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Why AI is the Most Hyped Technology of Our Time

Accenture claims that Artificial Intelligence (AI) will double growth rates for 12 developed countries by 2035 and increase labor productivity by as much as a third.

Even if the AI industry only grabs 1/3 of the $15 trillion in economic gains, it still implies a market of about $5 trillion or almost 1000 times the market in 2018 or a growth of about 78% per year.

Although estimates for its global market in 2018 were between half ($4.5 billion) to a little larger ($12 billion) than AI’s current market ($9.6 billion), the size of its 2030 market is forecasted to reach $85 billion, or about 1% the size of the AI market.

These books were followed by a feeding frenzy of reports from consulting organizations each trying to paint optimistic futures for AI and other new information technologies such as blockchain, Internet of Things, and quantum computing in order to obtain clients.

This constitutes about 40 percent of the overall $9.5 trillion to $15.4 trillion annual impact that could potentially be enabled by all analytical techniques.” These are big numbers and they are a powerful incentive for organizations to pursue AI, either with or without help from McKinsey consultants.

For instance, its predictions of a 10% improvement in energy efficiency in the UK and elsewhere were based on the purported success of both DeepMind and Nest Labs, another subsidiary of Alphabet since 2014, which lost $621 million on revenues of $726 million in 2017.

And the advertised potential 2020 energy savings figure for the UK has been more than halved since the campaign began, dropping from £26 to just £11 a year for duel-fuel bills and the cost of smart meters and their installation has risen, not good news for the diffusion of smart meters.

My forthcoming article in IEEE Spectrum (Why Projections for AI’s Economic Benefits are Overly Optimistic) demonstrates that the most well-funded AI start-ups are not targeting productivity enhancing applications and many are likely incurring huge losses, despite hiring famous university researchers at hugely inflated salaries.

For instance, while five years ago experts expected Watson to become a common tool for doctors, or even replace them, news articles now trumpet studies that show AI can interpret medical images as well as humans, even though image recognition has been touted for many years as the best application for AI.

For instance, two years ago in 2017, McKinsey’s “review of more than 160 global use cases across a variety of industries found that only 12percent had progressed beyond the experimental stage.” And ones that have proceeded past the experimental stage were mostly in finance and telecommunication.

Although progress in error rates for image and speech recognition or performance at playing chess or Go have been reported,improvements in these algorithms occur more slowly than has Moore’s Law.For example, the word error rate fell three times between 2008 and 2014, the image error rate fell about four times between 2010 and 2015, and chess ratings for computers rose by 65% between 1990 and 2014 while Moore’s Law experienced doubling in the number of transistors every 18 months to 2 years over 50 years.

In support of Moore’s Law over these years, other algorithms have also experienced improvements over decades, impacting on various industries such as telecom and finance, but their impact has been much smaller than that of Moore’s Law.For example, new telecom standards have required new algorithms, but the implementation of these algorithms required the faster processing speeds that came from Moore’s Law.

For instance, Cathy O’Neil, once an implementer of big data, has been criticizing it for many years, and is the author of the 2016 book, Weapons of Math Destruction: Big Data Increases Inequality and Threatens Democracy.Her book details the problems that big data caused for hiring and scheduling workers, setting bail and sentencing criminals, targeting crimes, rising in a ranking, and targeting customers with ads.

In each case, big data does not address an underlying productivity problem and instead targets a superficial issue, often one concerned with capturing more value from workers or from customers than from improving productivity and it often encourages people to game the system.

Gary Smith, author of the 2019 book, the AI Delusion, addresses these types of issues in a more basic way, focusing on the fundamental challenges of data analysis, a highly manual process that requires better data than often exists.

He said: “You can see the computer age everywhere but in the productivity statistics.” The reason computers have not had the on productivity that many expected is because manual systems weren’t as inefficient as ordinarily thought and thus computers didn’t contribute large improvements in productivity outside of a few industries such as communications and entertainment.

The fact is that people, materials, and equipment are for the most part effectively organized in industries such as manufacturing, logistics, construction, retail, and wholesale and the last few decades of implementing IT has increased the effectiveness of their organization.One exception is personal transportation in which most personal vehicles sit for 95% of the time and the rest of the time sit in traffic.

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