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Artificial Intelligence Will Save Jobs, Not Destroy Them.

Jun 24, 2020 | Artificial Intelligence (AI), Digitalization Trends, Organization Effectiveness

In the latter half of the 1980s a debate ensued between two camps  of economists roughly grouped around the views of Edward Prescott, on  the one hand, and Lawrence Summers, on the other.

Prescott argued that by and large, the booms and busts of the  economic cycle were due to “technological shocks”; and Summers dismissed  the notion as speculation not supported by evidence. 

Over the years, the ‘technological shock’ model of economic shifts  (TS) has surfaced over and over again in many forms, rising to the  occasion whenever the debate over cycles rears its head.

Today, TS has penetrated the discussion on the nexus between  Artificial Intelligence (AI) and the employment situation in advanced  economies, with some AI enthusiasts like former Googler Sebastian Thrun  offering fodder to those economists who are pessimistic about the impact  of technology on the job market.

The famous Oxford Martin study by Frey and Osborne in 2013  concluded that “[a]ccording to our estimates, about 47 percent of total  US employment is at risk.” It did offer the somewhat elite-reassuring  view that the highest paid jobs and those requiring the highest  educational attainment (and the two categories are often conjoined)  might be safe for a significant time. The question, of course, is: “for  how much longer?”

To be blunt, trying to predict the future more than two decades at a  stretch is more science fiction than anything else. This article will  thus focus on the ‘near horizon’ rather than the ‘distant future’.

A full generation after the Prescott-Summers debate, the issue of  ‘productivity’ remains central. In recent comments, Summers has pointed  to an interesting anomaly: despite the significant withdrawal of many  blue collar jobs from the US economy (and others like it), partly  because of the march of automation, productivity growth has been  unimpressive, or even anemic.

There are hints in the data, both formal and anecdotal, if one  cares to look carefully that while technology may be improving the  quality of life on the whole, its aggregate effect on enterprise  efficiency could be exaggerated, which is precisely the suspicion that  led me to this subject and to this article.

The subconscious trigger, however, must have been comparing the  efficient flow of Fortnum & Mason’s human-manned checkout-tills in  London’s Bond Street, with the relative bumbling at Sainsbury’s  robot-manned checkouts less than a mile away.

At the time I made my impressions I was not aware of a damning study at the University of Leicester in 2015  which had found that the robot-checkout contraptions could trigger  everything from aggressive behavior to increased shoplifting, and that  they were actually losing the supermarkets significant amounts of money.

When they first launched, the pitch was that auto-checkouts would  save shoppers 500,000 hours of unnecessary queuing time. The reality  today is depressingly different.

Similar attempts by Australian mining giants to induce human redundancy using robots have been beset with glitches, have led to lost production, and hasty retreats from the technology.

It is not surprising then that a careful look at the actual flow of  R&D dollars into AI in many of the most tech savvy companies  reveals a less prominent role for ‘hard automation’, defined roughly as  ‘machine induced human redundancy (MIHRED), than is usually perceived to  be the case.

Salesforce’s recently launched product, Einstein, focuses on  helping salespeople write superior emails to targeted prospects. SAP’s  HANA is integrating AI to help users better detect fraudulent  transactions. Enlitic promises algorithms to make junior doctors read  x-rays faster and more accurately, not to replace them. Affectiva wants  to use its deep-learning kit to help empathy-challenged people become  more emotively competent. It goes on and on.

At play here are two interlocking principles: the ideas of ‘digital  bicephaly (dibicephaly)’ – a literal new ‘exended-hemisphere’ of the  brain where accurate measurement, a behaviour largely alien to the human  mind, can thrive on demand – and ‘cognitive exoskeletons (COGNEX)’ – a  concept related to Flynn’s thesis of ‘new cognitive tools’ driving  incremental increases in observed human IQ. 

This is no Engelbart and Kurzweil style ‘machine-human symbiosis’ utopia however. 

At the root of the cognex-dibicephaly vision of the future is,  rather, a strong emphasis on the workplace and on the ‘mid-range’ scale  of capabilities in both humans and machines (pseudo-AI). There are two  contexts that converge on the same point.

Firstly, most people think of automation through the lens of  assembly-line logic. Actually, ‘automation’ is a softer and more  pervasive feature of all modern management. Every modern company in the  world has been deploying more and more supply chain management (SCM),  customer response management (CRM) and enterprise resource planning  (ERP) systems in a bid to automate more and more functions. Rather than  Human redundancy, improved HUMAN productivity has been the chief driver.

The problem though is that, as Denver-based Panorama Consulting has  noticed, only 12% of companies report full satisfaction with their  automation programs. 

Gartner has found that 75% of all ERP implementations fail.  In fact, Thomas Wailgum (a top executive of the American SAP Users  Group) once estimated that the chances of a successful ERP  implementation may be closer to 7%.

Poor automation outcomes make experimenting with innovative  business models harder and prone to failure, and the single most cited  cause is poor “personnel interfacing”. In every major lawsuit in the  wake of a failed implementation, like Bridgestone’s $600 million suit  against IBM, personnel-automation incongruity rise to the top of the  pile. It has been widely observed that attempts to circumvent rather  than enhance human input typically constitute the key failure points.

The question, it would seem then, is not how to remove humans from the chain altogether, but how to embed them more seamlessly.

The second point is the issue of ‘unfilled jobs’. 

America alone has nearly 5.8 million of them. George Washington  University’s Tara Sinclair is the lead author of a recent report that  showed that a quarter of advertised jobs in the US and about a fifth in  other rich countries like Canada and Germany were going unfilled.

The report correctly tied this mismatch of human skills and labour  requirements with the sluggish growth in global productivity and thereby  casts a more interesting complexion on the issue of human redundancy  and artificial intelligence, at least in the near-term horizon.

In the same way that personnel inadequacies continue to undermine  efforts to automate the enterprise, skills imbalances inflate  unemployment rates and exaggerate the effect of efficiency-inducing  technology. And both dynamics are strongest not in the unskilled or the  superskilled segments (the tail-ends) but in the ‘middle-bulge’ of the  employment curve. 

It is reasonable to infer, given this background, that large-scale  human redundancies caused by transhuman AI are fanciful, at least in the  near-term horizon, given the actual performance of automation and the  gaps in the enterprise today. 

What is more likely is the proliferation of mid-tier AI systems  transforming the capacity of mid-level skilled workers to better fill  vacant jobs and to participate in human-critical automation of the enterprise, and in the search for novel business methods and models. 

With superior virtual reality and machine-iteration systems,  average food technologists can carry out a more varied range of  biochemical explorations. Nurses can perform a wider range of imaging  tests. Fashion design trainees can contribute more effectively to the  fabric technology sourcing process. 

And so on and so forth. 

With improving personnel agility comes more nimble business models and an expansion of the job market.

Add these prospects to the potential productivity lift and the  better synching of job openings and personnel availability and a whole  new vision of what pro-human or cis-human AI might do for the job market  emerges, one that is starkly different from the dystopian prophecies  tethered to the rise of trans-human AI.




Original Article published by World Economic Forum / Bright Simons, President, MPedigree /2016

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