Algorithm and Human Creativity: Threats or Opportunity? A Literature Review

We explore the move from a mechanical vision of Artificial Intelligence (AI) to a systemic vision of Intelligence Augmentation (IA) (Barile et al., 2018, 2019, 2020, 2021; Navarrini, 2020; Chiriatti, 2019). AI assumes the role of empowered intelligence (IA) as it is capable of expressing a capacity for modeling integration of experiences, knowledge and emotions in conditions of strong uncertainty (Barile et al., 2021; Hagel, 2021). But in a world where the nature of machine learning is changing so rapidly, does technology empower or annihilate creativity? The aim of the paper is to draw attention to the impact that disruptive technology has on human creative processes. How might progress in AI affect Human Creativity (HC)?We propose a literature review to better understand both trends and gaps

Hierarchical reinforcement learning as creative problem solving

publisher: Elsevier articletitle: Hierarchical reinforcement learning as creative problem solving journaltitle: Robotics and Autonomous Systems articlelink: http://dx.doi.org/10.1016/j.robot.2016.08.021 content_type: article copyright: © 2016 Elsevier B.V. All rights reserved

Player Responses to a Live Algorithm: Conceptualising computational creativity without recourse to human comparisons?

Abstract Live algorithms are computational systems made to perform in an improvised manner with human improvising musicians, typically using only live audio or MIDI streams as the medium of interaction. They are designed to establish meaningful musical interaction with their musical partners, without necessarily being conceived of as "virtual musicians". This paper investigates, with respect to a specific live algorithm designed by the author, how improvising musicians approach and discuss performing with that system. The study supports a working assumption that such systems constitute a distinct type of object from the traditional categories of instrument, composition and performer, which are capable of satisfying some of the expectations of an engaging improvisatory performance experience, despite being unambiguously distinct from a human musician. I investigate how the study participants' comments and actions support this view. Specifically: 1) participants interacting with the system had a stronger sense of the nature of the interaction than when they were passively observing the interaction; 2) participants couldn't tell what the "rules" of the interactive behaviour were, and didn't feel they could predict the behaviour, but reported this as being a positive, engaging aspect of the experience. Their actions implied that the improvisation had purpose and invited engagement; 3) participants strictly avoided discussing the system in terms of virtual musicianship, or of creating original output, and preferred to categorise the system as an instrument or a composition, despite describing the interaction of the system as musically engaging; 4) participants felt the long-term structure was lacking. Such results, it is argued, lend weight to the idea that as CC applications in real creation scenarios grow, the creative contribution of computer systems becomes less grounded in comparison with human standards

Can Artificial Intelligence Make Art?

In two experiments (total N=693) we explored whether people are willing to consider paintings made by AI-driven robots as art, and robots as artists. Across the two experiments, we manipulated three factors: (i) agent type (AI-driven robot v. human agent), (ii) behavior type (intentional creation of a painting v. accidental creation), and (iii) object type (abstract v. representational painting). We found that people judge robot paintings and human painting as art to roughly the same extent. However, people are much less willing to consider robots as artists than humans, which is partially explained by the fact that they are less disposed to attribute artistic intentions to robots

Can Machines Be Artists? A Deweyan Response in Theory and Practice

To speak comfortably of the machine artist (as outlined in the call for papers for this Special Issue) makes key assumptions about what it is to be an artist. It assumes, for instance, that the experience of living as an artist, which includes the socialisation, hard work, single-mindedness, and focused energy of creative activity, is incidental rather than essential since these aspects are not comfortably applicable to machines. Instead, it supposes that what is essential is the artistic product, and it is the similarity of human and machine products that makes it possible to speak of machine artists. This definition of art in terms of products is supported by modern psychological theories of creativity, defined as the generation of novel ideas which give rise to valuable products. These ideas take place in the mind or brain, regarded as a closed system within whose workings the secret of creativity will eventually be revealed. This is the framework of what is widely referred to as “cognitivism”. This definition in terms of novel ideas and valuable products has been widely assumed by artificial intelligence (AI) and computational creativity (CC), and this has been backed up through a particular version of the Turing Test. In this, a machine can be said to be a creative artist if its products cannot be distinguished from human art. However, there is another psychological view of creativity, that of John Dewey, in which a lived experience of inquiry and focus is essential to being creative. In this theory, creativity is a function of the whole person interacting with the world, rather than originating in the brain. This makes creativity a Process rather than a Cognitivist framework. Of course, the brain is crucial in a Process theory, but as part of an open system which includes both body and environment. Developments in “machine art” have been seen as spectacular and are widely publicised. But there may be a danger that these will distract from what we take to be the most exciting prospect of all. This is the contribution of computer technology to stimulate, challenge, and provoke artistic practice of all forms

Can Machines Create Art?

