Smart machines range from augmented consumer appliances (as shown in Figure
1) to completely new innovations, like the “Astrobee” flying robot developed for
NASA (as depicted in Figure 2).

Figure 1. Smart Cooking and Cleaning Systems

Source: Flickr

The Astrobee free-flying robotic system is designed to help
astronauts reduce the time they spend on routine duties. Working autonomously or
via remote control by astronauts, flight controllers, or researchers on the
ground, the robots are designed to complete tasks such as taking inventory,
documenting experiments conducted by astronauts with their built-in cameras, or
working together to move cargo throughout the station. In addition, the system
serves as a research platform that can be outfitted and programmed to carry out
experiments in microgravity.

Figure 2. “Astrobee” Flying Robot

Source: NASA

Smart machines utilize machine learning to perform functions
traditionally conducted by humans.³ But as analyst Paul Clarke
adds, “For a system to be considered a genuine smart machine, … it needs to do
something that previously you would have thought only a human could do, and it
must display a high level of autonomy.”⁴

Smart Machines Mechanics

A smart machine is basically a machine or process regulated by a progammable
logic controller (PLC), a type of “ruggedized” computer which functions as the
machine’s brain. As described by analyst Dave Perkon, the PLC typically
interacts with either:

• A human-machine interface (HMI), or
• An Ethernet-connected supervisory control and data acquisition system (SCADA)
  with a smartphone-viewable HMI client.⁵

Smart Machine Market

A study conducted by Research & Markets reveals that the global smart machine
market will reach $29.9 billion by 2026. The market encompasses a wide array of
systems and devices, notably:

• Autonomous robots;
• Self-driving vehicles;
• Expert systems (such as medical decision support systems);
• Medical robots;
• Intelligent assistants (such as automated online assistants);
• Virtual private assistants (Siri, Google Assistant, Amazon Alexa, etc.);
• Embedded software systems (such as machine monitoring and control
  systems);
• Neurocomputers (such as purpose-built intelligent machines); and
• Smart wearable devices.⁶

If One Smart Machine Is Good, Two or More Are Better

While the term smart machine can apply to a single device or system, the
smart machine concept usually involves the aggregation and integration of
multiple, even hundreds or thousands, of smart machines to achieve a
high-level function or objective, such as regulating the production and
distribution of electricity, the so-called “smart grid.”

Today’s electric grid is built on a 1950s analog model in which electricity
is generated at a central power plant and then pushed over miles – or hundreds
of miles – of transmission lines to waiting customers, either businesses or
consumers. This traditional grid model is one-way, and allows for no two-way,
or interactive, communications between producers and consumers, with the effect
that both parties lack the information and the means to properly regulate
electric usage. With electricity becoming a more critical energy source,
helping power hybrid vehicles for example, this “dumb” grid model is no longer
tolerable.

Enter the newly emerging “smart grid,” which consists of three basic
components: smart machines, two-way communications, and advanced analytic
software.

• “Smart [machines], such as
  meters, monitors, and intelligent electronic devices, gather information
  about the flow and condition of power, and about the condition of equipment.
• “The smart [machines] transmit
  the information over a two-way communications pathway.
• “Advanced [analytic] software processes the data and uses it
  to ‘power’ applications. Some of those applications help run the grid
  itself. Others handle billing, service, and other customer-facing
  activities.”⁷

Smart Machines Today

Although smart machines sound futuristic, they are already commonly
deployed, even ubiquitous, machines such as:

• Smartphones,
• Smartwatches,
• Pilotless vehicles (drones),
• Virtual private assistants, even
• Housekeeping aids like the Roomba vacuum cleaner

Current View

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Smart Machines Are Really Smart

Analyst Brett Molina reveals that “robots are better at reading than humans,”
inquiring, somewhat rhetorically, “is there anything humans can do better
than robots?” In the latest affront to human superiority, AI systems from
Alibaba and Microsoft outperformed people in a reading comprehension
test.

The test utilized Stanford University’s SQuAD, a reading comprehension
dataset. Humans achieved a score of 82.304, while Alibaba posted an 82.44,
and Microsoft an 82.65.⁸

Smart Machines Are Irresistible

In most industries, labor costs are a constant drag on enterprise profits,
with the effect that enterprise executives are usually quick to embrace any
labor-saving strategy. Early automation created assembly lines,
allowing car manufacturers, for example, to produce more vehicles with fewer
workers. Later, industrial robots assumed more complex tasks like
welding, reducing the need for skilled workers even further. With the
advent of smart machines, even workers whose primary contribution is
intellectual, rather than physical, will be at risk.

