{"id":62690,"date":"2018-02-22T14:32:25","date_gmt":"2018-02-22T19:32:25","guid":{"rendered":"http:\/\/danielschristian.com\/learning-ecosystems\/?p=62690"},"modified":"2018-02-22T14:39:40","modified_gmt":"2018-02-22T19:39:40","slug":"tech-companies-should-stop-pretending-ai-wont-destroy-jobs-6-other-items-re-ai-bots-algorithms-more","status":"publish","type":"post","link":"http:\/\/danielschristian.com\/learning-ecosystems\/2018\/02\/22\/tech-companies-should-stop-pretending-ai-wont-destroy-jobs-6-other-items-re-ai-bots-algorithms-more\/","title":{"rendered":"“Tech companies should stop pretending AI won\u2019t destroy jobs” + 6 other items re: AI, bots, algorithms, & more"},"content":{"rendered":"

Tech companies should stop pretending AI won\u2019t destroy jobs<\/strong><\/a> — from technologyreview.com \/ MIT Technology Review by Kai-Fu Lee
\nNo matter what anyone tells you, we\u2019re not ready for the massive societal upheavals on the way.<\/em><\/p>\n

Excerpt (emphasis DSC):<\/span><\/em><\/p>\n

The rise of China as an AI superpower isn\u2019t a big deal just for China. The competition between the US and China has sparked intense advances in AI that will be impossible to stop anywhere. The change will be massive, and not all of it good. Inequality will widen. As my Uber driver in Cambridge has already intuited, AI will displace a large number of jobs, which will cause social discontent. Consider the progress of Google DeepMind\u2019s AlphaGo software, which beat the best human players of the board game Go in early 2016. It was subsequently bested by AlphaGo Zero, introduced in 2017, which learned by playing games against itself and within 40 days was superior to all the earlier versions. Now imagine those improvements transferring to areas like customer service, telemarketing, assembly lines, reception desks, truck driving, and other routine blue-collar and white-\u00adcollar work. It will soon be obvious that half of our job tasks can be done better at almost no cost by AI and robots. This will be the fastest transition humankind has experienced, and we\u2019re not ready for it.<\/span><\/strong>
\n…
\nAnd finally, there are those who deny that AI has any downside at all\u2014which is the position taken by many of the largest AI companies.<\/span> It\u2019s unfortunate that AI experts aren\u2019t trying to solve the problem. What\u2019s worse, and unbelievably selfish, is that they actually refuse to acknowledge the problem exists in the first place.<\/span><\/strong><\/p>\n

These changes are coming, and we need to tell the truth and the whole truth. We need to find the jobs that AI can\u2019t do and train people to do them. We need to reinvent education.<\/span><\/strong> These will be the best of times and the worst of times. If we act rationally and quickly, we can bask in what\u2019s best rather than wallow in what\u2019s worst.<\/span><\/strong><\/p>\n

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From DSC:<\/span><\/em>
\nIf a business has a choice between hiring a human being or having the job done by a piece of software and\/or by a robot, which do you think they’ll go with? My guess? It’s all about the money — whichever\/whomever will be less expensive will get the job. <\/span><\/p>\n

However, that way of thinking may cause <\/strong>enormous social unrest if the software and robots leave human beings in the (job search) dust.<\/strong> <\/em>Do we, as a society, win with this way of thinking? To me, it’s capitalism gone astray. We aren’t caring enough for our fellow members of the human race, people who have to put bread and butter on their tables. People who have to support their families. People who want to make solid contributions to society and\/or to pursue their vocation\/callings — to have\/find purpose in their lives.<\/span><\/p>\n

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Others think we\u2019ll be saved by a universal basic income. \u201cTake the extra money made by AI and distribute it to the people who lost their jobs,\u201d they say. \u201cThis additional income will help people find their new path, and replace other types of social welfare.\u201d But UBI doesn\u2019t address people\u2019s loss of dignity or meet their need to feel useful. It\u2019s just a convenient way for a beneficiary of the AI revolution to sit back and do nothing.<\/strong> <\/span><\/p><\/blockquote>\n

