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Turing and Von Neumann are often credited with being responsible for the design of modern computers. Turing's influence can be traced back to his 1936 paper where he defined the abstract notion of computability and demonstrated an ‘abstract’ paper computing machine. It was extremely influential especially in mathematical logic although its importance for the design of modern digital computers can only be inferred. He himself saw this work as an investigation into the theoretical possibilities and limitations of digital computing machines. During World War II Turing worked in the cryptanalysis section at Bletchley Park where various electromagnetic and some electronic devices for decoding were built. It is unclear what Turing's involvement was in the design and building of these machines. However immediately after the War, he produced a blue‐print for a computer (ACE) which is in architecture like a modern machine. A prototype was built sometime after Turing had left the NPL. It is difficult to pinpoint the practical contribution Turing has made. Nevertheless he can be claimed as one of the pioneers of computing especially because of the indirect influence of his ideas on the development of computer science.

The purpose of this paper is to consider Turing's test and his objections to the idea that a machine might eventually pass it. Discusses behavioural diversity in relation to the Turing test.

The paper argues that this objection cannot be dismissed easily, taking the view that the diversity exhibited by human behaviour is characterised by a kind of context‐sensitive adaptive plasticity. Draws on Descartes' arguments and artificial intelligence to interpret the Turing test.

It is found that the distinctive context‐sensitive adaptive plasticity of human behaviour explains why the Turing test is such a stringent test for the presence of thought and why it is much harder to pass than Turing himself may have realised.

This paper provides an unique view of Turing's test that will assist researchers in assessing its value and its goals.

The purpose of this paper is to investigate the relevance and the appropriateness of Turing-style tests for computational creativity.

The Turing test is both a milestone and a stumbling block in artificial intelligence (AI). For more than half a century, the “grand goal of passing the test” has taught the authors many lessons. Here, the authors analyze the relevance of these lessons for computational creativity.

Like the burgeoning AI, computational creativity concerns itself with fundamental questions such as “Can machines be creative?” It is indeed possible to frame such questions as empirical, Turing-style tests. However, such tests entail a number of intricate and possibly unsolvable problems, which might easily lead the authors into old and new blind alleys. The authors propose an outline of an alternative testing procedure that is fundamentally different from Turing-style tests. This new procedure focuses on the unfolding of creativity over time, and – unlike Turing-style tests – it is amenable to a more meaningful statistical testing.

This paper argues against Turing-style tests for computational creativity.

This paper opens a new avenue for viable and more meaningful testing procedures.

The novel contributions are: an analysis of seven lessons from the Turing test for computational creativity; an argumentation against Turing-style tests; and a proposal of a new testing procedure.

It is widely known that when Turing first introduced his “imitation‐game” test for ascertaining whether a computing machine can think, he considered, and found wanting, a series of objections to his position. It seems safe to say that one of these objections, the “theological objection” (TO), is regarded by Turing to be positively anemic, and that ever since he delivered his rapid (purported!) refutation over half a century ago, the received view has been that, indeed, this objection is as weak as can be. The purpose of this paper is to show that TO is not the pushover Turing, and others since, take it to be.

The paper is devoted to the TO within the Turing test (TT) and to Turing's reply to this objection.

The paper reaches the conclusion that Turing's response to TO fails.

This paper is a defense of the TO to the TT.

This paper aims to present a methodology for defining and modeling context-awareness and describing efficiently the interactions between systems, applications and their context. Also, the relation of modern context-aware systems with distributed computation is investigated.

On this purpose, definitions of context and context-awareness are developed based on the theory of computation and especially on a computational model for interactive computation which extends the classical Turing Machine model. The computational model proposed here encloses interaction and networking capabilities for computational machines.

The definition of context presented here develops a mathematical framework for working with context. Also, the modeling approach of distributed computing enables us to build robust, scalable and detailed models for systems and application with context-aware capabilities. Also, it enables us to map the procedures that support context-aware operations providing detailed descriptions about the interactions of applications with their context and other external sources.

A case study of a cloud-based context-aware application is examined using the modeling methodology described in the paper so as to demonstrate the practical usage of the theoretical framework that is presented.

The originality on the framework presented here relies on the connection of context-awareness with the theory of computation and distributed computing.

The hardware technology for an intelligent machine is available. We see no contraindication to the construction of the software of such a machine. This paper reviews and lists the functional properties of intelligent machines as seen by many authors, and attempts to formulate then in terms of basic computational methods and a program structure. It is suggested that an interchange between brain scientists and artificial intelligence workers could be more fruitful than before. The question of the validity of comparing brains and computers remains unsettled.

Rice’s Theorem is a notorious stumbling block in Computer Science. We review some previous work of us that shows that we can extend Rice’s result to large segments of everyday mathematics, so that similar stumbling blocks appear in many areas of mathematics, as well as applied areas such as mathematical economics; one of its applications (Koppl’s conjecture) is discussed in some detail. Note: this paper has been written in an informal style.

It has been argued that Gödel's theorem proves the case against the possibility of artificially intelligent machines, capable of achieving the same level of intelligence as human beings. The argument is that if a human being were a logistic system L, how is possible that it can see certain theorems to be provable when Gödel shows that such a system cannot demonstrate whether such theorems are provable or not. The fallacy is that the theorems of L that the human can see to be provable are a subset L′ of L, and that for some theorems of L′ and not L the human is subject to the same limitation as the machine.