Creativity and Machine Learning: a Survey

There is a growing interest in the area of machine learning and creativity. This survey presents an overview of the history and the state of the art of computational creativity theories, machine learning techniques, including generative deep learning, and corresponding automatic evaluation methods. After presenting a critical discussion of the key contributions in this area, we outline the current research challenges and emerging opportunities in this field.Comment: 25 pages, 3 figures, 2 table

Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities

Recent advances in artificial general intelligence (AGI), particularly large language models and creative image generation systems have demonstrated impressive capabilities on diverse tasks spanning the arts and humanities. However, the swift evolution of AGI has also raised critical questions about its responsible deployment in these culturally significant domains traditionally seen as profoundly human. This paper provides a comprehensive analysis of the applications and implications of AGI for text, graphics, audio, and video pertaining to arts and the humanities. We survey cutting-edge systems and their usage in areas ranging from poetry to history, marketing to film, and communication to classical art. We outline substantial concerns pertaining to factuality, toxicity, biases, and public safety in AGI systems, and propose mitigation strategies. The paper argues for multi-stakeholder collaboration to ensure AGI promotes creativity, knowledge, and cultural values without undermining truth or human dignity. Our timely contribution summarizes a rapidly developing field, highlighting promising directions while advocating for responsible progress centering on human flourishing. The analysis lays the groundwork for further research on aligning AGI's technological capacities with enduring social goods

Grammalepsy

This book is available as open access through the Bloomsbury Open Access programme and is available on www.bloomsburycollections.com. Collecting and recontextualizing writings from the last twenty years of John Cayley's research-based practice of electronic literature, Grammalepsy introduces a theory of aesthetic linguistic practice developed specifically for the making and critical appreciation of language art in digital media. As he examines the cultural shift away from traditional print literature and the changes in our culture of reading, Cayley coins the term “grammalepsy” to inform those processes by which we make, understand, and appreciate language. Framing his previous writings within the overall context of this theory, Cayley eschews the tendency of literary critics and writers to reduce aesthetic linguistic making-even when it has multimedia affordances-to “writing.” Instead, Cayley argues that electronic literature and digital language art allow aesthetic language makers to embrace a compositional practice inextricably involved with digital media, which cannot be reduced to print-dependent textuality

Affordances and limitations of algorithmic criticism

Humanities scholars currently have access to unprecedented quantities of machine-readable texts, and, at the same time, the tools and the methods with which we can analyse and visualise these texts are becoming more and more sophisticated. As has been shown in numerous studies, many of the new technical possibilities that emerge from fields such as text mining and natural language processing can have useful applications within literary research. Computational methods can help literary scholars to discover interesting trends and correlations within massive text collections, and they can enable a thoroughly systematic examination of the stylistic properties of literary works. While such computer-assisted forms of reading have proven invaluable for research in the field of literary history, relatively few studies have applied these technologies to expand or to transform the ways in which we can interpret literary texts. Based on a comparative analysis of digital scholarship and traditional scholarship, this thesis critically examines the possibilities and the limitations of a computer-based literary criticism. It argues that quantitative analyses of data about literary techniques can often reveal surprising qualities of works of literature, which can, in turn, lead to new interpretative readings

TwitSong: A current events computer poet and the thorny problem of assessment.

This thesis is driven by the question of how computers can generate poetry, and how that poetry can be evaluated. We survey existing work on computer-generated poetry and interdisciplinary work on how to evaluate this type of computer-generated creative product. We perform experiments illuminating issues in evaluation which are specific to poetry. Finally, we produce and evaluate three versions of our own generative poetry system, TwitSong, which generates poetry based on the news, evaluates the desired qualities of the lines that it chooses, and, in its final form, can make targeted and goal-directed edits to its own work. While TwitSong does not turn out to produce poetry comparable to that of a human, it represents an advancement on the state of the art in its genre of computer-generated poetry, particularly in its ability to edit for qualities like topicality and emotion

