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Industrial Automation - Engine and Motor

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Industrial Automation - Engine and Motor

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This exhibit explores how the development of different automation changed the realm of employment and job opportunities in our society. Automation refers to technologies that can substitute or reduce human intervention and labor in different tasks (Acemoglu & Restrepo, 2019, p. 3). The development of different types of automation has provoked many debates about whether automation will replace human jobs, which leads to job or labor displacement. A study's finding by Acemoglu and Restrepo (2022) suggests that robots have negative effects on employment and wages, where "one new robot [reduces] employment by 5.6 workers as opposed to 6.2 workers without trade" (p. 4). Many workers in different industries see automation as a threat as they feel anxious about losing their jobs due to the increase in automation.

A recent example is the rise of generative artificial intelligence (AI) art from text prompts in the art and creative industry in 2021. Generative AI is within the area of cognitive automation. According to Engel et al. (2022), cognitive automation can be referred to as “automating knowledge and service work to realize value offered by AI, which is based on implementing artificial cognition that mimics and approximates human cognition in machines” (p. 339). There are a number of AI art generators on the Internet, such as OpenAI’s DALL-E 2 and Image Creator by Microsoft Designer, that every Internet user can easily use to generate images by inputting a text prompt, and it will deliver the desired image. Some artists called AI-generated art fake art due to how easily AI art generators can create the desired image within seconds using just text. Unlike how AI art generators can create images in seconds, human artists need to go through a much longer process that includes researching, finding references, creating sketches and roughs, and going to production. The difference in production time used to create desired images between AI art generators and human artists within the creative industry leads to human artists worrying that AI art generators will displace their jobs. If it is from the perspective of computer hackers from the 1900s, the AI art generators would fit the hacker ethic of "you can create art and beauty on a computer" (Turner, 2006). AI art generators can also be seen as a way to provide "freedom" to art creation, as anyone with or without knowledge or skills to create art can use the generator to create an image they want.

Another example of automation and human labor is industry machines and factory workers. Charles Babbage's invention of the Difference Engine replaced individual human intelligence, which is human computers, with machine intelligence. However, according to Schaffer (1994), even if factories adopted the automatic system, it is difficult to see the factories themselves as intelligent (p. 222). Therefore, workers are needed to manage the automatic factory, which was formed by "mechanical and intellectual organs," and workers would work using machines and look after the machines (Marx, 1867, p. 544).

The Gutenberg Myth can be used to explain the phenomena of individuals worrying that a single development of automation will displace human labor. According to Cook (1995), the Gutenberg myth refers to "a single technology being the sole cause of rapid and far-reaching social change" (p. 78). Where people see the Gutenberg Press as the cause of the revolution without understanding other factors, such as the development of paper, the invention of the Linotype, and people's literacy, played a role in the printing revolution; with all these factors, it took over hundreds of years after the invention of the Gutenberg Press to have a printing revolution. The idea of Gutenberg's myth can reflect the idea that people think one technological invention would completely displace humans in the workplace. For example, thinking about the development of Generative AI as the reason content creators will be displaced without thinking about other developments, such as the computer, the hardware in the computer, and the Internet. Automation development enhances productivity and improves accuracy and quality by eliminating some manual labor; however, a single automation will not completely displace human workers.

Each subsection of the exhibit explores a different area of automation’s development and its relationship with the human role. Throughout the development of different automation from the past, humans have remained a critical part of the automation workflow. The subsections were organized from the least feasible automation area to the most feasible automation area that an individual can access and can be seen or found on a daily basis. Then, within each subsection, they were organized in chronological order based on the development of different automation in different areas.

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“A panoramic view of the water wheel that powering the foundry machinery at Finch Foundary in Devon, UK.” (Wikipedia Commons, “File:Finch Foundary Water Wheel, Devon, UK - Diliff.jpg,” accessed April 21, 2024)

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During the Middle Ages, waterwheels were automated devices that people used to generate power, grind into flour, and use in metal foundries (Teaching Science, 2015, "Constructing a waterwheel," p. 56). “A wooden water wheel needs regular maintenance to increase its lifetime” (Theodore R. Hazen, "The Operation and Maintenance of a Traditional Wooden Water Wheel," accessed April 21, 2024); this opens new opportunities for people to do the maintenance job.

