Machine Creativity Research Articles

Creative data justice: a decolonial and indigenous framework to assess creativity and artificial intelligence

ABSTRACT In the last decade, the Global South has emerged as a significant player in the data economy due to their majority user base, and studying its role is crucial to comprehend the future of AI. As societies grapple with the implications of AI on creative life, there is an opportunity to reevaluate the creative contributions of Global South cultures, ensuring they are acknowledged and foregrounded in the evolving landscape of human and machine creativity. This paper calls for reimagining and restructuring creative value with the emergence of AI enabled technologies by broadening who and what counts as creative in this data-driven era. To democratize creativity, a decolonial and indigenous framework of cross-cultural creative value is needed which critically intersects and examines the relations between creative labor, rights, and learning. The study of the Global South’s data economies is important not only to harness its potential but also to address the cross-cultural ethics of building Creative AI tools with data from their underrepresented communities. At its core, the creative data justice framework emphasizes the need to challenge the existing power imbalances in global data governance. This paper proposes that fair creative value can be achieved by drawing inspiration from indigenous systems of care as a counterforce to neoliberal values of efficiency and utility. This framework will help scholars, policymakers and designers in their inclusive approaches to creativity in the age of AI.

Nonknowledge in Computation. Reflecting on Irrevocable Uncertainty

Abstract My paper approaches the theme of computational creativity by looking at uncertainty as an epistemic and aesthetic tool that must be examined to address the challenges brought to critical practice by planetary computation. It positions uncertainty as central to how the encounter of the human practitioner with non-human machines is conceptualized, and as a resource for building speculative-pragmatic paths of resistance against algorithmic capture. It proposes ways to cultivate uncertainty and use it as a design material to produce new types of knowledge that question machines’ pre-emptying manoeuvres and resist their capture of potential. The argument proposed is that uncertainty affords the production of new imaginaries of the human-machine encounter that can resist the foreclosure of futures (what will be) and are sustained instead by the uncertainty of potential (what might be) (Munster 2013). Dwelling in a space of potential – Deleuze’s virtual, or what I call a space of ‘maybes’, requires of the practitioner a repositioning of their epistemic perspective and reflecting on the following questions: how can material knowledge be made by engaging with modes of un-knowing and not-knowing in machine interaction? How can these modes of un-knowing and not-knowing be fostered as a critical and political onto-epistemological project of reinventing critical practice for the algorithmic age? (Horl et al. 2021; Hansen 2021, 2015, Pasquinelli and Joler 2020). The paper argues that the machinic unknown should be engaged with - not through the conventional paradigm that pitches human vs machine creativity and attempts to rank and score them through similarities, but rather through a (paradoxical) deepening of the unknowability at the core of the machine (Parisi) and machine’s own incommensurability (Fazi 2020). It then proposes the Chinese notion of wu wei (active non action) (Jullien 2011, 2004, 2000, 1995; Allen 2015, 2011) as a stratagem to experiment with to craft speculative-pragmatic interventions, and to augment the ‘power of maybes’ as a space of anti-production, and resisting reduction (Ito 2019).

Homo creator i twórcze maszyny

The article analyzes the issue of machine creativity in the context of human creativity and questions the cultural consequences of integrating artificial intelligence into the domain of creative activities. Using examples such as creative adversarial networks (CAN) creating graphics and the poetry-generating algorithm code-davinci-002, the machine creative process is discussed. It is highlighted that artificial intelligence systems have the capability to act creatively and generate original solutions, texts, music, and images. However, humans are the source of creative intent and the ones who assign meaning and status to art. Machines currently play an increasingly significant role in creative processes, which could lead to the emergence of new art forms but also poses the risk of automating the aesthetic realm.

