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Weekly Bot Brief on Robotic Research and Investment Review 6-22-2018

POSTED 06/23/2018

There is no force on earth more powerful than an idea whose time has come"  -Victor Hugo

Bot Index Highlights:Bot Index vs. S & P 500, 6-25-2018

Tariffs and trade dominated the market news during the week ending June 22. The S & P 500 recovered a bit on Friday, containing its loss to only .88%, from a very sloppy previous four days. The robotic names, however, faired poorly to the tune of a 2.08% decline. Twenty five of the thirty components turned in negative results, and of the five that eked out gains, the best performer was Oceaneering International (+8.74%) and that was due to a sharp run up in the price of oil (OPEC met and voted to a smaller than expected production expansion; oil futures trading near $70).

Of the losers, there were many and of significance. While there were no double-digit decliners, a number of the pedigree names in the index suffered notable declines:

Hiwin Technologies         -9.33%

Accuray Inc.                      -8.05%

Ekso Bionics                     -6.11%

Rockwell Automation      -5.45%

NVIDIA Corp.                    -5.39%

Lincoln Electric                 -5.09%

IRobot Corp.                     -3.80%

Cyberdyne Inc.                 -3.74%

Lockheed Martin             -3.47%

Papers Presented at the American Economic Association Annual Meeting:

Each year the American Economic Association holds a major conference of its membership of primarily educational economists. At the one hundred and thirtieth meeting that was held in Philadelphia in early January of this year, an edited version of the one-hundred and twelve publications was delivered in the AEA May 2018 Papers and Proceedings. Of interest to the Bot Brief were the four editorials that fell under the subtitle of ‘Economic Consequences of Artificial Intelligence and Robotics’. A brief synopsis of the articles in included below:

In the article entitled, What Can Machines Learn and What Does It Mean for Occupations and the Economy, two professors from MIT and an educator from Carnegie Mellon explored the impact of robotics on productivity and their application within the employment/corporate framework. Some of their research led to takeaways that included: “So far, the realized economic effects [of machine learning] are small relative to the potential offered by this new general-purpose technology”. As we, likewise, heard from Mike Lewis in a recent Bot Brief edition the authors expressed, “Entrepreneurs and innovators take time to adopt new technologies, reconfigure existing work, discover new business processes and co-invent complementary technologies”. While the research pointed to the fact that virtually all occupations have some tasks upon which machine learning can apply, in reviewing the O*NET’s (Occupational Information Network by the U.S. Dept of Labor) mapping of 2,069 work activities within 964 U.S. occupations, the redesign of tasks provides the most significant improvement to productivity. In fact, the authors submitted, “The focus of researchers, as well as managers and entrepreneurs, should be not just on automation, but on job redesign”. In fact, the “Suboptimal bundling of tasks in jobs can block potential productivity gains from machine learning.” They noted that the following job categories which are most vulnerable to machine learning were; concierges, mechanical drafters, morticians, undertakers and funeral directors, credit authorizers and brokerage clerks. The employment of massage therapists, animal scientists, archeologists, public address systems and other announcers and plasterers and stucco masons were among the occupations that have the least possibility of ML application. The researchers concluded, “Automation technologies have historically been the key driver of increased industrial productivity. They have also disrupted employment and the wage structure systematically. However, our analysis suggests that ML will affect very different parts of the workforce than earlier waves of automation. Furthermore, tasks within jobs, typically show considerable variability in the suitability for machine learning, while few jobs can be fully automated using ML. Machine learning technology can transform many jobs in the economy, but full automation will be less significant than the reengineering of process and the reorganization of tasks.” Another interesting takeaway was that research pointed toward the fact that it will become more difficult to assess the performance of employees since the most measurable tasks are also those tasks that are most likely to be conducted by machine learning.

The second article, Modeling Automation by Daron Acemoglu of MIT and Pascual Restrepo from BC, presented arguments regarding how automation should be modeled. In disagreement with other research described as either Capital-Augmenting Technological Change or Labor-Augmenting Technological Change, the authors espoused a Task-Based Approach [to conceptualizing and modeling]. Their approach, which was supported with numerous data and mathematical manipulation, emphasized that “automation technologies that are more likely to reduce the demand for labor are not those that are ‘brilliant’ and highly productive, but those that are so-so – just productive enough to be adopted but not much more productive or cost-saving than the production techniques that they are replacing.” Regarding newly formed labor tasks, “new tasks increase productivity, the demand for labor, and the labor share.” Perhaps the most insightful of the article’s observations included something the authors dubbed “automation at the extensive margin”, which is to say that an increase in the demand for labor may be achieved by a deepening of automation by expanding the productivity of machines in already automated tasks.

In an article entitled, A Method to Link Advances in Artificial Intelligence to Occupational Abilities the authors sought to explore the fact that research on robotics has noted, “little systematic empirical research on the link between AI and labor, and the handful of existing studies arrive at different findings.” Their paper “provides a new method that we believe can help researchers and policy makers to better understand the link between AI and labor”. The paper drew similar conclusions to the aforenoted MIT/Carnage Mellon article where there was a focus on the labor categories most and least likely to be impacted by AI.

The final tome, Human Judgement and AI Pricing was prepared by three members of the faculty of the University of Toronto and starts out with the exclamation that “Artificial Intelligence is undergoing a renaissance”. Clearly, the authors have a positive bias on the implications that AI presents in a variety of roles, however, the publication drills down into AI’s ability to assist in the values determined by the utility function. For non-economic types, the utility function is a measurement of the consumers’ preferences for alternative real goods. The authors feel that AI can be of great assistance in sorting through the myriad data that tends to reflect those preferences, however, they argue that “only a human, at this time, can develop this (utility function determination) knowledge”. In their conclusion, the Canadian researchers determined that AI complements human judgement and decision-making and, as such, has a “non-straightforward impact on the demand for AI and how it is priced.”

While none of these research efforts made any drastic or dire predictions regarding either productivity or labor’s impact by robotics, the Bot Brief is pleased to see that empirical research is beginning to focus on the subject. Most certainly the Robotic Revolution will have a profound impact on the world economy and policy makers will need guidance from academic sources to stay ahead of the knowledge curve.

Member: American Economic Association, Society of Professional Journalists, United States Press Association. Institute of Chartered Financial Analysts, Robotic Industries Association

The Bot Brief is a weekly newsletter designed for economists, investment specialists, journalists and academicians. It receives no remuneration from any companies that may from time to time be featured and its commentaries, analysis, opinions and research represent the subjective views of Balcones Investment Research, LLC. Due to the complex and rapidly changing nature of the subject matter, the company makes no assurances as to the absolute accuracy of material presented.

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