Inception of the Turing Test

The concept of the Turing test is a captivating topic that prompts reflection on whether a computer could genuinely replicate human behaviour to the extent of being deemed sentient. Passing the test requires the machine to possess a range of complex abilities, such as comprehending natural language, learning from conversation exchanges, and exhibiting common sense. The late Alan Turing’s pioneering contributions to this field have enormously impacted the development of computing and artificial intelligence. His legacy continues to influence the direction of research in these areas today.

Alan Turing, who famously used the Turing Machine to formalise computing, proposed the Turing Test to determine whether a machine’s responses were part of the computable space. In his paper, he sought to replace the ambiguous question of whether machines can think with an “Imitation Game” to provide a clear definition.

The original concept behind Turing’s game involved three participants: a man, a woman, and an interrogator. The objective was for the interrogator to determine the gender of each participant. However, since the interrogator could discern gender based on voice (and possibly handwriting), the participant’s responses were either typed out or relayed by an intermediary. In the Turing Test, one of the human participants is replaced by a machine, and the interrogator’s goal shifts from determining gender to identifying which participant is human and which is a machine.

The Turing Test consists of several components that define the notion of machines being able to think. Firstly, the test requires an interrogator to identify one human and one machine. It is not enough for the computer to trick a human into thinking it is sentient; it must also deceive a suspicious human. Secondly, the physical aspect is irrelevant. The objective is to have no discernible difference between the output of the machine and that of an actual human. The communication medium is limited to written language without any additional hints. The test does not involve specific tasks such as complex problem-solving or creating art. Based on these criteria, a machine would pass the Turing Test if it could engage in casual conversation with a human and understand the context of the dialogue. Turing believed that if a machine could pass the test, it would prove that machines could think.

In addition to defining the game, the paper introduces digital computers and their potential for arbitrary computation – referencing the Turing machine. When combined with Godel’s incompleteness theorem and Turing’s formalisation of what is computable, the Turing test addresses the fundamental question of whether the ability to appear conscious is within the realm of computable problems that a Turing machine can solve or if it belongs to the small subset of truths that cannot be proven. While the test itself is straightforward, its question is of great significance and is linked to Turing’s earlier work on formalising what can be computed.

Problems/Difficulties with the Turing Test 

Turing’s original paper contains a significant portion dedicated to addressing counterarguments about the validity of his proposed test. In the introduction to this section, he predicts that computers will possess enough storage capacity within the next fifty years to pass the Turing test. This statement is interesting because it implies that the AI program needed to pass the test is not too complicated and that the only obstacle is the amount of memory available. This limitation may have been at the forefront of Turing’s thoughts, as he frequently encountered problems that could have been solved with more storage capacity. Similarly, today, we often rely on Moore’s Law to help us tackle complex issues.

Aside from the constraints posed by storage limitations, the individual in question raises further objections rooted in theology. Specifically, he posits that sentience is contingent upon possessing an immortal soul granted by a deity. He also puts forth mathematical arguments based on Godel’s findings and appeals to the human capacity for generating entirely original creations and experiencing emotions. These are a few objections he has raised in opposition to certain propositions.

A fascinating contradiction to the Turing Test is known as ‘The Argument from Consciousness.’ This argument suggests that merely imitating a human would not be sufficient since it does not encompass the entire spectrum of what we consider human. In other words, a machine that can pass the Turing Test may still be incapable of completing tasks such as composing a poem or a piece of music as part of an emotional reaction. According to Turing, a machine that passes the Turing test does not need to have the ability to experience or interpret art. He argues that unless you are the machine, it is impossible to tell if it is experiencing emotions. Therefore, there is no way to prove or contradict the claim. Turing uses this reasoning to dismiss the argument and suggests that the Turing test could involve the machine convincing the interrogator that it is experiencing emotions, even if it is not feeling them like a human would. This is similar to how humans communicate with each other to convince others of their feelings, but there is no guarantee that those emotions are genuine.

Turing’s test has been subject to counterarguments, and one of them is known as “Lady Lovelace’s Objection.” According to this argument, machines cannot generate new ideas since they can only perform their programmed jobs. This inability to produce ideas is a unique trait of humans, known to develop new concepts constantly. However, recent developments in machine learning have demonstrated that this limitation can be overcome. For example, software can recognise various human dialects and handwriting, resulting in voice and character recognition. Such advancements suggest that machines can be trained to realise new things, even in general situations, and can learn beyond their initial programming.

In general, the issues that could arise with the Turing test can be categorised into two groups:

Is the ability to mimic human behaviour an accurate measure of intelligence, or is it a difficult challenge?

Can intelligence be achieved without passing the Turing test?

It is reasonable to assert that passing the Turing test is only one aspect of humans’ daily challenges. It is possible that computers can simulate specific vital capabilities such as problem-solving, experiencing emotions, or having core beliefs or motivations. However, it is essential to note that these simulated capabilities may not necessarily be identical to those of humans. The Turing test bypasses these queries by considering the computer (and human) only on the text they output as a function of the casual conversation during the trial. Although a computer may pass the Turing test, whether this is sufficient evidence to declare machines as ‘intelligent’ or capable of ‘thinking’ is debatable. Passing the Turing test merely indicates that they can complete the test, but there is still much to comprehend before considering them intelligent.