Towards a mixed human–machine creativity

Towards a mixed human–machine creativity

Generative Artificial Intelligence

Generative AI is a cool tech that helps machines create new stuff that looks a lot like what humans make. Think of it like a computer artist who can paint pictures, write stories, compose music, and even make videos. This paper is all about explaining generative AI: where it came from, what it's doing now, and how it's used. It works by using fancy computer programs and smart algorithms to understand patterns in data and then make new things that fit those patterns. It's super important because it's changing how we do things in fields like healthcare, entertainment, and education. But, it's not all fun and games. Generative AI also brings up some serious questions about privacy, fairness, and making sure we use it in good ways. This paper dives into all that, showing the good and the tricky parts of generative AI and how it's shaping our world. Key Words: Generative AI, technology, machine creativity, data patterns, applications, healthcare, entertainment, education, privacy, fairness, ethical use, societal impact.

О гражданско-правовом положении и некоторых проблемах правового регулирования искусственного интеллекта

individuals and legal entities are analyzed. It has been established that developers of artificial intelligence, and those persons who implement programs based on machine learning, in practice should not only take a responsible approach to solving issues of servicing artificial intelligence systems, but also understand that the result of machine creativity should be (and in many cases is already) under civil protection. In this regard, today a significant issue of development in the field of artificial intelligence is to increase the transparency of algorithms and expand the possibilities for their verification. It is suggested that a ‘strong’ AI may become a full-fledged participant in civil legal relations in the future.

Generative Artificial Intelligence—the New Era of Creative Machines

Generative Artificial Intelligence—the New Era of Creative Machines

Responding to the Call: Exploring Automatic Music Composition Using a Knowledge-Enhanced Model

Call-and-response is a musical technique that enriches the creativity of music, crafting coherent musical ideas that mirror the back-and-forth nature of human dialogue with distinct musical characteristics. Although this technique is integral to numerous musical compositions, it remains largely uncharted in automatic music composition. To enhance the creativity of machine-composed music, we first introduce the Call-Response Dataset (CRD) containing 19,155 annotated musical pairs and crafted comprehensive objective evaluation metrics for musical assessment. Then, we design a knowledge-enhanced learning-based method to bridge the gap between human and machine creativity. Specifically, we train the composition module using the call-response pairs, supplementing it with musical knowledge in terms of rhythm, melody, and harmony. Our experimental results underscore that our proposed model adeptly produces a wide variety of creative responses for various musical calls.

Not “what”, but “where is creativity?”: towards a relational-materialist approach to generative AI

AbstractThe recent emergence of generative AI software as viable tools for use in the cultural and creative industries has sparked debates about the potential for “creativity” to be automated and “augmented” by algorithmic machines. Such discussions, however, begin from an ontological position, attempting to define creativity by either falling prey to universalism (i.e. “creativity is X”) or reductionism (i.e. “only humans can be truly creative” or “human creativity will be fully replaced by creative machines”). Furthermore, such an approach evades addressing the real and material impacts of AI on creative labour in these industries. This article thus offers more expansive methodological and conceptual approaches to the recent hype on generative AI. By combining (Csikszentmihalyi, The systems model of creativity, Springer, Dordrecht, 2014) systems view of creativity, in which we emphasise the shift from “what” to “where” is creativity, with (Lievrouw, Media technologies, The MIT Press, 2014) relational-materialist theory of “mediation”, we argue that the study of “creativity” in the context of generative AI must be attentive to the interactions between technologies, practices, and social arrangements. When exploring the relational space between these elements, three core concepts become pertinent: creative labour, automation, and distributed agency. Critiquing “creativity” through these conceptual lenses allows us to re-situate the use of generative AI within discourses of labour in post-industrial capitalism and brings us to a conceptualisation of creativity that privileges neither the human user nor machine algorithm but instead emphasises a relational and distributed form of agency.