For enterprise executives, replacing men and women with smart
machines is
irresistible, especially as their fiduciary duties demand that they keep
pace with competitors, who are also downsizing in favor of smart
machine alternatives.

With respect to one prominent form of smart machine, the industrial robot, a
just completed Federal Reserve Bank of Philadelphia white paper cautiously
concludes that their “findings suggest that further declines in the price of
industrial robots may lead to a sizable redistribution of a firm’s income away
from workers and toward capital owners among automating firms.”⁹

When IoT & Smart Machines Collide

In describing the smart machine revolution, analyst Paul Clarke compares smart machines and
The Internet of Things (IoT) as dual tsunamis which will magnify each other’s
effects. As Clarke explains, “The really exciting opportunities come
where these two tsunamis collide. Then we will have smart machines talking
to all these Internet enabled devices and, in so doing, becoming much more
aware of the world around them. And smart machines will be talking to other
smart machines, creating a lattice of smart machines and devices all talking
to one another.”

For example, “Let’s imagine you are driving to work and
your smart car develops a fault. The smart engine management system
communicates with the car manufacturer and additional diagnostics are
downloaded and run to identify the cause of the fault. A repair is required
and your car communicates with your garage to find a suitable service slot
tomorrow. The necessary parts are ordered from the manufacturer. Your car is
aware that its … annual service [is] due soon, so it arranges for [this] to happen at the same time.”¹⁰

With IoT as their digital backbone, analyst Tony Rhem contends that “[smart]
machines will be the most disruptive class of technologies over the coming
decade.”¹¹

The Intersection of Politics and Smart Machines

Andrew Yang, a New York businessman and former presidential candidate,
complains about the
corrosive influence of smart machines.
“All you need is self-driving cars to destabilize society.” In just a few years,
“we’re
going to have a million truck drivers out of work who are 94 percent
male, with an average level of education of high school or one year of
college. That one innovation will be enough to create riots in
the street. And we’re about to do the same thing to retail workers, call
center workers, fast-food workers, insurance companies, accounting
firms.”¹²

While Yang’s predictions are probably exaggerated, they may resonate
with the voting public, particularly if the general premise of his
argument is taken up by more mainstream politicians.

Smart Machines Are Becoming Even Smarter

In fact, Deloitte analysts predict an “era of pervasive intelligence,”
which “will be marked by a proliferation of AI-powered smart devices
able to recognize and react to sights, sounds, and other patterns.
Increasingly, machines:

• “Will learn from experiences,
• “Adapt to changing situations, and
• “Predict outcomes.

“Some will infer users’ needs and desires and even collaborate with
other devices by exchanging information, distributing tasks, and
coordinating their actions.”¹³

Outlook

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report]

Smart Machines and COVID-19

The coronavirus pandemic has greatly accelerated both the adoption of smart
machines and smart machine research and development. After all, smart machines can’t get sick, at least not biologically.
According to analysts Erico Guizzo and Randi Klett, this disease immunity makes
smart machines excellent candidates for risky healthcare functions such as “monitoring
patients, sanitizing hospitals, and helping frontline medical workers.”

While the current generation of medical robots may not be ideally suited to
their new and expanding COVID-19 responsibilities, “robot makers say the
experience they’ve gained during this trial-by-fire deployment will make their
future machines smarter and more capable.”¹⁴

Smart machines, of course, have already demonstrated their medical worth
pre-pandemic, facilitating, for example, minimally-invasive surgery (as
shown in Figure 3).

Figure 3. Safe and Precise Robotic Surgery

Source: Virtua Health

Smart Machines and Industrial Maintenance

Analyst John Soldatos predicts that smart machines “will provide a compelling
value proposition for industrial maintenance tasks” by facilitating:

• Automatic Data Collection – removing the need to deploy multiple
  data-gathering edge devices.
• Optimized Operations through Self-Learning – maximizing machine
  lifetimes and overall equipment efficiency (OEE).
• Predictive Maintenance – assessing what-if maintenance scenarios
  in terms of their potential to maximize OEE.
• Maintenance Scheduling and Optimization – initiating
  maintenance-related transactions such as ordering spare parts and scheduling
  tasks for technicians.
• Smart Contracts – executing maintenance-related transactions in
  accordance with smart contracts, e.g., service level agreements (SLAs).¹⁵