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To Fight Fatal Infections, Hospitals May Turn to Algorithms<\/strong> <\/a>\u2014 from scientificamerican.com by John McQuaid
\nMachine learning could speed up diagnoses and improve accuracy<\/em><\/p>\n

Excerpt:<\/em><\/p>\n

The CDI algorithm\u2014based on a form of artificial intelligence called machine learning\u2014is at the leading edge of a technological wave starting to hit the U.S. health care industry. After years of experimentation, machine learning\u2019s predictive powers are well-established, and it is poised to move from labs to broad real-world applications, said Zeeshan Syed, who directs Stanford University\u2019s Clinical Inference and Algorithms Program.<\/p>\n

\u201cThe implications of machine learning are profound,\u201d Syed said. \u201cYet it also promises to be an unpredictable, disruptive force\u2014likely to alter the way medical decisions are made and put some people out of work.<\/p>\n

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Lawyer-Bots Are Shaking Up Jobs<\/strong> <\/a>\u2014 from technologyreview.com by Erin Winick<\/p>\n

Excerpt:<\/em><\/p>\n

Meticulous research, deep study of case law, and intricate argument-building\u2014lawyers have used similar methods to ply their trade for hundreds of years. But they\u2019d better watch out, because artificial intelligence is moving in on the field.<\/p>\n

As of 2016, there were over 1,300,000 licensed lawyers and 200,000 paralegals in the U.S. Consultancy group McKinsey estimates that 22 percent of a lawyer\u2019s job and 35 percent of a law clerk\u2019s job can be automated, which means that while humanity won\u2019t be completely overtaken, major businesses and career adjustments aren\u2019t far off (see \u201cIs Technology About to Decimate White-Collar Work?\u201d). In some cases, they\u2019re already here.<\/p>\n

 <\/p>\n

\u201cIf I was the parent of a law student, I would be concerned a bit,\u201d says Todd Solomon, a partner at the law firm McDermott Will & Emery, based in Chicago. \u201cThere are fewer opportunities for young lawyers to get trained, and that\u2019s the case outside of AI already. But if you add AI onto that, there are ways that is advancement, and there are ways it is hurting us as well.\u201d<\/span><\/strong><\/p><\/blockquote>\n

 <\/p>\n

So far, AI-powered document discovery tools have had the biggest impact on the field. By training on millions of existing documents, case files, and legal briefs, a machine-learning algorithm can learn to flag the appropriate sources a lawyer needs to craft a case, often more successfully than humans. For example, JPMorgan announced earlier this year that it is using software called Contract Intelligence, or COIN, which can in seconds perform document review tasks that took legal aides 360,000 hours.<\/p>\n

…<\/p>\n

People fresh out of law school won\u2019t be spared the impact of automation either. Document-based grunt work is typically a key training ground for first-year associate lawyers, and AI-based products are already stepping in. CaseMine, a legal technology company based in India, builds on document discovery software with what it calls its \u201cvirtual associate,\u201d CaseIQ. The system takes an uploaded brief and suggests changes to make it more authoritative, while providing additional documents that can strengthen a lawyer\u2019s arguments.<\/p>\n

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Lessons From Artificial Intelligence Pioneers<\/strong><\/a> \u2014 from gartner.com by Christy Pettey<\/p>\n

CIOs are struggling to accelerate deployment of artificial intelligence (AI). A recent Gartner survey of global CIOs found that only 4% of respondents had deployed AI. However, the survey also found that one-fifth of the CIOs are already piloting or planning to pilot AI in the short term.<\/p>\n

Such ambition puts these leaders in a challenging position. AI efforts are already stressing staff, skills, and the readiness of in-house and third-party AI products and services. Without effective strategic plans for AI, organizations risk wasting money, falling short in performance and falling behind their business rivals.<\/p>\n

\u201c<\/span>Pursue small-scale plans likely to deliver small-scale payoffs that will offer lessons for larger implementations\u201d<\/span><\/span><\/strong><\/p><\/blockquote>\n