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“Caprington Colliery Newcomen engine in the National Museum of Scotland.” (Wikipedia, “Newcomen atmospheric engine,” accessed April 21, 2024)

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Thomas Newcomen's steam engine was the first engine that could generate power for machines. (National Museum of Scotland, "Newcomen atmospheric engine," accessed April 21, 2024). Steam engines need maintenance, such as cleaning, and humans must frequently refuel them to keep them running.

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“Fully operational difference engine at the Computer History Museum in Mountain View, California” (Wikipedia, “Difference Engine,” accessed April 21, 2024)

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Difference Engine (1832) - Charles Babbage

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Difference Engine (1832) - Charles Babbage

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The Difference Engine replaced human computers in calculating polynomial functions; it operates by cranking a handle (Martin, 2004, Computer: A History of the Information Machine). Although the Difference Engine did replace human labor, it still needs humans to operate. Humans' role changed from human-computer to engine operator.

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“A model of Nikola Tesla’s first induction motor at the Tesla Museum in Belgrade, Serbia” (Wikipedia, “Induction Motor,” accessed April 21, 2024)

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Electric Motor: induction motor (1887-1888) - Nikola Tesla

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Electric Motor: induction motor (1887-1888) - Nikola Tesla

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Nikola Tesla's Electric Motor, the AC induction motor, is the common industrial motor that most industrial machines use (Oregon State University, "Common Industrial Motor Types," accessed April 21, 2024). Industrial machines need humans to manage and maintain them regularly, and the AC induction motor also needs weekly inspections, such as listening to unusual noises and cleaning (Global Electronic Services, "AC Motor Maintenance Tasks and Their Recommended Intervals," accessed April 21, 2024).

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Vehicular Automation - Self-driving cars

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Vehicular Automation - Self-driving cars

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“A self-propelled cart replica at museum Clos Lucé” (Wikipedia, “Leonardo’s self-propelled cart,” accessed April 21, 2024)

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Self-propelled Cart (1478) - Leonardo da Vinci

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Self-propelled Cart (1478) - Leonardo da Vinci

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Leonardo da Vinci invented a self-propelled cart that can move without being pushed. It is powered by coiled springs and features steering and brake capabilities(Leonardo da Vinci Inventions, "Self-Propelled Cart," accessed April 21, 2024). Even though the cart can move without applying a person’s force, it still needs human input to release a brake to move.

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The development of industrial automation is important within our society as it brings a range of benefits to businesses, the economy, and different individuals by increasing productivity, improving quality control, and reducing costs. The benefits of industrial automation did eliminate some manual labor, but the need for humans to manage and maintain the automation will not completely displace human workers, as can be seen in the development of industrial automation.

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The development of self-driving cars brings benefits, including increasing road safety and reducing driver workload. Drivers were worried self-driving cars would eliminate their driving jobs. However, based on the development of self-driving cars, they will not completely displace humans as they need to be regularly maintained and supervised by humans.

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“The radio-operated automobile, American Wonder, 1925” (Wikipedia, “Houdina Radio Control,” accessed April 21, 2024)

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Radio-operated automobile (1925) - Houdina Radio Control Co

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Radio-operated automobile (1925) - Houdina Radio Control Co

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The American Wonder is a radio-operated automobile that uses radio waves to control a car (Time, "Science: Radio Auto," accessed April 21, 2024). People do not need a steering wheel to control this car, but this autonomous car still needs humans to control it through a controller.

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“Tesla Model S prototype at the 2009 Frankfurt Motor Show” (Wikipedia, “Tesla Model S,” accessed April 21, 2024)

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Tesla Model S offers an Autopilot driving system that includes features such as Traffic-Aware Cruise Control and Autosteer (Tesla, "Autopilot and Full Self-Driving Capability Features," accessed April 21, 2024). However, the Autopilot system can only be used as a driver assistance system. Humans need to supervise Model S in case of unexpected events on the road.

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“A Waymo self-driving car parked on a street in San Francisco on May 1, 2023” (USA Today, “Waymo driverless car set ablaze in San Francisco: ‘Putting out some rage," accessed April 21, 2024)

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MIT-spinoff NuTonomy made the first Robotaix available to the public in 2016 (The Verge, "World's first self-driving taxi trial begins in Singapore," accessed April 21, 2024). Robotaxi uses specialized cameras and other sensors to navigate, process data, and interact with the environment in real-time (Infosys BPM, "Fully automated driving and the rollout of robo-taxis," accessed April 21, 2024). Robotaxi can fully operate independently; however, it still needs a backup human driver to supervise it for safety reasons.

