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Turing Test in Artificial Intelligence

Last Updated : 21 Mar, 2024
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The Turing test was developed by Alan Turing(A computer scientist) in 1950. He proposed that the “Turing test is used to determine whether or not a computer(machine) can think intelligently like humans”? 

The Turing Test is a widely used measure of a machine’s ability to demonstrate human-like intelligence. It was first proposed by British mathematician and computer scientist Alan Turing in 1950.

The basic idea of the Turing Test is simple: a human judge engages in a text-based conversation with both a human and a machine, and then decides which of the two they believe to be a human. If the judge is unable to distinguish between the human and the machine based on the conversation, then the machine is said to have passed the Turing Test.

The Turing Test is widely used as a benchmark for evaluating the progress of artificial intelligence research, and has inspired numerous studies and experiments aimed at developing machines that can pass the test.

While the Turing Test has been used as a measure of machine intelligence for over six decades, it is not without its critics. Some argue that the test is too focused on language and does not take into account other important aspects of intelligence, such as perception, problem-solving, and decision-making.

Despite its limitations, the Turing Test remains an important reference point in the field of artificial intelligence and continues to inspire new research and development in this area.

Imagine a game of three players having two humans and one computer, an interrogator(as a human) is isolated from the other two players. The interrogator’s job is to try and figure out which one is human and which one is a computer by asking questions from both of them. To make things harder computer is trying to make the interrogator guess wrongly. In other words, computers would try to be indistinguishable from humans as much as possible. 


The “standard interpretation” of the Turing Test, in which player C, the interrogator, is given the task of trying to determine which player – A or B – is a computer and which is a human. The interrogator is limited to using the responses to written questions to make the determination 

The conversation between interrogator and computer would be like this: 
C(Interrogator): Are you a computer? 
A(Computer): No 

C: Multiply one large number to another, 158745887 * 56755647 
A: After a long pause, an incorrect answer! 

C: Add 5478012, 4563145 
A: (Pause about 20 seconds and then give an answer)10041157 

If the interrogator wouldn’t able to distinguish the answers provided by both humans and computers then the computer passes the test and the machine(computer) is considered as intelligent as a human. In other words, a computer would be considered intelligent if its conversation couldn’t be easily distinguished from a human’s. The whole conversation would be limited to a text-only channel such as a computer keyboard and screen. 

He also proposed that by the year 2000 a computer “would be able to play the imitation game so well that an average interrogator will not have more than a 70-percent chance of making the right identification (machine or human) after five minutes of questioning.” No computer has come close to this standard. 

But in the year 1980, Mr. John Searle proposed the “Chinese room argument“. He argued that the Turing test could not be used to determine “whether or not a machine is considered as intelligent like humans”. He argued that any machine like ELIZA and PARRY could easily pass the Turing Test simply by manipulating symbols of which they had no understanding. Without understanding, they could not be described as “thinking” in the same sense people do. We will discuss this in the next article. 

In 1990, The Newyork business man Hugh Loebner announce to reward $100,000 prize for the first computer program to pass the test. however no AI program has so far come close to passing an undiluted Turing Test

 Artificial intelligence can be categorized by job capacity and competence in the following two types: 

  1. Weak artificial intelligence: A type of artificial intelligence with a design for a personal assistant, customer relationships, video games, and questionnaires known as weak artificial intelligence. It consists of a small algorithm and data source. The algorithm and data source related to the data associated with the service industry some of the weak AI examples are – a.Amazon Alexa b. Railways Disha c. Apple’s Siri.
  2. Strong Artificial Intelligence: It is a system that carries on the task directly performed by humans like vehicle driving. This type of task is more complex and considered under a complicated system. They are programmed to handle situations in which the decision may be situational changes or unpredicted these kinds of systems are developed under strong AI and testing of these systems is very difficult but very useful for human beings. This categorization of AI is able to replace the manual human operative task with a programmed machine. These machines today are most popularly available with intelligent systems such as robots, which are treated the same rights as humans.

Turing Test: 

Alan Turing proposed a simple method of determining whether a machine can demonstrate human intelligence. If a machine engages in a conversation with a human about how to process the data it has been demonstrated by a machine, He has proposed the following skills of the test as follows: 

The turning judges the conversational skills of humans. According to this test, a computer program can think of a proper response for humans. This test matches the conversational data from the existing data through an algorithm and back respond to humans.

Advantages of the Turing Test in Artificial Intelligence:

  1. Evaluating machine intelligence: The Turing Test provides a simple and well-known method for evaluating the intelligence of a machine.
  2. Setting a benchmark: The Turing Test sets a benchmark for artificial intelligence research and provides a goal for researchers to strive towards.
  3. Inspiring research: The Turing Test has inspired numerous studies and experiments aimed at developing machines that can pass the test, which has driven progress in the field of artificial intelligence.
  4. Simple to administer: The Turing Test is relatively simple to administer and can be carried out with just a computer and a human judge.

Disadvantages of the Turing Test in Artificial Intelligence:

  1. Limited scope: The Turing Test is limited in scope, focusing primarily on language-based conversations and not taking into account other important aspects of intelligence, such as perception, problem-solving, and decision-making.
  2. Human bias: The results of the Turing Test can be influenced by the biases and preferences of the human judge, making it difficult to obtain objective and reliable results.
  3. Not representative of real-world AI: The Turing Test may not be representative of the kind of intelligence that machines need to demonstrate in real-world applications.


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