\u201cAI is just starting to become useful to organizations but many will find that AI faces the usual obstacles to progress of any unproven and unfamiliar technology,\u201d says Whit Andrews, vice president and distinguished analyst at Gartner. \u201cHowever, early AI projects offer valuable lessons and perspectives for enterprise architecture and technology innovation leaders embarking on pilots and more formal AI efforts.\u201d<\/p>\n

So what lessons can we learn from these early AI pioneers?<\/p>\n

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Why Artificial Intelligence Researchers Should Be More Paranoid<\/strong><\/a> \u2014 from wired.com by Tom Simonite<\/p>\n

Excerpt:<\/em><\/p>\n

What to do about that? The report\u2019s main recommendation is that people and companies developing AI technology discuss safety and security more actively and openly\u2014including with policymakers. It also asks AI researchers to adopt a more paranoid mindset and consider how enemies or attackers might repurpose their technologies before releasing them.<\/p>\n

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How to Prepare College Graduates for an AI World<\/strong><\/a>\u00a0— from wsj.com by
\nNortheastern University President Joseph Aoun says schools need to change their focus, quickly<\/em><\/p>\n

Excerpt:<\/em><\/p>\n

WSJ:<\/strong> What about adults who are already in the workforce?<\/em><\/p>\n

DR. AOUN:<\/strong> Society has to provide ways, and higher education has to provide ways, for people to re-educate themselves, reskill themselves or upskill themselves.<\/p>\n

That is the part that I see that higher education has not embraced. That\u2019s where there is an enormous opportunity. We look at lifelong learning in higher education as an ancillary operation, as a second-class operation in many cases. We dabble with it, we try to make money out of it, but we don\u2019t embrace it as part of our core mission.<\/p>\n

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Inside Amazon’s Artificial Intelligence Flywheel <\/strong><\/a>— from wired.com by Steven Levy
\n<\/strong>How deep learning came to power Alexa, Amazon Web Services, and nearly every other division of the company.<\/em><\/p>\n

Excerpt:<\/em><\/p>\n

Amazon loves to use the word flywheel to describe how various parts of its massive business work as a single perpetual motion machine. It now has a powerful AI flywheel, where machine-learning innovations in one part of the company fuel the efforts of other teams, who in turn can build products or offer services to affect other groups, or even the company at large. Offering its machine-learning platforms to outsiders as a paid service makes the effort itself profitable\u2014and in certain cases scoops up yet more data to level up the technology even more.<\/p>\n

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 <\/p>\n","protected":false},"excerpt":{"rendered":"

Tech companies should stop pretending AI won\u2019t destroy jobs — from technologyreview.com \/ MIT Technology Review by Kai-Fu Lee No matter what anyone tells you, we\u2019re not ready for the massive societal upheavals on the way. Excerpt (emphasis DSC): The rise of China as an AI superpower isn\u2019t a big deal just for China. The […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[556,356,314,817,174,86,208,112,72,403,63,391,419,74,285,353,431,44,309,201,321,367,299],"tags":[],"class_list":["post-62690","post","type-post","status-publish","format-standard","hentry","category-amazon","category-artificial-intelligence-agents-llms-and-related","category-asia","category-bots","category-career-development","category-change","category-cloud-based-computing-apps","category-corporate-business-world","category-daniel-s-christian","category-ethics","category-google","category-human-computer-interaction-hci","category-ideas-teaching","category-leadership","category-legislation-legislatures","category-moralsvalues","category-organizational-change","category-pace-of-change","category-platforms","category-policy","category-united-states","category-vendors","category-workplace"],"_links":{"self":[{"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/posts\/62690","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/comments?post=62690"}],"version-history":[{"count":14,"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/posts\/62690\/revisions"}],"predecessor-version":[{"id":62753,"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/posts\/62690\/revisions\/62753"}],"wp:attachment":[{"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/media?parent=62690"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/categories?post=62690"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/tags?post=62690"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}