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Mind
Published in David Burden, Maggi Savin-Baden, Virtual Humans, 2019
David Burden, Maggi Savin-Baden
There is a long history of research into creativity by computers – often called computational creativity (Ackerman et al., 2017). Boden (1998) identified three forms of computational creativity (CC): Combinational, where the computer ‘produces unfamiliar combinations of familiar ideas, and it works by making associations between ideas that were previously only indirectly linked’ – such as creating a photo-montage;Exploratory, where the computer explores possibilities within a ‘culturally accepted style of thinking, or ‘conceptual space’ – such as a major or minor scale in music; andTransformational, where the computer removes or changes the constraints on an accepted conceptual space and then explore this new space.
An Overview of Deep Learning in Industry
Published in Jay Liebowitz, Data Analytics and AI, 2020
Quan Le, Luis Miralles-Pechuán, Shridhar Kulkarni, Jing Su, Oisín Boydell
Rather than recognizing what is available in the data, in generation tasks, the objective is to output novel or additive content based on input data. Examples include generating captions for images, converting a piece of music into a new style (Hadjeres and Pachet, 2016), or composing entire documents. This section surveys key applications of deep learning for generating novel content. The ability to build machine learning systems that generate new content is something that did not really exist before the advent of deep learning approaches and has spurred a renewed interest in the area of computational creativity.
A Look at Top 35 Problems in the Computer Science Field for the Next Decade
Published in Durgesh Kumar Mishra, Nilanjan Dey, Bharat Singh Deora, Amit Joshi, ICT for Competitive Strategies, 2020
Deepti Goyal, Amit Kumar Tyagi
Computational Creativity (CC) is the workmanship, science, reasoning and building of computational frameworks which, by taking on specific responsibilities, exhibit behaviours that unbiased observer consider to be creative. This area really required a break-through in formalizing what it implies for programming to be innovative, along with the different energizing and important utilizations of inventive programming in sciences, expressions, writing, gaming and other areas.
Artificial intelligence as relational artifacts in creative learning
Published in Digital Creativity, 2023
Jeongki Lim, Teemu Leinonen, Lasse Lipponen, Henry Lee, Julienne DeVita, Dakota Murray
Computational Creativity is a research domain at the intersection between creativity studies and computer science. The researchers use frameworks from psychological theories on creativity to develop an autonomous system that can produce creative solutions and artistic outputs (Colton and Wiggins 2012; Reddy 2022). These actions by computers can be categorized as combinatory, exploratory, and transformative creativity (Boden 1991). The first two actions operate within and at the edge of the domain knowledge space, while the last develops a new knowledge paradigm. How these systems can foster human creativity has been explored in the concept called computational co-creativity (Lubart 2005). For instance, in a mixed-initiative model, the computer and human user take turns in a collaborative effort that can result in increased divergent thinking (Liapis et al. 2016). This model provides useful insights into developing interaction design. However, it lacks the educational consideration for a transformative learning outcome where students develop creative capacities independent of the computer system. We are incorporating these theories and concepts from computational creativity practices into evaluating the student actions and the developmental impact of the system.
Artificial everyday creativity: creative leaps with AI through critical making
Published in Digital Creativity, 2022
The term ‘Artificial Creativity,’ much along the lines of Artificial Intelligence, is a field that studies human creativity by developing computational systems that can be considered creative on their own terms (Cope 2005; p. vii in Fonseca 2011). The aim of artificial creativity is to employ cognitive, embodied, and situated frameworks to facilitate an understanding of creativity-as-it-is so that we might push for creative practices to be performed alongside artificial agents (Saunders 2019). For some scholars, artificial creativity is a subfield of ‘computational creativity,’ focusing on the automation of creative tasks by machines, whereas for others, the concern is not whether a machine is creative or not but rather how machines may play a role in the creation of new approaches in art and design or other creative outcomes (McCormack and D’Inverno 2012; Fonseca 2011; Bown 2021). While there is momentum around artificial creativity in domains like arts, music, and robotics, there is still considerable debate around the term creativity itself and how its definitions will determine what we imagine and create with computers in the future (McCormack and D’Inverno 2012).
Experience evaluations for human–computer co-creative processes – planning and conducting an evaluation in practice
Published in Connection Science, 2019
Anna Kantosalo, Sirpa Riihiaho
This paper proposes a human perspective in evaluation of human–computer co-creative processes in which humans and computationally creative systems create together. Computational creativity is a sub-field of artificial intelligence devoted to the research and simulation of creative behaviour. An important goal of computational creativity is to generate creative artefacts via computational means. Human–computer co-creativity examines how we can use these computational creativity methods to promote human creativity and vice versa. In the human–computer co-creative process, the human becomes an integral part of the creative process itself instead of being just a part of the audience. This requires adopting a new evaluation stance, which goes beyond the traditional computational creativity evaluation foci of creative output and internal workings of the system, and includes the experiences of the human working with the system. Investigating the real user experiences of real users is essential for further developing these systems.