt_disable":"","jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_newsletter_tier_id":0,"footnotes":""},"categories":[233,245,246,247],"tags":[745,498],"class_list":["post-5441","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-feature-series","category-china-and-north-east-asia","category-india-and-south-asia","category-indonesia-and-southeast-asia","tag-artificial-intelligence","tag-dr-tapan-sarker"],"acf":[],"modified_by":"Jill Moriarty","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>AI in Asia: Challenges and opportunities | Griffith Asia Insights<\/title>\n<meta name=\"description\" content=\"In 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