Artificial Intelligence Is Worrying

Owing to advances in artificial intelligence (AI), some worry that smart
machines may become too smart. Noted scientists and engineers,
including deceased physicist and
cosmologist Stephen Hawking and Elon Musk, the futurist behind PayPal, Tesla,
and SpaceX, believe that AI presents an existential threat – a bits and bytes
version of the asteroid that killed the dinosaurs 65 million years ago. Critics
allege two main problems with artificial intelligence:

• First, we are starting to create smart machines that
  think like humans but have no morality to guide their actions.
• Second, in the future, these smart machines will be able to
  procreate, producing even smarter machines, a process often referred to
  as “superintelligence”. Colonies of smart machines could grow at an
  exponential rate – a phenomenon
  for which mere people could not erect sufficient safeguards.¹⁶

As Always, Primary Security Is of Paramount Concern

As they evolve, smart machines must get smarter relative to security and
privacy, thwarting attacks along three primary threat vectors. As analyst Adam
Hadhazy explains:

• “The first [threat vector] involves a malevolent agent inserting bogus
  information into the stream of data that an AI system is using to learn – an
  approach known as ‘data poisoning.’
• “[The second threat vector] is called an evasion attack. It assumes a
  machine learning model has successfully trained on genuine data and
  achieved high accuracy at whatever its task may be. An adversary could
  turn that success on its head, though, by manipulating the inputs the
  system receives once it starts applying its learning to real-world
  decisions.
• “The [third threat vector] is privacy attacks. Adversaries can try to piggyback on machine learning models as they soak
  up data, gaining access to guarded information such as credit card
  numbers, health records, and users’ physical locations.”¹⁷

Smart Machines Need Ethics

Far from an abstract concern, Gartner believes that smart machine developers
must program basic human ethics into smart machines. “Clearly, people
must trust smart machines if they are to accept and use
them,” said Frank Buytendijk, research vice president at Gartner.
“The ability to earn trust must be part of any plan to implement … smart
machines, and will be an important selling point when marketing this technology. CIOs
must be able to monitor smart machine technology for unintended consequences of
public use and respond immediately, embracing unforeseen positive outcomes and
countering undesirable ones.”¹⁸

Consider, for example, the following scenario: a self-driving car encounters
a situation where a collision is imminent and unavoidable. Should the smart car
protect its occupants at the expense of other drivers, passengers, and
pedestrians, or should it seek to minimize human injury even at the expense of
its occupants? Potential dilemmas like these must be identified, and public- and
private-sector officials must agree – in advance – on the appropriate, i.e.,
legal, response to such deadly real-world problems.

An Organized Resistance Looms

While enterprise executives may encourage the use of smart machines, smart
machines will not enjoy universal popularity. Opponents will include:

• Labor Unions, whose members risk losing
  their jobs.
• Hacktivists, who view smart machines as
  a weapon for attacking the middle class.
• State and Non-State Cyber Warriors, who
  will seek to exploit smart machines, especially smart machine collectives
  like smart grids.
• Anti-AI Activists, who are anxious
  about the unchecked evolution of artificial intelligence systems.
• Immigration Opponents, who may liken the creation of smart robots
  with the influx of job-consuming undocumented workers.

Importantly, the proliferation of smart machines will prompt new security
threats, which, if history is any indication, will not be addressed before
serious smart
problems materialize.

Smart Machines Pose Challenges for Higher Education

Just as smart machines have implications for business, they have implications
for higher education, for preparing the next generation of business leaders.
Analyst Diana Oblinger believes it’s time for higher education leaders to ask
some critical questions. For example:

• Regarding data – “Do students have the appropriate mix of mathematics,
  statistics, and coding to understand how data is manipulated and how
  algorithms work?”
• Regarding the division of labor – “What role do collaborative platforms
  and collective intelligence have in how we develop and deploy expertise?”
• Regarding ethics – “An algorithm may represent a “trade secret,” but it
  might also reinforce dangerous assumptions or result in unconscious bias.
  What kind of transparency should we strive for in the use of algorithms?”¹⁹