Analysis and Prediction of Students’ Psychological Characteristics of Law Breaking and Crime Based on Support Vector Machine Algorithm

Abstract In recent years, some people with high intelligence and high quality have violated the law and even committed crimes, which has ruined their bright future. It is really sad. A school is a place where teenagers learn skills and improve their quality. It should be a pure land. The frequent occurrence of juvenile students’ crimes seriously affects the normal teaching order and endangers the safety of students’ lives and property. With the rapid development of support vector machines (Abbreviation: SVM), it has also become one of the main data fusion technologies. SVM is a creative machine learning method based on statistical learning theory. It is a learning method specialized in finite sample prediction. This paper studies the psychological characteristics of students’ crimes based on the SVM algorithm. From the aspect of proportion, the result of the SVM algorithm is ideal, and the proportion of the training set is up to 33.35%. The above algorithm has good expansibility for such sample data, but when the parameters are not appropriate, the proportion is only more than 20.12%, which once again explains the importance of the SVM algorithm. Only through the SVM algorithm to improve the efforts of the whole society, can we put an end to the generation of criminal psychology of higher vocational students from the source and cultivate more high-quality talents for the country.

AI vs humans in the AUT: Simulations to LLMs

This paper reviews studies of proposed creative machines applied to a prototypical creative task, i.e., the Alternative Uses Task (AUT). Although one system (OROC) did simulate some aspects of human strategies for the AUT, most recent attempts have not been simulation-oriented, but rather have used Large Language Model (LLM) systems such as GPT-3 which embody extremely large connectionist networks trained on huge volumes of textual data. Studies reviewed here indicate that LLM based systems are performing on the AUT at near or somewhat above human levels in terms of scores on originality and usefulness. Moreover, similar patterns are found in the data of humans and LLM models in the AUT, such as output order effects and a negative association between originality and value or utility. However, it is concluded that GPT-3 and similar systems, despite generating novel and useful responses, do not display creativity as they lack agency and are purely algorithmic. LLM studies so far in this area have largely been exploratory and future studies should guard against possible training data contamination.

Best humans still outperform artificial intelligence in a creative divergent thinking task

Creativity has traditionally been considered an ability exclusive to human beings. However, the rapid development of artificial intelligence (AI) has resulted in generative AI chatbots that can produce high-quality artworks, raising questions about the differences between human and machine creativity. In this study, we compared the creativity of humans (n = 256) with that of three current AI chatbots using the alternate uses task (AUT), which is the most used divergent thinking task. Participants were asked to generate uncommon and creative uses for everyday objects. On average, the AI chatbots outperformed human participants. While human responses included poor-quality ideas, the chatbots generally produced more creative responses. However, the best human ideas still matched or exceed those of the chatbots. While this study highlights the potential of AI as a tool to enhance creativity, it also underscores the unique and complex nature of human creativity that may be difficult to fully replicate or surpass with AI technology. The study provides insights into the relationship between human and machine creativity, which is related to important questions about the future of creative work in the age of AI.

An adaptive semi-supervised deep learning-based framework for the detection of Android malware

Positive developments in smartphone usage have led to an increase in malicious attacks, particularly targeting Android mobile devices. Android has been a primary target for malware exploiting security vulnerabilities due to the presence of critical applications, such as banking applications. Several machine learning-based models for mobile malware detection have been developed recently, but significant research is needed to achieve optimal efficiency and performance. The proliferation of Android devices and the increasing threat of mobile malware have made it imperative to develop effective methods for detecting malicious apps. This study proposes a robust hybrid deep learning-based approach for detecting and predicting Android malware that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). It also presents a creative machine learning-based strategy for dealing with unbalanced datasets, which can mislead the training algorithm during classification. The proposed strategy helps to improve method performance and mitigate over- and under-fitting concerns. The proposed model effectively detects Android malware. It extracts both temporal and spatial features from the dataset. A well-known Drebin dataset was used to train and evaluate the efficacy of all creative frameworks regarding the accuracy, sensitivity, MAE, RMSE, and AUC. The empirical finding proclaims the projected hybrid ConvLSTM model achieved remarkable performance with an accuracy of 0.99, a sensitivity of 0.99, and an AUC of 0.99. The proposed model outperforms standard machine learning-based algorithms in detecting malicious apps and provides a promising framework for real-time Android malware detection.

