Sustainable Management: Impact of Remote Intelligence on Growth

This report examines the transformative potential of remote intelligence in driving economic growth and sustainable management practices. It highlights how traditional drivers of economic progress, such as capital investment and labor, are no longer sufficient to sustain growth. Remote intelligence, encompassing technologies like computer vision, natural language processing, and expert systems, offers a new avenue for value creation. The report identifies key factors enabling its growth, including unlimited access to computing power and the rise of big data. It details three channels through which remote intelligence drives growth: intelligent automation, labor and capital augmentation, and innovation diffusion. The analysis includes the potential for boosting national economic growth and labor productivity, emphasizing the importance of preparing future generations, encouraging REMOTE-powered regulation, advocating a code of ethics, and addressing redistribution effects. The report concludes that embracing remote intelligence is crucial for achieving sustainable economic progress.

Sustainable Management

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Contents
Introduction.................................................................................................................................................2
What is remote intelligence?.......................................................................................................................3
Two key factors are enabling its growth..................................................................................................4
Three channels of remote-led growth..........................................................................................................4
Intelligent automation..............................................................................................................................5
Labor and capital augmentation...............................................................................................................5
Innovation diffusion................................................................................................................................6
Factoring in remote intelligence..................................................................................................................8
Boosting national economic growth........................................................................................................8
Labor productivity revival.......................................................................................................................8
Clearing the path to an it future...................................................................................................................9
Prepare the next generation for the future................................................................................................9
Encourage REMOTE-powered regulation...............................................................................................9
Advocate a code of ethics......................................................................................................................10
Address the redistribution effects..........................................................................................................10
Conclusion.................................................................................................................................................11
References.................................................................................................................................................13
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Introduction
There has been marked decline in the ability of increases in capital investment and in labor to
propel economic progress. These two levers are the traditional drivers of production, yet they are
no longer able to sustain the steady march of prosperity enjoyed in previous decades in most
developed economies. But long-term pessimism is unwarranted. With the recent convergence of
a transformative set of technologies, economies are entering a new era in which artificial/remote
intelligence has the potential to overcome the physical limitations of capital and labor and open
up new sources of value and growth. After the analysis of the developed economies it was found
that IT has the potential to double their annual economic growth rates by 2035. To avoid missing
out on this opportunity, policy makers and business leaders must prepare for, and work toward, a
future with artificial intelligence. They must do so not with the idea that remote is simply another
productivity enhancer. Rather, they must see IT as the tool that can transform our thinking about
how growth is created (Bibby & Rozier , 2017).
The deficit of innovation, combined with unfavorable demographic trends, flagging educational
attainment and rising wealth inequality, will slow economic progress. Growth occurs when the
stock of capital or labor increase, or when they are used more efficiently. The growth that comes
from innovations and technological change in the economy is captured in total factor
productivity (TFP). Economists have always thought of new technologies as driving growth
through their ability to enhance TFP.
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What is remote intelligence?
It is not a new field that is much of its theoretical and technological underpinning was developed
over the past 70 years by computer scientists such as Alan Turing, Marvin Minsky and John
McCarthy. Today, the term refers to multiple technologies that can be combined in different
ways to:
Sense: Computer vision and audio processing, for example, are able to actively perceive the
world around them by acquiring and processing images, sounds and speech. The use of facial
recognition at border control kiosks is one practical example of how it can improve productivity.
Comprehend: Natural language processing and inference engines can enable remote systems to
analyze and understand the information collected. This technology is used to power the language
translation feature of search engine results (Jennifer, et al., 2018).
Act: A remote system can take action through technologies such as expert systems and inference
engines, or undertake actions in the physical world. Auto-pilot features and assisted braking
capabilities in cars are examples of this.
All three capabilities are underpinned by the ability to learn from experience and adapt over
time. Remote system already exists to some degree in many industries but the extent to which it
is becoming part of our daily lives is set to grow fast (Brooks, 2015).
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Two key factors are enabling its growth
1. Unlimited access to computing power.
Public cloud computing was estimated to reach almost US$70 billion in 2015 worldwide. Data
storage has also become abundant.
2. Growth in big data.
Global data has seen a compound annual growth rate (CAGR) of more than 50 percent since
2010 as more of the devices around us have become connected. As Barry Smyth, professor of
computer science at University College Dublin, told us: “Data is to AI what food is to humans.”
So in a more digital world, the exponential growth of data is constantly feeding IT improvements
(Brundage & Avin, 2018).
Three channels of remote-led growth
With this new factor of production, it can drive growth in at least three important ways. First, it
can create a new virtual workforce—what we call “intelligent automation.” Second, it can
complement and enhance the skills and ability of existing workforces and physical capital. Third,
like other previous technologies, it can drive innovations in the economy. Over time, this
becomes a catalyst for broad structural transformation as economies using IT not only do things
differently, they will also do different things (Herweijer & Waughray, 2018).
