Intelligence Simulation Essay

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Successful Simulation of Intelligence is Essentially Equivalent to the Possession of Intelligence

Researchers have been attempting to develop artificial intelligence for more than a half century now, but it has become increasingly apparent that intelligence is a multidimensional construct that is incredibly difficult or perhaps even impossible to truly simulate. As the review that follows below will demonstrate, the assertion that successful simulate of intelligence is essential equivalent to the possession of intelligence is erroneous due to the multidimensionality of intelligence as well as its other nebulous qualities that preclude successful simulation being the essential equivalent to the possession of intelligence (Pogio & Meyers, 2016). This paper reviews the relevant literature to show that the Turing test and Chinese Room argument fail to provide an adequate response to this claim. Finally, a discussion concerning whether the successful simulation of human reasoning is in principle possible and whether the answer to this question is inherently dependent on the answer to the main question is followed by a summary of the research and important findings concerning the Turing test and Chinese Room argument as they relate to the possession of intelligence are provided in the conclusion.

Is this claim equivalent to the Turing test? 

The test developed by Alan Turing on intelligence, commonly known as the “Turing test,” was an important development in the ongoing effort to create true artificial intelligence (Shieber, 2016). In sum, the Turing test attempts to produce artificial responses that mimic human communications to the extent that humans are “fooled” into thinking that the machine is another person by holding a conversation (Blackmore, 2011). The manner in which the Turing test tries to achieve the goal, however, is fraught with constraints that do not make it the equivalent to the possession of intelligence. As Marcus and Rossi point out, “Within the field, the test is widely recognized as a pioneering landmark, but also is now seen as a distraction, designed over half a century ago, and too crude to really measure intelligence” (2016, p. 3). Indeed, given its complex nature, many researchers believe that even the best tests cannot accurately measure human intelligence, and developing computer-based applications that can simulate intelligence are therefore limited by a lack of definitional clarity.
In this regard, Marcus and Rossi conclude that, “Intelligence is a multidimensional variable, and no one test could possibly ever be definitive truly to measure it” (2016, p. 4). 

While the Turing test represented an innovative approach when it was developed, the test has since become more of an “exercise in deception” instead of a “true measure of anything especially correlated with intelligence” (Marcus & Rossi, 2016, p. 4). As an example, Marcus and Rossi cite the chatbot “Eugene Goostman” who purportedly was the first AI application to pass the Turing test by deceiving one-third of a panel of judges into believing it was a 13-year-old Ukrainian boy with limited English fluency. The program even responded appropriately to a joke from a human judge, an attribute that Hofstadter (1985) maintains is an essential element of intelligence. 

According to a report from Aamonth (2014), this seminal event essentially confirmed Turing’s original prediction that AI would become sufficiently advanced by 2000 to trick humans into believing they were other humans at least 30% of the time. The Goostman chatbot, however, succeeded in deceiving human judges by “mainly ducking questions and returning canned one-liners; it cannot see, it cannot think, and it is certainly a long way from genuine artificial general intelligence” (Marcus & Rossi, 2016, p. 4). The ability to “return canned one-liners” is also a characteristic of the Chinese Room argument that has also been used to underscore the fundamental differences between simulating intelligence and possessing true intelligence as discussed further below.

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Aamonth, D. (2014, June 9). Interview with Eugene Goostman, the fake kid who passed the Turing test. Time. Retrieved from

Blackmore, S. (2011). Consciousness:  An introduction. New York:  Oxford University Press.

Churchland, P. M. & Churchland, P. S. (1990, January). Could a machine think? Scientific American, 32-37.

Hofstadter, D. R. (1985). Metamagical themas: Questing for the essence of mind and pattern. New York:  Basic Books.

Marcus, G. & Rossi, F. (2016, April 1). Beyond the Turing test. AI Magazine, 37(1), 3-5.

Phelps, B. (2011, January 1). How close to real can a non-real cyberrat behave? Behavior and Philosophy, 39/40, 309-312.

Poggio, T. & Meyers, E. (2016, Spring). Turing++ questions: A test for the science of (human) intelligence. AI Magazine, 37(1), 73-75.

Ray, R. D. (2011, January 1). Cyberrat, interbehavioral systems analysis, and a "Turing test" trilogy. Behavior and Philosophy, 39/40, 203.

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Searle, J. R. (1980). Minds, brains, and programs. Behavioral and Brain Sciences, 3, 417-457.

Shieber, S. M. (2016, Spring). Principles for designing an AI competition, or why the Turing test fails as an inducement prize. AI Magazine, 37(1), 91-93.

Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236): 433-460.

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