Computing machinery and creativity

Lessons learned from the Turing test

Daniel Peter Berrar, Alfons Josef Schuster

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Purpose: The purpose of this paper is to investigate the relevance and the appropriateness of Turing-style tests for computational creativity. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: This paper argues against Turing-style tests for computational creativity. Practical implications: This paper opens a new avenue for viable and more meaningful testing procedures. Originality/value: 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.

Original languageEnglish
Pages (from-to)82-91
Number of pages10
JournalKybernetes
Volume43
Issue number1
DOIs
Publication statusPublished - 2014

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Turing
Machinery
creativity
Computing
Testing
Artificial intelligence
testing procedure
artificial intelligence
Artificial Intelligence
Creativity
Argumentation
Unfolding
argumentation
Design Methodology
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Keywords

  • Artificial intelligence
  • Creativity
  • Turing test

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Information Systems
  • Theoretical Computer Science
  • Electrical and Electronic Engineering

Cite this

Computing machinery and creativity : Lessons learned from the Turing test. / Berrar, Daniel Peter; Schuster, Alfons Josef.

In: Kybernetes, Vol. 43, No. 1, 2014, p. 82-91.

Research output: Contribution to journalArticle

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