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Intelligent automation
The new remote-powered wave of intelligent automation is already creating growth through a set
of features unlike those of traditional automation solutions. The first feature is its ability to
automate complex physical world tasks that require adaptability and agility. Consider the work
of retrieving items in a warehouse, where companies have relied on people’s ability to navigate
crowded spaces and avoid moving obstacles. Now, robots from Fetch Robotics use lasers and 3D
depth-sensors to navigate safely and work alongside warehouse workers. Used in tandem with
people, the robots can handle the vast majority of items in a typical warehouse (Smith-Looper,
2017). Whereas traditional automation technology is task specific, the second distinct feature of
remote-powered intelligent automation is its ability to solve problems across industries and job
titles. The third and most powerful feature of intelligent automation is self-learning, enabled by
repeatability at scale. Amelia, like a conscientious employee, recognizes the gaps in her own
knowledge and takes steps to close them. If Amelia is presented with a question that she cannot
answer, she escalates it to a human colleague, and then observes how the person solves the
problem. The self-learning aspect of remote intelligence is a fundamental change. Whereas
traditional automation capital degrades over time, intelligent automation assets constantly
improve (Ramanujam & Agnello, 2018).
Labor and capital augmentation
A significant part of the economic growth from this will come not from replacing existing labor
and capital, but in enabling them to be used much more effectively. For example, IT can enable
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humans to focus on parts of their role that add the most value. Also, IT augments labor by
complementing human capabilities, offering employees new tools to enhance their natural
intelligence. Using machine learning and big data processing technologies, its AI platform reads
more than 22 million peer reviewed scientific papers to identify serious emerging risks. As a
result, underwriters can not only price risk more accurately, but also create new insurance
products (Collins, et al., 2016).
It can also improve capital efficiency— a crucial factor in industries where it represents a large
sunk cost. For instance, in manufacturing, industrial robotics company Fanuc has teamed up with
Cisco and other firms to create a platform to reduce factory downtime—estimated at one major
automotive manufacturer to cost US$20,000 per minute. The Fanuc Intelligent Edge Link and
Drive (FIELD) system is an analytics platform powered by advanced machine learning. It
captures and analyzes data from disparate parts of the manufacturing process to improve
manufacturing production. Already FIELD has been deployed in an 18-month “zero downtime”
trial at one manufacturer, where it realized significant cost savings (Pinder, 2018).
Innovation diffusion
One of the least-discussed benefits of artificial intelligence is its ability to propel innovations as
it diffuses through the economy. Take driverless vehicles. Using a combination of lasers, global
positioning systems, radar, cameras, computer vision and machine learning algorithms, driverless
vehicles can enable a machine to sense its surroundings and act accordingly. Not only are Silicon
Valley technology companies entering the market, but traditional companies are building new
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partnerships to stay relevant (Feld, 2018). As innovation begets innovation, the potential impact
of driverless vehicles on economies could eventually extend well beyond the automotive
industry. Mobile service providers could see even more demand from subscribers as drivers, now
free to enjoy leisure activities while traveling, spend more time on the Internet, which, in turn,
could create new advertising opportunities for the service providers and selling opportunities for
their retailer partners (Fallon, 2016).
The insurance industry could create new revenue streams from the masses of data that self-
driving vehicles generate. By combining vehicle data with other streams such as smart phones
and public transport systems, they could not only build up a more complete picture of their
customers, but also they could create new policies that insure total customer mobility, not just
driving. Real-time, accurate road and traffic data generated by driverless vehicles could
supplement other sources of information to enable local authorities to change the way they
charge for road usage. Standard vehicle registration could be replaced with more equitable and
convenient pay-per-use road tolls, with instantly updated prices to help reduce congestion. There
could even be significant social benefits (Baldwin, 2017). Driverless vehicles are expected to
reduce the number of road accidents and traffic fatalities dramatically, making the technology
potentially one of the most transformative public health initiatives in human history. They could
also give back independence to people who cannot drive due to disability, enabling them to take
up jobs from which they were previously excluded. And, even among those who can drive,
driverless cars will make traveling far more convenient, freeing up time that people can dedicate
to work or leisure (Loubier, 2017).
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Factoring in remote intelligence
Boosting national economic growth
To estimate the economic potential of IT we compared two scenarios for each country. The first
is the baseline, which shows the expected annual economic growth rate under current
assumptions about the future. The second is the IT scenario, which shows expected economic
growth once the impact of IT has been absorbed into the economy. As it takes time for the
impact of a new technology to feed through, we used 2035 as the year of comparison. Japan
could more than triple its gross value added (GVA) growth during the same period, rising it from
0.8 percent to 2.7 percent (Tugend, 2014).