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Cognitive Automation - Content Creation 

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Cognitive Automation - Content Creation 

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Generative AI has the ability to generate desired images or text-based content, reducing the time and cost of the content creation process. Occupations within the content creation industry, such as graphic designers and media jobs, see generative AI as a threat to their jobs. Based on the development of generative AI, content creation will not completely displace human creators, as generative AI needs AI training by feeding data, updating data sets, and requiring human oversight.

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“The 1979 exhibition, Drawings, at SFMOMA, featured this “turtle” robot creating drawings in the gallery. Collection of the Computer History Museum, 102627449.” (Computer History Museum, “Harold Cohen and AARON —A 40-year Collaboration," accessed April 21, 2024)

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To generate drawings, Cohen created a set of rules and forms that the computer would interpret; then, the robot equipped with a marker would draw on a piece of paper (Computer History Museum, “Harold Cohen and AARON—A 40-year Collaboration," accessed April 21, 2024). Even though humans did not draw the drawings by hand, the robot needed a human's command to create the art.

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"Representing images on multiple layers of abstraction in deep learning" (Wikipedia, “Deep learning,” accessed April 21, 2024)

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Deep learning is "a subset of machine learning that uses multi-layered neural networks;" it eliminates some of the pre-processing steps before feeding data, as it can learn from unstructured data (IBM, “What is deep learning?” accessed April 21, 2024). Humans need to organize a large dataset, images, or text for deep learning to train on.

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"The CLIP training architecture" (Viso.ai, “CLIP: Contrastive Language-Image Pre-Training (2024),” accessed April 21, 2024)

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CLIP (Contrastive Language-Image Pretraining) (2020) - OpenAI 

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CLIP (Contrastive Language-Image Pretraining) (2020) - OpenAI 

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CLIP is an AI model that "learns visual concepts from natural language descriptions" (Viso.ai, “CLIP: Contrastive Language-Image Pre-Training (2024),” accessed April 21, 2024). In the training stage, CLIP collects data from the Internet. (Geeks for Geeks, "CLIP (Contrastive Language-Image Pretraining," accessed April 21, 2024). Because the data set is from the Internet, CLIP needs human oversight to prevent generating false information and using biased or offensive language.

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ChatGPT Logo (Wikipedia, “ChatGPT,” accessed April 21, 2024)

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ChatGPT is an AI chatbot that generates output using a "Generative Pre-trained Transformer" (GPT); its training dataset also comes directly from the Internet (Harvard Univerity, “The Making of ChatGPT: From Data to Dialogue,” accessed April 21, 2024). ChatGPT can generate images and text, such as emails. Because the data set is from the Internet, ChatGPT also needs humans to oversee the output.

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Acemoglu, D., & Restrepo, P. (2019). Automation and New Tasks: How Technology Displaces and Reinstates Labor. The Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3

Acemoglu, D., & Restrepo, P. (2020). Robots and Jobs: Evidence from US Labor Markets. The Journal of Political Economy, 128(6), 2188–2244. https://doi.org/10.1086/705716

Cook, S. D .N. (1995). The Structure of Technological Revolutions and the Gutenberg Myth. In  Pitt, J.C. (eds), New Directions in the Philosophy of Technology. Philosophy and Technology, vol 11 (pp. 67 - 82). Springer, Dordrecht. https://doi.org/10.1007/978-94-015-8418-0_4

Engel, C., Ebel, P., & Leimeister, J. M. (2022). Cognitive automation. Electronic Markets, 32(1), 339–350. https://doi.org/10.1007/s12525-021-00519-7

Marx, K. (1867). Capital, Volume I. Verlag von Otto Meisner.

Schaffer, S. (1994). Babbage's Intelligence: Calculating Engines and the Factory System. Critical Inquiry, 21(1), 203–227. https://doi.org/10.1086/448746

Turner, F. (2022). How Digital Technology Found Utopian Ideology: Lessons from the First Hackers’ Conference. In Critical Cyberculture Studies (pp. 255–269). New York University Press. https://doi.org/10.18574/nyu/9780814708903.003.0026