Investigating features and techniques for Arabic authoriship attribution

Authorship attribution is the problem of identifying the true author of a disputed text. Throughout history, there have been many examples of this problem concerned with revealing genuine authors of works of literature that were published anonymously, and in some cases where more than one author claimed authorship of the disputed text. There has been considerable research effort into trying to solve this problem. Initially these efforts were based on statistical patterns, and more recently they have centred on a range of techniques from artificial intelligence. An important early breakthrough was achieved by Mosteller and Wallace in 1964 [15], who pioneered the use of ‘function words’ – typically pronouns, conjunctions and prepositions – as the features on which to base the discovery of patterns of usage relevant to specific authors. The authorship attribution problem has been tackled in many languages, but predominantly in the English language. In this thesis the problem is addressed for the first time in the Arabic Language. We therefore investigate whether the concept of functions words in English can also be used in the same way for authorship attribution in Arabic. We also describe and evaluate a hybrid of evolutionary algorithms and linear discriminant analysis as an approach to learn a model that classifies the author of a text, based on features derived from Arabic function words. The main target of the hybrid algorithm is to find a subset of features that can robustly and accurately classify disputed texts in unseen data. The hybrid algorithm also aims to do this with relatively small subsets of features. A specialised dataset was produced for this work, based on a collection of 14 Arabic books of different natures, representing a collection of six authors. This dataset was processed into training and test partitions in a way that provides a diverse collection of challenges for any authorship attribution approach. The combination of the successful list of Arabic function words and the hybrid algorithm for classification led to satisfying levels of accuracy in determining the author of portions of the texts in test data. The work described here is the first (to our knowledge) that investigates authorship attribution in the Arabic knowledge using computational methods. Among its contributions are: the first set of Arabic function words, the first specialised dataset aimed at testing Arabic authorship attribution methods, a new hybrid algorithm for classifying authors based on patterns derived from these function words, and, finally, a number of ideas and variants regarding how to use function words in association with character level features, leading in some cases to more accurate results

Perceptual fail: Female power, mobile technologies and images of self

Like a biological species, images of self have descended and modified throughout their journey down the ages, interweaving and recharging their viability with the necessary interjections from culture, tools and technology. Part of this journey has seen images of self also become an intrinsic function within the narratives about female power; consider Helen of Troy “a face that launched a thousand ships” (Marlowe, 1604) or Kim Kardashian (KUWTK) who heralded in the mass mediated ‘selfie’ as a social practice. The interweaving process itself sees the image oscillate between naturalized ‘icon’ and idealized ‘symbol’ of what the person looked like and/or aspired to become. These public images can confirm or constitute beauty ideals as well as influence (via imitation) behaviour and mannerisms, and as such the viewers belief in the veracity of the representative image also becomes intrinsically political manipulating the associated narratives and fostering prejudice (Dobson 2015, Korsmeyer 2004, Pollock 2003). The selfie is arguably ‘a sui generis,’ whilst it is a mediated photographic image of self, it contains its own codes of communication and decorum that fostered the formation of numerous new digital communities and influenced new media aesthetics . For example the selfie is both of nature (it is still a time based piece of documentation) and known to be perceptually untrue (filtered, modified and full of artifice). The paper will seek to demonstrate how selfie culture is infused both by considerable levels of perceptual failings that are now central to contemporary celebrity culture and its’ notion of glamour which in turn is intrinsically linked (but not solely defined) by the province of feminine desire for reinvention, transformation or “self-sexualisation” (Hall, West and McIntyre, 2012). The subject, like the Kardashians or selfies, is divisive. In conclusion this paper will explore the paradox of the perceptual failings at play within selfie culture more broadly, like ‘Reality TV’ selfies are infamously fake yet seem to provide Debord’s (1967) illusory cultural opiate whilst fulfilling a cultural longing. Questions then emerge when considering the narrative impact of these trends on engendered power structures and the traditional status of illusion and narrative fiction

Universal Intelligence: A Definition of Machine Intelligence

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.Comment: 50 gentle page