Creative AI: Music Composition Programs as an Extension of the Composer’s Mind

Moruzzi, Caterina

doi:10.1007/978-3-319-96448-5_8

Cited by 10 publications

(11 citation statements)

References 5 publications

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“…Another unresolved topic in the computational creativity field is the evaluation of generative systems in terms of their creativity ( Ritchie, 2019 ). As Moruzzi (2018) illustrates: “The subjective judgments and biases which come with the evaluation of something as creative make it impossible to objectively answer the question “Can a computer be creative?” What we are measuring when we provide an answer to this question, in fact, are not the computer’s accomplishments but instead our subjective evaluation of them. We can then try to analyze not just the creativity exhibited by the outcome produced by the computer but, instead, the intention of the computer in producing it.…”

Section: Computational Creativitymentioning

confidence: 99%

“…But in addition, as previously stated, Boden (2009) emphasizes that creativity should be "valuable". This implies a subject-dependent evaluation, as what influences the assessment we make of something is not only its features or objective properties, but rather how such a thing is produced and presented (Moruzzi, 2018). It is also worth emphasizing that novelty often implies unpredictability and uncertainty, especially in the case of musical creativity (Daikoku et al, 2021).…”

Section: Computational Creativitymentioning

confidence: 99%

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Creativity in Generative Musical Networks: Evidence From Two Case Studies

et al. 2021

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Deep learning, one of the fastest-growing branches of artificial intelligence, has become one of the most relevant research and development areas of the last years, especially since 2012, when a neural network surpassed the most advanced image classification techniques of the time. This spectacular development has not been alien to the world of the arts, as recent advances in generative networks have made possible the artificial creation of high-quality content such as images, movies or music. We believe that these novel generative models propose a great challenge to our current understanding of computational creativity. If a robot can now create music that an expert cannot distinguish from music composed by a human, or create novel musical entities that were not known at training time, or exhibit conceptual leaps, does it mean that the machine is then creative? We believe that the emergence of these generative models clearly signals that much more research needs to be done in this area. We would like to contribute to this debate with two case studies of our own: TimbreNet, a variational auto-encoder network trained to generate audio-based musical chords, and StyleGAN Pianorolls, a generative adversarial network capable of creating short musical excerpts, despite the fact that it was trained with images and not musical data. We discuss and assess these generative models in terms of their creativity and we show that they are in practice capable of learning musical concepts that are not obvious based on the training data, and we hypothesize that these deep models, based on our current understanding of creativity in robots and machines, can be considered, in fact, creative.

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Section: Computational Creativitymentioning

confidence: 99%

Section: Computational Creativitymentioning

confidence: 99%

Creativity in Generative Musical Networks: Evidence From Two Case Studies

et al. 2021

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“…Generative algorithms recently started to be applied to the generation of paintings and music (Dong et al 2017;Moruzzi 2018;Yang et al 2017). The reason for exploring GANs and CANs is because their process of creation is inherently different from that of other generative algorithms.…”

Section: Artificial Creativitymentioning

confidence: 99%

Measuring creativity: an account of natural and artificial creativity

Moruzzi

2020

Euro Jnl Phil Sci

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Despite the recent upsurge of interest in the investigation of creativity, the question of how to measure creativity is arguably underdiscussed. The aim of this paper is to address this gap, proposing a multidimensional account of creativity which identifies problem-solving, evaluation, and naivety as measurable features that are common among creative processes. The benefits that result from the adoption of this model are twofold: integrating discussions on creativity in various domains and offering the tools to assess creativity across systems of different kinds. By situating creativity within this framework, I aim to contribute to a non-anthropocentric, more comprehensive understanding of the notion, and to debates on natural and artificial creativity.

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“…For instance, the possible compounds eligible for drug design is between 10 23 and 10 60 . Recent advances in DNN and specifically in GANs have enabled innovations in creating a new image or composing a symphony . This discovery paradigm can be applied to various materials and provided thoughtful guidance to the synthesis of new materials .…”

Section: Molecular Discoveries Using Gansmentioning

confidence: 99%

“…153 Recent advances in DNN and specifically in GANs have enabled innovations in creating a new image or composing a symphony. [154][155][156] This discovery paradigm can be applied to various materials and provided thoughtful guidance to the synthesis of new materials. 51,[157][158][159][160] GAN is one of the nonparametric approaches for deep generative models initially proposed by Goodfellow et al 45 The generative models can be used to create plausible molecular structures for high-throughput screening, which is the first step in molecular discovery.…”

Section: Molecular Discoveries Using Gansmentioning

confidence: 99%

Machine learning and artificial neural network accelerated computational discoveries in materials science

Yang

Hou

Jiang

et al. 2019

WIREs Comput Mol Sci

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Artificial intelligence (AI) has been referred to as the "fourth paradigm of science," and as part of a coherent toolbox of data-driven approaches, machine learning (ML) dramatically accelerates the computational discoveries. As the machinery for ML algorithms matures, significant advances have been made not only by the mainstream AI researchers, but also those work in computational materials science. The number of ML and artificial neural network (ANN) applications in the computational materials science is growing at an astounding rate. This perspective briefly reviews the state-of-the-art progress in some supervised and unsupervised methods with their respective applications. The characteristics of primary ML and ANN algorithms are first described. Then, the most critical applications of AI in computational materials science such as empirical interatomic potential development, ML-based potential, property predictions, and molecular discoveries using generative adversarial networks (GAN) are comprehensively reviewed. The central ideas underlying these ML applications are discussed, and future directions for integrating ML with computational materials science are given. Finally, a discussion on the applicability and limitations of current ML techniques and the remaining challenges are summarized.

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Creative AI: Music Composition Programs as an Extension of the Composer’s Mind

Cited by 10 publications

References 5 publications

Creativity in Generative Musical Networks: Evidence From Two Case Studies

Creativity in Generative Musical Networks: Evidence From Two Case Studies

Measuring creativity: an account of natural and artificial creativity

Machine learning and artificial neural network accelerated computational discoveries in materials science

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