Academic Paper Example

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.

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.

Developed as a thought experiment by John Searle (1980), the Chinese Room argument has frequently been cited as a textbook example of the manner in which intelligence can be simulated but not replicated. The experiment is also evocative because it highlights the basic issues that are involved in conceptualizing variables such as “intelligence,” “understanding” and “thinking.” Imagine being in a room surrounded by filing cabinets full of coded “canned one-liners” and guidelines concerning which response to select when presented with requests for information on cards from anonymous users. The individual in the Chinese Room does not need to know (or care) what the symbols mean, only which response is most appropriate based on the guidelines. Given the enormous complexity of the Chinese alphabet and language, it is reasonable to suggest that the individual would require a long time to locate the appropriate response, but as proficiency is gained over time in responding to inputs, the output process would become far more efficient. This outcome, however, does not mean that the individual has learned anything about Chinese (or Martian or whatever), only that proficiency in answering inquiries has been gained. 

This process is analogous to what takes place when humans request information from computers, of course, and the Chinese Room argument makes it clear that the process has only been facilitated by digital processing rather than creating truly “thinking computers” (Overskeid, 2005). Likewise, Searle (1990) concluded early on that, “A [computer] program merely manipulates symbols, whereas a brain attaches meaning to them” (p. 26). Moreover, the symbols themselves are completely meaningless to a computer, or the Chinese Room operator, but understanding what they mean is not required in order to follow the guidelines and generate appropriate outputs that may be highly convincing in their authenticity and relevance, but do not rise to the possession of true intelligence. It is important to note, however, that despite the limitations of the Turing test and the constraints identified in the Chinese Room argument, the potential for the successful simulation of human reasoning, at least in principle, remains a possibility for the future as discussed below.          
Finally, do you think that successful simulation of human reasoning is in principle possible? Does one’s answer to this question inherently depend on one’s answer to the main question?

Although accurately measuring intelligence may remain elusive, the successful simulation of human reasoning through artificial intelligence may only be a matter of developing sufficient computer processing speed. Given that Moore’s law has largely held true since its formulation in 1965 and Church’s theory suggests that it is possible (Churchland & Churchland, 1990), it is certainly conceivable that within a few years artificial intelligence will have advanced to the point where human reasoning can be successfully simulated, and that these applications can “learn” over time and become even more proficient at this type of simulation. There has been some progress made in this area already, with artificial intelligence applications demonstrating “phenomenological equivalence” (Ray, 2011, p. 203). Therefore, the answer to this question does not depend on the refutation of the main claim.