{"id":583,"date":"2018-01-10T02:17:27","date_gmt":"2018-01-10T02:17:27","guid":{"rendered":"http:\/\/kusuaks7\/?p=188"},"modified":"2025-04-28T09:38:21","modified_gmt":"2025-04-28T09:38:21","slug":"tone-down-your-ai-expectations","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/tone-down-your-ai-expectations\/","title":{"rendered":"Tone down your AI expectations"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tReady to learn Machine Learning? Browse courses<\/a>\u00a0like\u00a0Robotics Application Machine Learning<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong>\n\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
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\n\t\t\t\t\t\t\t\t\tWe have had many previous hype cycles around AI. As I wrote in Silicon Collar: \u201cSince the 1950s! That is when Alan Turing defined his famous test to measure a machine\u2019s ability to exhibit intelligent behavior equivalent to that of a human. In 1959, we got excited when Allen Newell and his colleagues coded the General Problem Solver. In 1968, Stanley Kubrick sent our minds into overdrive with HAL in his movie,\u00a02001: A Space Odyssey<\/em>. We applauded when IBM\u2019s Deep Blue supercomputer beat Grandmaster Garry Kasparov at chess in 1997. We were impressed in 2011 when IBM\u2019s Watson beat human champions at Jeopardy! and again in 2016 when Google\u2019s AlphaGo showed it had mastered Go, the ancient board game. Currently, we are so excited about Amazon\u2019s Echo digital assistant\/home automation hub and its ability to recognize the human voice, that we are saying a machine has finally passed the Turing Test. Almost.\u201d\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
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\n\t\t\t\t\t\t\t\t\tThe good news is over the seven decades, the AI community has gifted us a wide range of big words like deep learning, neural networks, cognitive computing and natural language processing.\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
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\n\t\t\t\t\t\t\t\t\tYale computer science professor David Gelernter thinks we have only scratched the surface. In his book\u00a0The Tides of Mind<\/em>, he calls it \u201cthe spectrum of consciousness,\u201d which is \u201cessentially a range of mental states through which all humans cycle each day. The cyclical element is crucial and underlies his metaphor of tidal motion. At the upper end of the spectrum, mental high tide, we are focused on the outer world, biased towards logical and abstract reasoning, and more likely to remember our experiences later. But as we drift down through the middle and into the lower reaches of the spectrum, we become increasingly conscious of the inner worlds of memory, prefer narrative to logic, and cross eventually into the difficult-to-remember realms of dreams.\u201d\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
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\n\t\t\t\t\t\t\t\t\tBut we are told this time it is different. We can train machines with the gobs of Big Data we have been collecting and we have computing power in the cloud like we have never had before.\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
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\n\t\t\t\t\t\t\t\t\tBut do we really have enough of each? Listening to Ginni Rometty, CEO of IBM at Dreamforce last month (see interview below \u2013 Marc Benioff has always been a very respectful interviewer, and Ginni is in fine form), I have my doubts. Ginni makes the point that 80% of data is not searchable. I would argue that ratio for enterprise data is even higher. Most useful data is locked up in on-prem systems. Getting permissions to have broad samples of such data will be no easy feat. Then you have the issue of semantics and master data rationalization across the multiple sources of data. We are only now seeing early versions of natural user interfaces by training machines around decades of voice\/accent data collected by the likes of Nuance in transcriptions ,voice mails we have been leaving each other since the early 1980s and photos we have been uploading into the cloud.\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
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\n\t\t\t\t\t\t\t\t\t\nGinni is also preparing us for a world of quantum computing because for a growing range of scenarios even today\u2019s supercomputers (or cloud infrastructure) are not enough. Take the trajectory of weather forecasting. Decade after decade, data sets have become larger with satellites, buoy sensors and Hurricane Hunter type sorties collecting data on moisture, temperature, wind speed and other metrics. The modeling has become way more complex. So, the thirst for ever increasing compute capacity continues. Forecasts keep getting better and better \u2013 but tell that to many of my fellow Floridians who evacuated multiple times as Hurricane Irma made a mockery of most forecasts. As a renowned meteorologist said, \u201cI was very surprised not by how one model was going back and forth \u2014 but by how all the models were going back and forth.\u201d Exactly, now wait till Salesforce\u2019s Einstein and IBM\u2019s Watson start contradicting each other Customers are being told they need both.\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