In addition to individuals who have reached adulthood, some young children possess the ability to perceive and feel but may not be able to perform optimally in the Turing test due to their limited knowledge and experience in communication. It would be unjust to subject them to the Turing test and conclude that they cannot engage in cognitive processes. Therefore, it is plausible that a computer could exhibit signs of cognitive functioning without necessarily passing the Turing test.

Alternatives to the Turing Test 

Numerous individuals have suggested their version of the Turing test to address the perceived or possible limitations of the test initially proposed by Alan Turing. The alternatives either narrow the test’s scope to make it more achievable or shift it to an area where researchers may progress better. One such alternative is the Feigenbaum Test, which circumvents the challenges that make it challenging for a computer to communicate conversationally, as in the Turing test. The Feigenbaum test sets a benchmark for when technologies like Expert systems have matured by requiring the computer to portray expertise in a particular field convincingly. This definition of the test accomplishes two things: it eliminates the casual, unrestricted nature of the Turing interrogator. It necessitates that the computer solve problems that an expert in the field would be able to solve. The test is more difficult in some ways due to the expert problem-solving component but more straightforward in others where casual conversation is not required.

Nicholas Negroponte, a notable co-founder of the renowned MIT Media Lab, had a unique proposition regarding the Turing test. According to him, the actual value and capability of a ‘thinking’ computer lies in its ability to work alongside a human collaboratively towards achieving a common goal instead of merely deceiving a human into believing that it is also human. This version of the Turing test would require the machine to assist the human just as another human would, making it a much more challenging test. The computer would have to possess a superior ability to understand and comprehend imprecise or flawed communication from humans, which is a significant feat. This approach towards testing a machine’s cognitive skills is highly regarded and respected in computer science.

The Current State of the Turing Test

Since its proposal, numerous attempts have been made to create software that can pass the Turing test or a more restricted version. While a limited understanding of the trial, which involves briefly convincing an unsuspecting human that they are communicating with another individual instead of a computer, has been achievable for some time, the test has not yet been passed. 

The Turing test is a complex problem that requires extensive research to overcome. Unfortunately, progress has been slow and incremental, with researchers striving to create solutions that surpass their predecessors. While faster computers and larger memory capacities have been developed, they have not significantly impacted progress. The issue is creating practical algorithms that utilise the available processing power and storage. Limited progress in AI research and the immense difficulty of the problem may be contributing factors to the lack of significant advancements. Despite considerable effort, little progress has been made to date.

There is currently only one incentive for researchers to pursue further advancements in artificial intelligence – the Leobner Prize and its corresponding Turing Test Competition. The prize offers a cash reward of $100,000 to any system that can successfully pass as a human. To encourage more participation, the competition also awards a medal and $2,000 each year to the best entry. However, due to the relatively low value of these rewards compared to other more lucrative avenues for research, only a select few researchers actively work towards this goal.

On the Loebner Prize website, you can find transcripts of the top four entries from each year since 1995. Upon reviewing the conversations of the winning entries, it is fantastic to see how closely they resemble human responses and how difficult it is to differentiate between the two. However, there are still significant differences between humans and computers, with the software sometimes responding nonsensically. Occasionally, the winning program provides seemingly random answers to simple questions.

Below are examples of exchanges from the 2005 contest between the judge and the winning program, Jaberwocky. In this particular example, the judge types in random characters.

JUDGE: Hi George, cslkjslw weicmslw weos, correct? PROGRAM: Don’t repeat your character, Chiang!

The program has identified the letters as part of the judge’s name, which is not a conclusion a human would typically come to. The outcome differed when the judge interacted similarly with a human, as the human asked questions instead of jumping to conclusions.

JUDGE: Wljso lwjejdo wleje elwjeo wjeol.

CONFEDERATE: Could that be Polish?


CONFEDERATE: What you reported overhead.

JUDGE: This is Kjell, aren’t you?

CONFEDERATE: I’m a little nervous about your writing talents.

The system that wins often seems to be responding randomly, as seen in exchanges like the one below:

JUDGE: OK, sufficiently for now. I’ll talk with the other commodity.

PROGRAM: OK, then, it was lovely to share your hair brush.

JUDGE: What do you mean by that? How do I hold you to share my hairbrush?


JUDGE: Good?


JUDGE: This has evolved into a very zen-like discussion.

PROGRAM: And poorly, however, I presented it to charity.

JUDGE: What? What did you present to charity? This discussion?

PROGRAM: Er.. 1 million Bucks?

JUDGE: Which charity?

PROGRAM: Are we speaking about the Simpsons or fake?

JUDGE: I’m uncertain about the topic at hand, to be honest.

The Turing test’s challenges and our inability to achieve them are evident when we observe the winning systems’ complicated and disjointed conversations, as shown above. Although software can imitate human responses for limited topics with predetermined paths, natural conversation remains out of reach for even the most advanced systems currently available.


Although Alan Turing had hoped that a computer would pass the Turing test by 2000, this has not yet happened. Despite extensive research efforts, processor technology improvements, and inexpensive memory availability, no computer has come close to passing the Turing test. This suggests that the increase in computing power predicted by Moore’s Law has not been the most critical factor in improving AI for the Turing test. Instead, the main challenge lies in the software architecture. Expert Systems, for example, may offer promising solutions as they continue to be developed and applied to different problems, including the Turing test and its derivatives.

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