DeepCreativity: measuring creativity with deep learning techniques

Author:

Franceschelli Giorgio1,Musolesi Mirco2

Affiliation:

1. Alma Mater Studiorum Università di Bologna, Italy

2. University College London, United Kingdom, The Alan Turing Institute, United Kingdom, Alma Mater Studiorum Università di Bologna, Italy

Abstract

Measuring machine creativity is one of the most fascinating challenges in Artificial Intelligence. This paper explores the possibility of using generative learning techniques for automatic assessment of creativity. The proposed solution does not involve human judgement, it is modular and of general applicability. We introduce a new measure, namely DeepCreativity, based on Margaret Boden’s definition of creativity as composed by value, novelty and surprise. We evaluate our methodology (and related measure) considering a case study, i.e., the generation of 19th century American poetry, showing its effectiveness and expressiveness.

Publisher

IOS Press

Subject

Artificial Intelligence

Reference51 articles.

1. Of bits and wows: a bayesian theory of surprise with applications to attention;Baldi;Neural Networks: The Official Journal of the International Neural Network Society,2010

2. Berns S. and Colton S. , Bridging Generative Deep Learning and Computational Creativity. In ICCC 2020, pages 406–409, 2020.

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. GenAICHI 2024: Generative AI and HCI at CHI 2024;Extended Abstracts of the CHI Conference on Human Factors in Computing Systems;2024-05-11

2. Dynamics of automatized measures of creativity: mapping the landscape to quantify creative ideation;Frontiers in Education;2023-10-12

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