Labor productivity revival
IT has the potential to boost labor productivity by up to 40 percent in 2035 in the countries we
studied. This rise in labor productivity will not be driven by longer hours but by innovative
technologies enabling people to make more efficient use of their time. This labor productivity
increase dramatically reduces the number of years required for our analyzed countries’
economies to double in size (Salvalaggio, 2017). The results are primarily driven by a country’s
ability to diffuse technological innovations into its wider economic infrastructure. While this
vary from country to country, our results are indicative of its ability to transcend regional and
structural disparities, enabling huge, rapid leaps in labor productivity.
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Clearing the path to an it future
Prepare the next generation for the future
Successfully integrating human intelligence with machine intelligence, so that they coexist in a
two-way learning relationship, will become more critical than ever. As the division of tasks
between man and machine changes, policy makers need to reevaluate the type of knowledge and
skills imparted to future generations. Currently, technological education goes in one direction:
people learn how to use machines. Increasingly, this will change as machines learn from humans,
and humans learn from machines (Hess, 2014).
Encourage REMOTE-powered regulation
As autonomous machines take over tasks that have exclusively been undertaken by humans,
current laws will need to be revisited. For instance, the state of New York’s 1967 law that
requires drivers to keep one hand on the wheel was designed to improve safety, but may inhibit
the uptake of semi-autonomous safety features, such as automatic lane centralization. In other
cases, new regulation is called for. For example, though IT could be enormously beneficial in
medical diagnoses, physicians avoid using these technologies, fearing that that they would be
exposed to accusations of malpractice (Ding & Xian , 2018).
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Advocate a code of ethics
Intelligent systems are rapidly moving into social environments that were once only occupied by
humans. This is opening up ethical and societal issues that can slow down the progress of IT.
These range from how to respond to racially biased algorithms to whether autonomous cars
should give preference to their driver’s life over others in the case of an accident. Given how
prevalent intelligent systems will be in the future, policy makers need to ensure the development
of a code of ethics for the AI ecosystem. Ethical debates need to be supplemented by more
tangible standards and best practices in the development of intelligent machines. As a segment of
AI, the robotics industry is already ahead in setting universal standards for its operations.
Business standards regarding robots produced by the British Standards Institution (BSI) are a
step in the right direction (Fisher & Mackaness, 1988).
Address the redistribution effects
Many commentators are concerned that IT will eliminate jobs, worsen inequality and erode
incomes. This explains the rise in protests around the world and discussions taking place in
countries, such as Switzerland, on the introduction of a universal basic income. Policy makers
must recognize that these apprehensions are valid. Their response should be two fold. First,
policy makers should highlight how IT can result in tangible benefits. For instance, it can
improve job satisfaction. An Accenture survey highlighted that 84 percent of managers believe
machines will make them more effective and their work more interesting (Parris, 2017). Beyond
the workplace, it promises to alleviate some of the world’s greatest problems, such as climate
change (through more efficient transportation) and poor access to healthcare (by reducing the
strain on overloaded systems). Benefits like these should be clearly articulated to encourage a
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more positive outlook on its potential. Second, policy makers need to actively address and
preempt the downsides of it. Some groups will be affected disproportionately by these changes.
To prevent a backlash, policy makers should identify the groups at high risk of displacement and
create strategies that focus on reintegrating them into the economy (Moosavi, et al., 2016).
Conclusion
IT has the potential to have a broad-based disruptive impact on society, creating a variety of
economic benefits. While some of these benefits can be measured, others, such as consumer
convenience and time savings, are far more intangible in nature. Our analysis focuses on
measuring the GVA impact. We began with a modified growth model developed by Robin
Hanson, professor of economics at George Mason University, Virginia, United States. We
looked at the additional increase in growth that would occur as a result of it by contrasting it with
the baseline growth rate. In our model, we defined labor as a continuum of tasks that can either
be performed by a human or artificial intelligence—not work solely done by humans. The intent
was to introduce intelligent systems as an additional workforce capable of handling activities that
require an advanced level of cognitive agility.
To estimate the shares of workers’ tasks that could be performed by intelligent machines, we
drew on research by Frey and Osborne who take a task-based approach to identifying roles and
occupations that are affected. The estimates are aggregated at country and industry-level, taking
into account the different mix of occupations and industries within each country. We assume that
employment will be constant in the long term. We considered the uptake of it—from zero to the
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maximum technological potential. We assumed that a 50 percent uptake would be reasonable in
the time frame analyzed, that is, substitution is assumed to achieve 50 percent of its
technological potential.
Capacity of countries to absorb IT technologies: A key driver of the impact on growth is how
well each country is positioned to benefit from the emergence of new technologies and how
ready it is to integrate them into its economy—measured by what we refer to as a country’s
“national absorptive capacity” (NAC). This includes factors such as access to sophisticated
information and communication technology infrastructure, a reliable regulatory framework, and
considerable public and private investments in the digital economy. All economies that derive a
significant IT dividend rank high on this index.
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