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New Age Of Convergence:
Industrial Robotics & AI/ML

Disentangling the Convergence of
Robotics, AI/ML & Automation

“Smart robots” are robots that integrate machine learning and artificial intelligence to continuously improve the robots’ performance. 

Excerpted from:
Primer on Artificial Intelligence and Robotics
Manav Raj and Robert Seamans
Journal of Organization Design

Part 1.

Scholars have been increasingly interested in the economic, social, and distributive implications of artificial intelligence, robotics, and other types of automation.
Technological change can bring about great progress but also great turmoil. For example, while the steam engine led to great economic growth (see, e.g., Crafts 2004) it also led to job displacement. It is important for organizations to understand and anticipate the effects that artificial intelligence, robotics, and other types of automation may have, and design themselves accordingly.

While many lessons can be drawn from prior episodes of automation, it is possible that artificial intelligence and robotics may have unique consequences.

Differences from prior episodes of automation include that (1) the nature of business activity has shifted dramatically over the past decade such that many businesses now rely on platform (i.e., 2-sided market) business models, (2) artificial intelligence is likely to affect white-collar workers more so than blue-collar workers (while perhaps robotics may affect blue-collar workers more than white-collar workers), and (3) artificial intelligence may affect the links between establishments and firms (e.g., monitoring and firm scope).

Artificial Intelligence, Robotics & Automation

We discuss implications of these technologies on organizational design, then describe areas in which organizational scholars can make substantial contributions to our understanding about how artificial intelligence and robotics are affecting work, labor, and organizations. We also describe ways in which organizational scholars have been using artificial intelligence tools as part of their research methodology.

Studies of artificial intelligence and robotics base their theory and analysis on constructs of automation, robotics, artificial intelligence and machine learning, and automation.

In this body of literature, use of robotics, artificial intelligence, and machine learning technologies can be used both as independent and as dependent variables—as dependent variables to examine factors that encourage or discourage the adoption and use of these technologies and independent variables to see how the use of these technologies impacts a variety of outcomes, such as effects on labor, productivity, growth, and firm organization.

The definitions below are meant to be a helpful first step in such an endeavor.

The International Federation of Robots (IFR), an international industrial group focused on commercial robotics, defines an industrial robot as an “automatically controlled, reprogrammable, multipurpose manipulator, programmable in three or more axes, which can be either fixed in place or mobile for use in industrial automation applications.”

While this definition is a starting point, other roboticists may differ on dimensions such as whether a robot must be automatically controlled or could be autonomous or whether a robot must be reprogrammable. At a broader level, any machine that can be used to carry out complex actions or tasks in an automatic manner may be considered a robot.

Artificial Intelligence and Machine Learning

Similar to robotics, artificial intelligence is a construct with varying definitions and potentially broad interpretations. For starters, it is useful to distinguish between general and narrow artificial intelligence (Broussard 2018). “General artificial intelligence” refers to computer software that can think and act on its own; nothing like this currently exists.

“Narrow artificial intelligence” refers to computer software that relies on highly sophisticated, algorithmic techniques to find patterns in data and make predictions about the future. In this sense, the software “learns” from existing data and hence is sometimes referred to as “machine learning” but this should not be confused with actual learning.

Broussard (2018) writes that “machine ‘learning’ is more akin to a metaphor…: it means that the machine can improve at its programmed, routine, automated tasks. It doesn’t mean that the machine acquires knowledge or wisdom or agency, despite what the term learning might imply [p. 89].”

Many applications of machine learning focus on prediction and estimation of unknowns based on a given set of information (Athey 2018; Mullainathan and Spiess 2017). There are a variety of algorithms that can be used for this machine learning.

Some of these techniques are relatively straightforward uses of logit models which would be familiar to most organizational scholars, whereas others involve highly sophisticated algorithms that attempt to mimic how a human brain looks for patterns in data (the latter are called “neural networks”).

Artificial intelligence technology can be used towards a variety of purposes, including playing abstract strategy games such as Chess or Go; to playing real-time video games such as Atari, Asterix, or Crazy Climber; to image or street number recognition; to natural language translation; and many other uses.

Automation refers to the use of largely automatic, likely computer-controlled, systems and equipment in manufacturing and production processes that replace some or all of the tasks that previously were done by human labor. Automation is not a new concept, as innovations such as the steam engine or the cotton gin can be viewed as automating previously manual tasks. One of the concerns for scholars in this area revolves around how and in what contexts increased use of robotics and artificial intelligence technology may lead to increased automation, and the impact that this form of increased automation may have on the workforce and the design of organizations.

Disentangling Artificial Intelligence, Robotics & Automation

While artificial intelligence, robotics, and automation are all related concepts, it is important to be aware of the distinctions between each of these constructs.

Robotics is largely focused on technologies that could be classified as “manipulators” as per the IFR definition, and accordingly, more directly relates to the automation of physical tasks.

On the other hand, artificial intelligence does not require physical manipulation, but rather computer-based learning. The distinction between the two technologies can become fuzzier as applications of artificial intelligence may involve robotics or vice versa. For example, “smart robots” are robots that integrate machine learning and artificial intelligence to continuously improve the robots’ performance. Both artificial intelligence and robotics technologies are capable of automation.

However, an open question is how and whether the effects of automation may differ across the two technologies. Some scholars contend that computerization and the increased use of artificial intelligence have the potential to automate certain non-routine tasks compared to the more rote tasks previously subjected to automation (Frey and Osborne 2017; Autor et al. 2006).

Accordingly, it is possible that technologies incorporating artificial intelligence may be able to automate far more tasks than pure robotics-based technologies.

Importantly, even though a technology such as artificial intelligence or robotics may automate some of the tasks previously done by human labor, it does not necessarily imply that the human has been automated out of a job. In many cases, a computer or robot may be able to complete relatively low-value tasks, freeing up the human to focus efforts instead on high-value tasks. In this sense, artificial intelligence and robotics may augment the work done by human labor.