Ten Smart Predictions for Smart Machines

1. The smart machine industry will mature faster than many enterprise
   executives believe, resulting in lost business opportunities. Sebastian Thrun, the inventor of Google’s
   self-driving cars,
   told the Financial Times that “almost every established industry is not moving
   fast enough” to adapt their businesses to the smart machine age.²⁰
2. Smart machine-related security problems will rival those experienced
   in the early days of e-commerce.
3. Smart machine failures, like commercial drone crashes, will be
   offered as evidence that smart machines are unnatural and unsafe.²¹
4. Labor unions will gain new members as they lead the fight against
   job-killing smart machines.²²
5. Progress in developing true artificial intelligence will disappoint,
   reassuring some that Terminator-style takeovers are unlikely.
6. Responding to public pressure over unemployment – or fear of
   unemployment – governments will attempt to enact anti-smart machine legislation.²³
7. The job training industry will receive a needed boost as workers displaced
   by smart machines seek an alternative career path.
8. Despite job training and other workforce stabilization initiatives, a study
   released by the World Economic Forum projects that increased automation and AI
   will lead to significant aggregate job loss.²⁴
9. As smart machines wipe out low-wage jobs in emerging markets like Brazil
   and India, political systems will become increasingly unstable, with the
   potential for violent confrontations between the economic “haves” and “have nots.”²⁵
10. Erik Brynjolfsson, a Massachusetts
    Institute of Technology professor and co-author of The Second Machine Age,
    asserts that “We’re moving to a world where there will
    be vastly more wealth and vastly less work. That shouldn’t be a bad thing, and
    shame on us if we turn it into a bad thing.”²⁶

Recommendations

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Be Optimistic

For those enterprise officials rightly concerned about smart machine-induced
job loss and its effect on employee welfare, the overall influence of today’s
technological progress on future employment, while disruptive, may, on the
whole, be positive. As analyst Thomas Black reminds us, “A World Economic Forum
survey concluded that while 75 million jobs may be displaced, 133 million new
roles may emerge that are more adapted to’ the division of labor among humans,
machines and algorithms.”²⁷

Be Open

Analyst Jenny Schecher encourages executives to be open and adaptive. “With smart machines emerging on the market,
new trends are being implemented into industries, which causes disruption and
creates a change among different sectors.

“In order for enterprises to survive this
shift of technology, it is important for them to remain reactive and open to
change by first understanding the benefits that can come from these machines and
how they operate.

“Then, industry leaders can understand how
these major advancements in technology can disrupt their industry.”²⁸

Implement Smartly

As
reported by Jeffrey Sachs, smart machines will disrupt the employer-employee
relationship. “Several … studies, including at Oxford University and
McKinsey, have tried to estimate the share of jobs that are likely to be up
for grabs by smart machines in the next 20 or so years. Each
occupation is analyzed for the kinds of tasks needed. Are they highly
repetitive or highly context-specific? Do they require highly
specialized mechanical skills, a high degree of interaction with others, or
a high measure of emotional empathy? And so on. From this
categorization of job tasks, the researchers estimate the share of jobs that
can be substituted by robots and artificial intelligence systems. Their answer:
Roughly half of today’s jobs are susceptible to at least
some kinds of replacement by smart machines.“²⁹

Before introducing smart machines into the workplace, enterprise planners
should conduct a Workforce
Impact Analysis.

• Will the machines displace any workers?
• Can displaced workers be reassigned to other
  functions? If so, are there any provisions for retraining? Also,
  are there any impediments to retraining, such as cost?
• What type and level of supervision do the new
  machines require? Can current staffers be trained to provide such
  oversight?
• What resistance – if any – will workers
  present? Be realistic about all possibilities. For example, is sabotage or other forms
  of workplace violence a possibility? Can workers’ fears and
  resentments be mitigated through education or other means?
• Since all enterprises are different, what is the best strategy for
  implementing smart machines, both locally and on an enterprise-wide basis?

The Workforce Impact Analysis should be performed in cooperation with the
Human Resources and Risk Management departments.

Start Small

Begin smart machine integration with a “pilot project.” A pilot has
the advantage of testing
assumptions about smart machine operations, enabling fine-, or even
course-,
tuning as necessary. A pilot provides a “try it before you buy it”
environment where resources, especially financial, can be conserved.
Finally, a pilot offers an opportunity to gather worker feedback,
potentially identifying methods for implementing smart machines in a
manner
that complements workers’ labor styles.

Think Reversion

Borrowing from application development best practices, consider how
a
particular smart machine implementation could go wrong, and devise a
reversion, or reverse installation, plan. Also, consider the possible
business effects of a failed implementation, and fashion a recovery
plan. Consult with the Risk Management and Business Continuity
departments to
determine how to best “undo” a smart machine deployment.

