{"id":2143,"date":"2019-12-18T04:14:51","date_gmt":"2019-12-18T01:14:51","guid":{"rendered":"http:\/\/kusuaks7\/?p=1748"},"modified":"2024-02-08T09:40:34","modified_gmt":"2024-02-08T09:40:34","slug":"ai-isnt-smarter-than-you-are-yet","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/ai-isnt-smarter-than-you-are-yet\/","title":{"rendered":"AI Isn\u2019t Smarter Than You Are… Yet"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tArtificial Intelligence (AI) and machine learning (ML) applications are growing and expanding into all industries and functions.\u00a0From ranking sales leads (Einstein) to chatting with customers (Bold 360) artificial intelligence seems to be popping up everywhere.\u00a0However, many of the of the boldest claims for artificial intelligence have not yet come to fruition.\u00a0AI is\u00a0not yet curing cancer<\/a>\u00a0and Amazon\u2019s recruiting tool acted with\u00a0just as much bias<\/a>\u00a0as a normal human recruiter.\u00a0And therein lies a key takeaway for anyone wanting to find success with AI:\u00a0artificial intelligence can\u2019t yet do things that humans can\u2019t do.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tSure, machines can be faster and make fewer errors than humans, but if you\u2019re trying to train a machine to do something, you better make sure you could teach a human to do it first, even if the human would be painfully slow.\u00a0You also need to make sure that that the machine can actually use all of the information that a human would need to solve the problem.\u00a0If your task involves performing some analysis on numbers in a spreadsheet, you\u2019re in good shape.\u00a0Machines are good at math and the data is already in a convenient structure.\u00a0If, instead, your task is to read a report and write a summary, well, you\u2019re in trouble.\u00a0Natural language processing (NLP) still has a long way to go before it read with the same level of comprehension as a first grader, much less a financial analyst.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nYou can see the evidence for these facts in the adoption patterns of AI.\u00a0Robotic Process Automation (RPA) is one of the\u00a0fastest growing applications<\/a>\u00a0of AI.\u00a0Rather than solving new problems in new ways, RPA solves existing problems in the exact same ways as humans\u2014essentially learning to copy the actions human take to interact with digital systems and automating the repetitive pieces with \u201cvirtual workers.\u201d\u00a0To date, most AI is copying what humans do in order to do it faster, more efficiently, and more accurately\u2014whether scoring leads, creating dashboards, or detecting fraud.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThis is good news and bad news for workers.\u00a0The good news is that AI, through automation and RPA, can take over many of our most unpleasant and boring tasks.\u00a0No one will miss populating data throughout five different internal systems or manually moving files for storage.\u00a0The bad news is that there are jobs in the market that can be automated almost out of existence.\u00a0Although the end result of automation on the workforce is something experts\u00a0love<\/a>\u00a0to\u00a0argue<\/a>\u00a0about, it\u2019s pretty clear that jobs with low skilled and repetitive tasks will begin to evaporate.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFor leaders who are looking to use artificial intelligence to improve output or lower costs, there is also good and bad news.\u00a0The bad news is that artificial intelligence isn\u2019t advanced enough to solve challenging, complex problems.\u00a0Or even to offload much of research and development or investor presentations.\u00a0The good news is that there are real cost savings available through artificial intelligence, they just often aren\u2019t in the most exciting, glamorous, and news-worthy parts of the business.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tExecutives who want to take on AI projects\u2014and that should be everyone because this is a competency that is becoming \u201ctable stakes\u201d for staying competitive in every industry\u2014need to keep some basic advice in mind.\n
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  1. Start simple.\u00a0Begin by automating simple, repetitive processes that eat up employee time and wear on employee nerves.\u00a0This kind of automation doesn\u2019t make headlines, but that improving your profit margin might.\u00a0RPA is a good way to begin building baseline skills in AI.<\/li>\n \t
  2. Focus on what humans do already.\u00a0Once you can automate simple tasks, begin expanding into more intellectual exercises.\u00a0Items like sales lead rating or inventory forecasting are good places to begin.\u00a0Humans do these tasks today, usually leveraging solid data and a little bit of insight.\u00a0See if machines can help or do it better.<\/li>\n \t
  3. Remember the need for data. Humans are excellent processors of unstructured data.\u00a0Conversations, reports, web pages, fuzzy memories, and more get incorporated into our decision making processes.\u00a0If we want to offload tasks to machines, we need to give them access to all of the data that we use when making decisions.\u00a0If the majority of this data is unstructured, then the tasks is not a good fit for AI.<\/li>\n<\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    \n\t\t\t\t\t\t\t\t\tAt AIMatters, we got to experience all of these challenges and roadblocks ourselves as we tried to teach a machine learning system to identify companies\u2019 business models based on financial reports.\u00a0Although a smart analyst could read an annual report and come up with a solid answer, it took many iterations for the team to find the algorithms and the data required to teach a machine to do the same.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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    \n\t\t\t\t\t\t\t\t\t\nIn twenty years, we will be having an entirely different conversation about AI\u2014and hopefully reflecting on the many new insights and innovations this technology has contributed to the world.\u00a0In the meantime, leaders need to take baby steps and focus on tasks that machines can succeed at\u2014those that humans can do well, with accessible and structured data.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

    Artificial intelligence seems to be popping up everywhere. However, many of the boldest claims for artificial intelligence have not yet come to fruition.  And therein lies a key takeaway for anyone wanting to find success with AI: artificial intelligence can’t yet do things that humans can’t do. In twenty years, we will be having an entirely different conversation about AI—and hopefully reflecting on the many new insights and innovations this technology has contributed to the world.<\/p>\n","protected":false},"author":69,"featured_media":3086,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[97],"ppma_author":[1627],"class_list":["post-2143","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-artificial-intelligence"],"authors":[{"term_id":1627,"user_id":69,"is_guest":0,"slug":"barry-libert","display_name":"Barry Libert","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Libert","first_name":"Barry","job_title":"","description":"Barry Libert is the co-founder of AIMatters, an AI startup serving the C-Suite with the first AI powered strategy platform.  He serves on a number of boards and advises CEO of startups and large enterprises on becoming AI-first companies. He also co-wrote The Network Imperative: How to Survive and Grow in the Age of Digital Business Models published by HBR. "}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2143","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/69"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=2143"}],"version-history":[{"count":5,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2143\/revisions"}],"predecessor-version":[{"id":35893,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2143\/revisions\/35893"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3086"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=2143"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=2143"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=2143"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=2143"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}