GENERATIVE PRACTICES IN CREATIVE INDUSTRIES: THE PHYGITAL ASPECT OF ARTISTIC DESIGN

The concept of phygital (from the words "physical" and "digital"), as the name implies, involves the synthesis of physical and digital components. This concept originated in the field of marketing; however, today it is increasingly used in various fields of science, including art history, and reflects a new trend in the world of art and design when the boundaries between offline and online environments are blurred. Earlier, the Internet was used mainly as a tool for promotion and communication, whereas in recent years the virtual and the real are beginning to interact more closely. The transformation of the virtual into the real and vice versa is becoming one of the key areas of contemporary art; more and more works exist "at the junction" of artistic expression and design solutions. In this regard, researchers of modern culture and art are faced with a number of questions about the nature and aesthetic potential of phygital. The purpose of this study was to analyse a number of representative projects of creative industries created at the intersection of analog and digital representation, to identify their specifics and expressive possibilities, as well as to clarify the specifics of image transformation during online-offline transgression. The results of the study revealed that today a special place in the field of phygital projects is occupied by works made by designers and artists using generative models. This is facilitated by the active development of machine learning. In particular, we are talking about artificial intelligence and its capabilities, which have recently become the focus of attention of artists, designers, and researchers of contemporary art. In 2019, the work Neural Street Art appeared on the wall of a multi-storey building in Yekaterinburg (Popova St., 9). With the help of machine algorithms, a mosaic, created by an artist in the 4th century AD to decorate the Spanish villa La Olmeda, was recreated. The image was applied to the building by a robot printer developed by the STENOGRAFFIFA creative team. The work of a “distributed author”, a meta-author [6] according to P.Galanter (an unknown artist of the Flavian era, the Yandex neural network, a robot-printer, creative teams that made the corresponding technologies and software), raises questions about the connection between digital and physical and the prospects of a generative human - machine creativity. Increasingly, representatives of creative industries are using neural networks to create works: for example, Scott Eaton demonstrated sculptures created “in collaboration” with AI at the Artist + AI: Figures and Forms exhibition, in particular, the bronze Human Allocation of Space (2019). At the same time, digital copies of their works are often created for artists using NFT technologies. For example, Filipino artist Bjorn Calleja, known for his NFT projects, creates tiny sculptures of his animated characters and then digitises them again as NFT tokens (in .glb format for 3D models). More and more works of artists and designers created with the help of neural networks (GAN - generative-adversarial neural network, CAN - creative-adversarial neural network) acquire not just a physical but a phygital embodiment. This is not only about "twinning", i.e. NFT twins of physical copies of objects, but about hybrid works and installations that exist simultaneously in digital and physical spaces. Thus, we came to the conclusion about the special role of artificial intelligence in modern phygital projects in creative industries, as well as the importance of the development of virtual and augmented reality as a space for the existence of works of this type. This study outlines the advantages of the phygital format for the designer's work, as well as the expressive possibilities of online-offline transition in the space of contemporary art, classifies art spaces at the intersection of physical and digital, features phygital as a means of communication relevant to today's visual culture.

Pervasive Computing in Space

Imagine “astronauts working in a lunar habitat with Internet of Things devices.” In response to such a simple prompt, within seconds a generative AI rendered the image above, which enamored this issue's guest editors with its fanciful, seemingly humorous depiction of what appears to be a pair of space-suited engineers engaged in something like a hackathon while sitting in the lunar dust with coffee cups nearby. Beyond being a fun way to kick off this Special Issue on Pervasive Computing in Space, this image underscores how quickly world-changing technology is now being unleashed. Just within the last year or so, AI has moved beyond mastering niche applications and into popular use with increasingly sophisticated chatbots and image-generating environments that win art contests alongside humans. Regardless of the intensifying discussion this sparks about machine creativity and intelligence, AI has now become a quickly moving landscape. We do not know how far this ride will go and where the next roadblocks, always close-at-hand in previous years, will manifest.

