Defining Automation

Automation is the technique of making an apparatus, a process, or a system operate automatically and can further be defined as the creation and application of technology to monitor and control the production and delivery of products and services.[i] Recent economic analysis of automation is often tied to “machine learning” or Artificial Intelligence and machine robotics. Artificial intelligence is the study and design of intelligent agents where an intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success.[ii] Artificial intelligence involves a machine mimicking “cognitive” functions that humans associate with other human minds, such as problem solving.

Artificial Intelligence has been defined in a number of ways, including:

  1. systems that think like humans (e.g., cognitive architectures and neural networks);
  2. systems that act like humans (e.g., pass the Turing test via natural language processing; knowledge representation, automated reasoning, and learning);
  3. systems that think rationally (e.g., logic solvers, inference, and optimization); and
  4. systems that act rationally (e.g., intelligent software agents and embodied robots that achieve goals via perception, planning, reasoning, learning, communicating, decision-making, and acting).[iii]

A recent White House report on Artificial Intelligence noted venture capitalist Frank Chen broke down Artificial Intelligence into five general categories: logical reasoning, knowledge representation, planning and navigation, natural language processing, and perception.[iv]

Synonyms for Artificial Intelligence include computational intelligence, synthetic intelligence or computational rationality.[v] Artificial Intelligence is based upon research from a range of fields including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, operations research, economics, control theory, probability, optimization and logic.[vi] Also, Artificial Intelligence also is closely connected with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others.[vii]

John McCarthy is credited with coining the phrase in 1956 defines Artificial Intelligence as “the science and engineering of making intelligent machines.”[viii] However, the roots of Artificial Intelligence go back to at least the 1940s and the concept was crystalized in Alan Turing’s famous 1950 paper, “Computing Machinery and Intelligence.”[ix] Turing’s paper posed the question: “Can machines think?” proposed a test for answering that question, and raised the possibility that a machine might be programmed to learn from experience much as a young child does.[x] However, as with many technological advances, the dreams of Artificial Intelligence took a while to be realized. It really want not until the 1990s that Artificial Intelligence research could be seen as addressing real world issues.

Currently, Artificial Intelligence can be broken down into a couple different categories. Narrow Artificial Intelligence addresses specific application areas such as playing strategic games, language translation, self-driving vehicles, and image recognition, and it underpins many commercial services such as trip planning, shopper recommendation systems, and ad targeting, and is finding important applications in medical diagnosis, education, and scientific research.[xi] General Artificial Intelligence is a notional future system that exhibits apparently intelligent behavior at least as advanced as a person across the full range of cognitive tasks.[xii]

The concept of Machine Learning is also critical to understand. Machine Learning is a statistical process that starts with a body of data and tries to derive a rule or procedure that explains the data or can predict future data.[xiii] Learning from data to create Artificial Intelligence contrasts with the older approaches in which computer programmers sit down with human domain experts to learn the rules and criteria used to make decisions, and translate those rules into software code.[xiv] An expert system aims to emulate the principles used by human experts but Machine Learning uses statistical methods to find a decision procedure that works well in practice.[xv] Machine learning involves computerized algorithms that have taken the place of human transactions. As an example, Machine Learning serves stock exchanges– where high-frequency trading by machines has replaced human decision-making as machines can spot trading inefficiencies or market differentials at a very small scale and execute trades that make money for people.[xvi]

A robot is defined as a machine capable of moving independently (as by walking or rolling on wheels) and performing complex actions (such as grasping and moving objects) that could be capable of independent thought but is primarily focused on repetitive tasks like work on an assembly line.[xvii] The use of robots is growing across the world. In 2013, there were an estimated 1.2 million robots in use and this number grew to 1.5 million in 2014 and is projected to increase to about 1.9 million in 2017.[xviii] Japan has the largest number with 306,700, followed by North America (237,400), China (182,300), South Korea (175,600), and Germany (175,200).[xix] Overall, robotics is expected to rise from a $15 B industry sector now to $67 B by 2025.[xx]

Robots and the field of robotics are often tied to the automation of the manufacturing industry and its production process. It has been established that three types of automation in production can be distinguished: (1) fixed automation, (2) programmable automation, and (3) flexible automation.[xxi] Fixed automation is an automated production facility in which the sequence of processing operations is fixed by the equipment configuration with programmed commands contained in the machines in the form of cams, gears, wiring, and other hardware that is not easily changed over from one product style to another.[xxii] Programmable automation produces products in batches where with each new batch, the production equipment must be reprogrammed and changed over to accommodate the new product style.[xxiii] Industrial robots are often used with programmable automation.[xxiv] Finally, flexible automation is similar to programmable automation but with a variety of limited products so that the changeover of the equipment can be done very quickly and automatically.[xxv]

The impact of robots on manufacturing may pale in comparison to how 3-D or Additive Printing impacts what used to be the heart of the American economy. 3-D Printing is a way for software to send design plans to specialty printers and have those devices make exact copies of those goods or products.[xxvi] The ability to print highly durable material impacts product manufacturing and delivery and dramatically reduce the cost and workers required for assembling products in a manufacturing setting.





[iii] Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (3rd Edition) (Essex, England: Pearson, 2009).



[vi] Ibid.

[vii] Ibid.

[viii] Ibid.


[x] Ibid.

[xi] Ibid.

[xii] Ibid.

[xiii] Ibid.

[xiv] Ibid.

[xv] Ibid.



[xviii] Alison Sander and Meldon Wolfgang. “The Rise of Robotics.” The Boston Consulting Group, August 27, 2014.

[xix] RBC Global Asset Management, “Global Megatrends: Automation in Emerging Markets,” 2014.

[xx] Sander and Wolfgang.


[xxii] Ibid.

[xxiii] Ibid.

[xxiv] Ibid.

[xxv] Ibid.


Categories: Automation, ED Planning