In the future, human-level machine communication based on neural networks will not only play a role on social media and in customer service. in the context of deep learning, reinforcement learning and supervised learning, respectively. Nevertheless, the experimental setup is still relevant for the development of artificial intelligences today, e.g. To date (March 2022), no official examples can be cited of machines passing Turing tests. At the end of the test, if the human questioner cannot determine from the answers which of the two answerers is the machine, the intelligence of the machine can be defined as human-like. The free, unspecified questions are asked by the human without any visual or auditory contact with the interlocutors via an input tool such as a keyboard or a screen.
The test uses a simple question-answer procedure between a human questioner and two anonymous answerers who are not visible to the questioner. The structure and procedure of the Turing test couldn’t be any simpler. The goal was and is to be able to speak of artificial intelligence or machine intelligence in the case of machine communication behavior that is indistinguishable from humans. Since artificial neural networks according to the principle neural network did not yet play a major role in the debate about artificial intelligence and objective scientific proof of thought processes was a long way off, observable analyses of communication with machines were used for this purpose. In his article “ Computing machinery and intelligence”, published in the journal “Mind”, Turing outlined the basic features of an experimental set-up now famous as the Turing Test, but known at the time as the “Imitation Game”. He intensively studied the problem of artificial intelligence and its criteria during his work on one of the first legendary tube computers called Manchester Mark I at the University of Manchester. The Turing Test was invented and developed by the eponymous British mathematician, computer scientist and logician Alan Turing in 1950.
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