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\n\t\t\t\t\t\t\t\t\t\nGinni does not overtly talk about it, but 50% of IBM revenues come from services. Go ask early Watson healthcare clients how much of the tab was in services. IBM\u2019s interest in Salesforce\u2019s AI tool, Einstein is being driven by its Bluewolf services unit. Amazon offers its engineers as consultants via its ML Solutions Lab, Google via its The Machine Learning Advanced Solutions Lab. We will need highly skilled labor to train the machines. And we will also need tactical labor to keep refining the data. Amazon has its talent networks \u2013 the Kindle author, the Flex courier, the Mechanical Turk and fulfillment networks it can mine. Apple has its iTune and iOS networks to draw from.\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
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\n\t\t\t\t\t\t\t\t\t\nEnough usable data, enough compute, enough talent? Take a hard look at each from the lens of your AI project.\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
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\n\t\t\t\t\t\t\t\t\tFar more tactical implementations of Robotic Process Automation (RPA) are more promising. RPA is not machine learning, it uses software bots to mimic human activity \u2013 like logging in to a system, copying and pasting data across systems. The payback is much lower than what AI\/ML promises, but it removes the drudgery and increases productivity of many white collar jobs.\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
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\n\t\t\t\t\t\t\t\t\tMihir Shukla of RPA vendor, Automation Anywhere told me in an interview for Silicon Collar\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
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\n\t\t\t\t\t\t\t\t\t\u201cThink of the impact of this on real estate requirements. I can take what would need offices in six large buildings and put them on two racks. I can help consolidate global offices. A cognitive bot can process an invoice in 30 languages\u2014I don\u2019t need centers in the Philippines and other centers in Japan to deliver diverse language skills. Think of the employee recruiting and onboarding time we can help save.\u201d\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
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\n\t\t\t\t\t\t\t\t\tIt is a more traditional definition of savings that comes with automation, and far more realistic. But even there, many services firms are pushing RPA which is a bit of a red herring when it comes to implementation costs.\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
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\n\t\t\t\t\t\t\t\t\tOf course, many companies are being deluded by the promises of AI (and broader automation). J.P. Gownder, a Forrester analyst says if you don\u2019t have realistic expectations \u201cYou\u2019ll be inclined to automate too many roles \u2014 at the expense of both customer and employee experience.\u00a0 You won\u2019t hire the right mix of new roles\u2014 or any new people at all.\u201d\u00a0 In other words, besides seeing poor payback from your AI projects, you may hurt other critical parts of your business.\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
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\n\t\t\t\t\t\t\t\t\t\nOn the flip side, we may not need to worry as much when Elon Musk says AI is more dangerous than N. Korea. Or when Prof. Hawking says AI will end mankind. The bad guys will face the same data, compute and talent shortages that AI for good is facing.\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
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\n\t\t\t\t\t\t\t\t\tTone down your AI expectations. And get ready for the next generation of AI big words.\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":"

Ready to learn Machine Learning? Browse courses\u00a0like\u00a0Robotics Application Machine Learning developed by industry thought leaders and Experfy in Harvard Innovation Lab. We have had many previous hype cycles around AI. As I wrote in Silicon Collar: \u201cSince the 1950s! That is when Alan Turing defined his famous test to measure a machine\u2019s ability to exhibit<\/p>\n","protected":false},"author":165,"featured_media":3219,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[97],"ppma_author":[1693],"class_list":["post-583","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-artificial-intelligence"],"authors":[{"term_id":1693,"user_id":165,"is_guest":0,"slug":"vinnie-mirchandani","display_name":"Vinnie Mirchandani","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Mirchandani","first_name":"Vinnie","job_title":"","description":"Vinnie Mirchandani, Author of Silicon Collar, is CEO of Deal Architect<\/a>, a top advisory boutique recognized in The Black Book of Outsourcing. His books include SAP Nation, The New Polymath, and The New Technology Elite. Previously Analyst at Gartner, Partner with PwC Consulting. Keynoted speaker at many business and technology conferences, he has been quoted in the Wall Street Journal, BusinessWeek, The Financial Times, CIO Magazine, and other executive and technology publications."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/583","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\/165"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=583"}],"version-history":[{"count":5,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/583\/revisions"}],"predecessor-version":[{"id":37720,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/583\/revisions\/37720"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3219"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=583"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=583"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=583"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=583"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}