Open Access articleAs machines take over more tasks previously done by humans, artistic creation is also considered as a candidate to be automated. But, can machines create art? This paper offers a conceptual framework for a philosophical discussion of this question regarding the status of machine art and machine creativity. It breaks the main question down in three sub-questions, and then analyses each question in order to arrive at more precise problems with regard to machine art and machine creativity: What is art creation? What do we mean by art? And, what do we mean by machines create art? This then provides criteria we can use to discuss the main question in relation to particular cases. In the course of the analysis, the paper engages with theory in aesthetics, refers to literature on computational creativity, and contributes to the philosophy of technology and philosophical anthropology by reflecting on the role of technology in art creation. It is shown that the distinctions between process versus outcome criteria and subjective versus objective criteria of creativity are unstable. It is also argued that we should consider non-human forms of creativity, and not only cases where either humans or machines create art but also collaborations between humans and machines, which makes us reflect on human-technology relations. Finally, the paper questions the very approach that seeks criteria and suggests that the artistic status of machines may be shown and revealed in the human/non-human encounter before any theorizing or agreement takes place; an experience which then is presupposed when we theorize. This hints at a more general model of what happens in artistic perception and engagement as a hybrid human-technological and emergent or even poetic process, a model which leaves more room for letting ourselves be surprised by creativity—human and perhaps non-human

A Deep Learning Approach to generate Beethoven's 10th Symphony

Luidwig van Beethoven composed his symphonies between 1799 and 1825, when he was writing his Tenth symphony. As we dispose of a great amount of data belonging to his work, the purpose of this project is to work on the possibility of extracting patterns on his compositional model and generate what would have been his last symphony, the Tenth. Computational creativity is an Artificial Intelligence field which is still being developed. One of its subfields is music generation, to which this project belongs. Also, there is an open discussion about the belonging of the creativity, to the machine or the programmer. Firstly we have extracted all the symphonies' scores information, structuring them by instrument. Then we have used Deep Learning techniques to extract knowledge from the data and later generate new music. The neural network model is built based on the Long Short-Therm Memory (LSTM) neural networks, which are distinguished from others since these ones contain a memory module. After training the model and predict new scores, the generated music has been analyzed by comparing the input data with the results, and establishing differences between the generated outputs based on the training data used to obtain them. The result's structure depends on the symphonies used for training, so obtained music presents Beethoven's style characteristics

Exploration of the creative processes in animals, robots, and AI: who holds the authorship?

Picture a simple scenario: a worm, in its modest way, traces a trail of paint as it moves across a sheet of paper. Now shift your imagination to a more complex scene, where a chimpanzee paints on another sheet of paper. A simple question arises: Do you perceive an identical creative process in these two animals? Can both of these animals be designated as authors of their creation? If only one, which one? This paper delves into the complexities of authorship, consciousness, and agency, unpacking the nuanced distinctions between such scenarios and exploring the underlying principles that define creative authorship across different forms of life. It becomes evident that attributing authorship to an animal hinges on its intention to create, an aspect intertwined with its agency and awareness of the creative act. These concepts are far from straightforward, as they traverse the complex landscapes of animal ethics and law. But our exploration does not stop there. Now imagine a robot, endowed with artificial intelligence, producing music. This prompts us to question how we should evaluate and perceive such creations. Is the creative process of a machine fundamentally different from that of an animal or a human? As we venture further into this realm of human-made intelligence, we confront an array of ethical, philosophical, and legal quandaries. This paper provides a platform for a reflective discussion: ethologists, neuroscientists, philosophers, and bioinformaticians converge in a multidisciplinary dialogue. Their insights provide valuable perspectives for establishing a foundation upon which to discuss the intricate concepts of authorship and appropriation concerning artistic works generated by non-human entities