Web Links

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• Association for the Advancement of Artificial Intelligence: http://www.aaai.org/
• Defense Advanced Research Projects Agency: http://www.darpa.mil/
• Machine Intelligence Research Institute: http://www.intelligence.org/
• MIT Computer Science and Artificial Intelligence Laboratory: http://www.csail.mit.edu/
• US National Institute of Standards and Technology: http://www.nist.gov/

References

¹ Margaret Rouse. “Smart Machines.”
TechTarget.

² Murad Ahmed. “Davos: Smart Machines Set to Transform Society.”
Financial Times. January 20, 2016.

³ Margaret Rouse. “Smart Machines.”
TechTarget.

⁴ Paul Clarke. “When the Internet of Things and Smart Machines Collide.”
Wired. March 2015.

⁵ Dave Perkon. “The Basics of Smart
Machines.” Control Design. November 1, 2019.

⁶ “Global Smart Machines Market 2021-2026 – Integration of
Robotics, AI and IoT a Substantial Smart Machine Market Opportunity for Service
Automation.” Research and Markets.
February 17, 2021

⁷ “Smart Grid 101: The Smart Grid.”
Smart Grid News. January 20, 2010.

⁸ Brett Molina. “Robots Are Better at Reading Than Humans.”
USA Today. January 16, 2018.

⁹ Hong Cheng, Lukasz A. Drozd, Rahul Giri, Mathieu
Taschereau-Domouchel, and Junjie Xia. WP 21-11: “The Future of Labor: Automation
and the Labor Share in the Second Machine Age.”
Federal Reserve Bank of Philadelphia. March 3, 2021:43.

¹⁰ Paul Clarke. “When the Internet of Things and Smart Machines Collide.”
Wired. March 2015.

¹¹ Tony Rhem. “IoT in the Age of Smart Machines.”
knowledgemanagementdepot.com. February 28, 2021.

¹² Kevin Roose. “His 2020 Campaign Message: The Robots Are Coming.”
The New York Times. February 10, 2018.

¹³ David Schatsky, Jonathan Camhi, and Aniket Dongre. “Pervasive Intelligence:
Smart Machines Everywhere.” Deloitte LLP. November 7,
2018.

¹⁴ Erico Guizzo and Randi Klett. “How Robots Became Essential
Workers in the COVID-19 Response.” IEEE. September 30, 2020.

¹⁵ John Soldatos. “Smart Machines and the Future of Enterprise Industrial
Maintenance.” Prometheus Group. September 11, 2019.

¹⁶ Nick Bilton. “Artificial Intelligence as a Threat.”
The New York Times. November 5, 2014.

¹⁷ Adam Hadhazy. “Protecting Smart Machines from
Smart Attacks.”
The Trustees of Princeton University. October 10, 2019.

¹⁸ “Gartner Says Smart Machines Require Ethical Programming.”
Gartner. March 17, 2015.

¹⁹ Diana Oblinger. “Smart Machines and Human Expertise:
Challenges for Higher Education.” EDUCAUSE. August 27, 2018.

²⁰ Murad Ahmed. “Davos: Smart Machines Set to Transform Society.”
Financial Times. January 20, 2016.

²¹ “Gartner Says Smart Machines Will Have Widespread and Deep Business Impact Through 2020.”
Gartner. October 10, 2013.

²² Ibid.

²³ Ibid.

²⁴ Murad Ahmed. “Davos: Smart Machines Set to Transform Society.”
Financial Times. January 20, 2016.

²⁵ Ibid.

²⁶ Ibid.

²⁷ Thomas Black. “Will Smart Machines Kill Jobs or Create Better
Ones?” Bloomberg L.P. December 18, 2020.

²⁸ Jenny Schecher. “Everything You Need to Know about Smart Machines.” Millennium Alliance. August 29, 2017.

²⁹ Jeffrey D. Sachs. “Smart Machines and the Future of Jobs.”
Boston Globe. October 10, 2016.

About the Author

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James G. Barr is a leading business continuity analyst and

business writer with more than 30 years’ IT experience. A member of

“Who’s Who in Finance and Industry,” Mr. Barr has designed,

developed, and deployed business continuity plans for a number of Fortune

500 firms. He is the author of several books, including How to

Succeed in Business BY Really Trying, a member of Faulkner’s Advisory

Panel, and a senior editor for Faulkner’s Security Management

Practices. Mr. Barr can be reached via e-mail at jgbarr@faulkner.com.

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