Computational Creativity and Aesthetics with Algorithmic Information Theory

We build an analysis based on the Algorithmic Information Theory of computational creativity and extend it to revisit computational aesthetics, thereby, improving on the existing efforts of its formulation. We discuss Kolmogorov complexity, models and randomness deficiency (which is a measure of how much a model falls short of capturing the regularities in an artifact) and show that the notions of typicality and novelty of a creative artifact follow naturally from such definitions. Other exciting formalizations of aesthetic measures include logical depth and sophistication with which we can define, respectively, the value and creator’s artistry present in a creative work. We then look at some related research that combines information theory and creativity and analyze them with the algorithmic tools that we develop throughout the paper. Finally, we assemble the ideas and their algorithmic counterparts to complete an algorithmic information theoretic recipe for computational creativity and aesthetics.

On the Work of the Samara Branch of the RAS Scientific Council on the Methodology of Artificial Intelligence and Cognitive Research

The summary presents the main results of the work of the Samara branch of the RAS Scientific Council for the Methodology of Artificial Intelligence and Cognitive Research, created in 2007 on the basis of S.P. Korolev Samara National Research University (Samara University). The Samara branch of the Council and the Samara University held international conferences on information technology, information society, science fiction, established Artificial Intelligence Center as well as completed interdisciplinary technical and humanitarian research projects in the field of socio-humanitarian cybernetics, digital models of creative processes, computational aesthetics.

What Is (Machine) Philosophy?

This interview with the digital media theorist Luciana Parisi opens with the hypothesis that cybernetics is not merely the name for that postwar metascience of command and control. For Parisi, cybernetics names a “historical reconfiguration of metaphysics on behalf of technics.” This interview asks about the meaning and consequences of this hypothesis but steers away from the all-too-easy poiesis-as-panacea solution to the computational quagmire. Instead, this interview descends into the computational medium, into the specificity of its logic, asking what it might mean not merely to live in a cyberneticized world but to actively participate and believe in such a world. Parisi’s response puts to philosophy an important task: not to seek the accommodations of an expanding concept of the human within a machinic world but to think with the logic of the ascendant cybernetic metaphysics. For Parisi, a necessary move herein is to negotiate the reality of the algorithm’s syntactic operations, their performativity, a move that for her implies a certain form of belief. In tracking this form of belief across disciplines, this interview broaches questions of scalability, race and colonialism, the nonneutrality of technoscience, and the potential of computational aesthetics. Finally, the interview gestures toward Parisi’s future work, because, as she reminds us, we cannot go back; there are questions emerging from within machines that are eager to emerge and are waiting for us to think them.

On machine vision and photographic imagination

In this article we introduce the concept of implied optical perspective in deep learning computer vision systems. Taking the BBC's experimental television programme “Made by Machine: When AI met the Archive” (2018) as a case study, we trace a conceptual and material link between the system used to automatically “watch” the television archive and a specific type of photographic practice. From a computational aesthetics perspective, we show how deep learning machine vision relies on photography, its technical regimes and epistemic advantages, and we propose a novel way to identify the latent camera through which the BBC archive was seen by machine.

Review of computational neuroaesthetics: bridging the gap between neuroaesthetics and computer science

The mystery of aesthetics attracts scientists from various research fields. The topic of aesthetics, in combination with other disciplines such as neuroscience and computer science, has brought out the burgeoning fields of neuroaesthetics and computational aesthetics within less than two decades. Despite profound findings are carried out by experimental approaches in neuroaesthetics and by machine learning algorithms in computational neuroaesthetics, these two fields cannot be easily combined to benefit from each other and findings from each field are isolated. Computational neuroaesthetics, which inherits computational approaches from computational aesthetics and experimental approaches from neuroaesthetics, seems to be promising to bridge the gap between neuroaesthetics and computational aesthetics. Here, we review theoretical models and neuroimaging findings about brain activity in neuroaesthetics. Then machine learning algorithms and computational models in computational aesthetics are enumerated. Finally, we introduce studies in computational neuroaesthetics which combine computational models with neuroimaging data to analyze brain connectivity during aesthetic appreciation or give a prediction on aesthetic preference. This paper outlines the rich potential for computational neuroaesthetics to take advantages from both neuroaesthetics and computational aesthetics. We conclude by discussing some of the challenges and potential prospects in computational neuroaesthetics, and highlight issues for future consideration.

Explorations in the Indeterminacy of Computation: An Interview with M. Beatrice Fazi

This interview with M. Beatrice Fazi explores in detail her work on computation. Focusing in particular upon her recent publications, it covers the themes of contingency and indeterminacy. The questions explore Fazi’s perspectives on computational aesthetics, abstraction and experience. Through an interrogation of the conceptual insights that Fazi’s recent work offers, the interview outlines an agenda for future work in the philosophy of computation and sets forward a series of conceptual policies for seeing the digital, software and data in a fresh light.

Gaze Information Channel in Van Gogh’s Paintings

This paper uses quantitative eye tracking indicators to analyze the relationship between images of paintings and human viewing. First, we build the eye tracking fixation sequences through areas of interest (AOIs) into an information channel, the gaze channel. Although this channel can be interpreted as a generalization of a first-order Markov chain, we show that the gaze channel is fully independent of this interpretation, and stands even when first-order Markov chain modeling would no longer fit. The entropy of the equilibrium distribution and the conditional entropy of a Markov chain are extended with additional information-theoretic measures, such as joint entropy, mutual information, and conditional entropy of each area of interest. Then, the gaze information channel is applied to analyze a subset of Van Gogh paintings. Van Gogh artworks, classified by art critics into several periods, have been studied under computational aesthetics measures, which include the use of Kolmogorov complexity and permutation entropy. The gaze information channel paradigm allows the information-theoretic measures to analyze both individual gaze behavior and clustered behavior from observers and paintings. Finally, we show that there is a clear correlation between the gaze information channel quantities that come from direct human observation, and the computational aesthetics measures that do not rely on any human observation at all.