How is Technical Creativity Possible?

The article deals with the problem of sources, possibilities and limitations of technical creativity. Technical creativity is defined in the article as the creation of objects that are qualitatively different from all previously created artifacts. The problem of impossibility, improbability and unthinkability of creativity is analyzed. The possibility of its solution in the case of technical creativity is considered. The role of the subject and the environment in the emergence of new objects is revealed. The limitations and conditions of inventive activity are considered, including the role of previously created technical devices. The methodological basis of the work is the three-act concept of P.K. Engelmeyer and the theory of affordances (possibility) by D. Gibson. It is shown that in ontological terms, technical creativity can be considered as the realization of those possibilities that are determined by the characteristics of the environment, in particular, physical laws, but due to various circumstances are not updated without the participation of the subject. At the same time, technical evolution should be considered in the unity of its own internal laws and human activity. Internal patterns are due to the fact that the emergence of new devices creates new opportunities (affordances) for constructive human activity. The role of the subject in this case is to actualize the potential, that is, the practical implementation of those opportunities that appear in the "man-environment" system. The limitations of technical creativity are logical, physical, paradigm consistency and praxeological admissibility. On the example of generative neural networks, the possibilities and limitations of machine creativity are considered.

DeepCreativity: measuring creativity with deep learning techniques

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.

The Strangest Music in the World: Self-Supervised Creativity and Nostalgia for the Future in Robotic Rock Band “The Three Sirens”

The emergence of deep learning since the mid-2010s and its successful application to creative activity challenges long-held anthropocentric conceptions of art and music, bringing back ideas about machine creativity that had been previously explored in the 20th century. Particularly, in the 1990s, some artists, composers, and musicians started working with machine learning and other adaptive computation systems. The work of Nicolas Baginsky is emblematic of that era. In 1992, he created the robot guitar Aglaopheme, which became the first performer of a self-learning robotic band developed throughout the 1990s, soon joined by the robot bass Peisinoe, the robot drum Thelxiepeia, and eventually other artificial agents, forming the autonomous robotic band The Three Sirens. In this review, we describe the technological, musical, and imaginative aspects of Baginsky’s robotic instruments. The unreal and behind-the-scenes story of the mythological three sirens is important in understanding how the robots are designed and what they (are) intend(ed) to do. In the context of artificial intelligence, the concept of seeking a surprising musical effect will push us to reimagine such concepts as musical creativity and improvisation within the algorithmic composition and provide opportunities to discuss nostalgia for the future music and live performance.

AI Composers and Cyborg Performers: More-than-human Creativity in Music

Abstract This article examines the integration of artificial intelligence in music, exploring its potential to redefine traditional creative boundaries and establish a more-than-human co-creativity. It discusses theoretical frameworks by Margret Boden, Donna Haraway, and Bruno Latour, presenting AI not just as a tool, but as an active creative participant. The paper addresses ethical concerns about AI's role in music, including authorship and copyright, advocating for a reevaluation of musicological frameworks to accommodate the evolving collaborative landscape of human and machine creativity.

The Lovelace effect: Perceptions of creativity in machines

This article proposes the notion of the ‘Lovelace Effect’ as an analytical tool to identify situations in which the behaviour of computing systems is perceived by users as original and creative. It contrasts the Lovelace Effect with the more commonly known ‘Lovelace objection’, which claims that computers cannot originate or create anything, but only do what their programmers instruct them to do. By analysing the case study of AICAN – an AI art-generating system – we argue for the need for approaches in computational creativity to shift focus from what computers are able to do in ontological terms to the perceptions of human users who enter into interactions with them. The case study illuminates how the Lovelace effect can be facilitated through technical but also through representational means, such as the situations and cultural contexts in which users are invited to interact with the AI.