Human Thinking and Artificial Intelligence
“To Err is Human; (Croskery, 2010)”
This famous phrase from line 525 of Alexander Pope’s “An Essay on Criticism” in the early 18th century demonstrates that it has been long understood human error is unique to the process of development within our species.
Intelligence is all around us from computers to the natural world. Whether intelligence is manufactured or occurs in an organic state, the concept of being considered “intelligent” does not change. Artificial intelligence is an experiment of the “artificial life” (which denotes a range of computational techniques that emulate an aspect of living systems, [Guastello, p. 330, 2013]) research community that should be more aptly named “non-carbon-based intelligence” or “engineered intelligence.” A computer is intelligent, but it is this idea that creates fear in the absence of clarification that while we as humans are also intelligent, we are unique in our ability to go beyond deep learning to reach a deep understanding.
Human thinking cannot be replaced by engineered intelligence. Based on Henning Beck’s TEDx from 2016, learning is not unique to humans. It can be proved in animals, trees, and machines; however, these non-carbon-based machines use progressive algorithms to respond to the input-to-output relationship. The output of human thought is the action of the process itself. The idea that the errors present in the human process are pinnacle to thought development and cannot be replicated, even with current computer systems based on input-process-output deep learning models, creates a clear distinction between human thinking and intelligence, which can be defined as “ability to learn, understand, and make judgments (Cambridge Dictionary, 2022).”
With the advent of the ideas of Social Cognitive and Affective Neuroscience (SCAN) that “learning has cognitive processes typically involved in education and training such as learning, memory, attention, and decision-making are in fact highly interdependent with social and emotional processes, (Wilcher & Fore, 2014)” concepts such as Skinner’s operant conditioning (Goldstein, p. 11, 2018) are merely foundational works for the future of human-machine interface. Despite the limitations of the current operatives, SCAN holds possibilities for better understanding the learning process which could be key to unlocking how the human mind uses sensory perception to generate ideas. Without objective observations and adequate test environments, this area of study is open for development (Wilcher & Fore, 2014). Along with Neisser’s perspectives regarding electrophysiology and Palmer’s study regarding how the environment can influence perception (Goldstein, pp. 18-19, 2018), the idea that there is more to human thinking than progressive algorithms is shown to elicit solid support.
As a result, it can be concluded that a functioning system can be seen as intelligent in producing output resulting from progressive algorithms, but does not “think” as though a human would with a deep understanding that gives rise to thought and idea generation.
The deciding question I pose is: “Should we continue to pursue the re-creation of what exists as “human” or continue to question the origin of existence while working in harmony to develop better interfaces for human-human and human-machine interactions?” This is where I feel that protection of the “human factor” is necessary on all levels from the government to individual lives.
Beck, H. (2016). What is a Thought? How the Brain Creates New Ideas. Retrieved October 22, 2022, fromhttps://www.youtube.com/watch?v=oJfFMoAgbv8&t=1sLinks to an external site..
Croskerry P. (2010). To err is human–and let’s not forget it. CMAJ : Canadian Medical Association journal = journal de l’Association medicale canadienne, 182(5), 524. https://doi.org/10.1503/cmaj.100270Links to an external site.
Goldstein E. B. (2018). Cognitive psychology: connecting mind research and everyday experience (5th ed.). 11-19. Wadsworth Cengage Learning.
Guastello, S. J. (2013). Human factors engineering and ergonomics: A systems approach, second edition. 330-333. Taylor & Francis Group.
Intelligence. INTELLIGENCE definition | Cambridge English Dictionary. (2022). Retrieved October 22, 2022, from https://dictionary.cambridge.org/us/dictionary/english/intelligenceLinks to an external site.
Wiltshire, T. J., & Fiore, S. M. (2014). Social Cognitive and affective neuroscience in human-machine systems: A roadmap for improving training, Human-Robot Interaction, and team performance. IEEE Transactions on Human-Machine Systems, 44(6), 779–787. https://doi.org/10.1109/thms.2014.2343996Links to an external site.