Poem Machine - a Co-creative NLG Web Application for Poem Writing

Peer reviewe

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

ELO2019: Electronic Literature Organization Conference & Media Arts Festival, Programme and Book of Abstracts

The Electronic Literature Organization (ELO) is pleased to announce its 2019 Conference and Media Arts Festival, hosted by University College Cork. The conference and exhibition will be held from July 15-17, 2019, on UCC’s campus in the heart of Cork city, Ireland. The theme for ELO2019 #ELOcork is “peripheries”: delegates are invited to explore the edges of literary and digital culture, including emerging traditions, indeterminate structures and processes, fringe communities of praxis, effaced forms and genres, marginalised bodies, and perceptual failings. ELO2019 #ELOcork will mark the first time that the ELO conference has been hosted by an Irish institution: join us for this momentous gathering

ELO2019 Programme & Books of Abstracts

ELO2019 Programme & Books of Abstracts, University College Cork, July 15-17, 201

On the Creativity of Large Language Models

Large Language Models (LLMs) are revolutionizing several areas of Artificial Intelligence. One of the most remarkable applications is creative writing, e.g., poetry or storytelling: the generated outputs are often of astonishing quality. However, a natural question arises: can LLMs be really considered creative? In this article we firstly analyze the development of LLMs under the lens of creativity theories, investigating the key open questions and challenges. Then, we discuss a set of "easy" and "hard" problems in machine creativity, presenting them in relation to LLMs. Finally, we examine the societal impact of these technologies with a particular focus on the creative industries

The Poetry of Prompts: The Collaborative Role of Generative Artificial Intelligence in the Creation of Poetry and the Anxiety of Machine Influence

2022 has been heralded as the year of generative artificial intelligence (AI). Generative AI like ChatGPT and Stable Diffusion, along with a host of others, launched late in the year and immediately disrupted the status quo of the literary and art worlds, leading to outcries to ban “AI Art” and spawning an entirely new market of NFTs. Fears over the “death of the artist” and the “death of college composition,” however, are unfounded when considering the historical adoption of emerging technologies by creatives and the reconsideration of authorship that began with post structuralism and the Foucauldian Death of the Author in 1967. Contemporary scholarship has faced challenges in reconciling the function of the human author in conjunction with artificial intelligence (AI) due to the progressive sophistication and selfsufficiency of generative code. Nonetheless, it is erroneous to establish the threshold for authorship based on the development or advancement of AI or robotics, as it falls within the realm of ontology. Instead, assertions of AI authorship stem from a romanticized perception of both authorship and AI during a period in which neither holds significance. A new discussion on the role of the human agent in the writing process, particularly in the creative process like poetry, should prioritize the practical aspects of what an author does. This study examines how AI is increasingly becoming involved in collaborative efforts to create poetry and aims to explore the potential of this trend. Furthermore, the study seeks to provide empirical evidence on the boundaries of AI\u27s ability to replicate human thought and experience. Through generating content in the creative written arts using ChatGPT-3, poetry analysis revealed that, in fact, such new generative models can imitate the vocabulary, language choices, style, and even rhythm of famous poets such as Keats, it is unable to generate emotions that it has not experienced. The questions that will continue to be raised on the nature of humanity, existence, and creative capabilities should be reframed with the concept of fear fore grounded to assist in understanding the uniquely human anxiety and drive to create in an attempt to communicate across the gulf what it “feels” like to be human as a phenomenology of experience

The Poetry of Prompts: The Collaborative Role of Generative Artificial Intelligence in the Creation of Poetry and the Anxiety of Machine Influence

2022 has been heralded as the year of generative artificial intelligence AI Generative AI like ChatGPT and Stable Diffusion along with a host of others launched late in the year and immediately disrupted the status quo of the literary and artworlds leading to outcries to ban AI Art and spawning an entirely new market of NFTs Fears over the death of the artist and the death of college composition however are unfounded when considering the historical adoption of emerging technologies by creatives and the reconsideration of authorship that began with poststructuralism and the Foucauldian Death of the Author in 196

Natural language generation as neural sequence learning and beyond

Natural Language Generation (NLG) is the task of generating natural language (e.g., English sentences) from machine readable input. In the past few years, deep neural networks have received great attention from the natural language processing community due to impressive performance across different tasks. This thesis addresses NLG problems with deep neural networks from two different modeling views. Under the first view, natural language sentences are modelled as sequences of words, which greatly simplifies their representation and allows us to apply classic sequence modelling neural networks (i.e., recurrent neural networks) to various NLG tasks. Under the second view, natural language sentences are modelled as dependency trees, which are more expressive and allow to capture linguistic generalisations leading to neural models which operate on tree structures. Specifically, this thesis develops several novel neural models for natural language generation. Contrary to many existing models which aim to generate a single sentence, we propose a novel hierarchical recurrent neural network architecture to represent and generate multiple sentences. Beyond the hierarchical recurrent structure, we also propose a means to model context dynamically during generation. We apply this model to the task of Chinese poetry generation and show that it outperforms competitive poetry generation systems. Neural based natural language generation models usually work well when there is a lot of training data. When the training data is not sufficient, prior knowledge for the task at hand becomes very important. To this end, we propose a deep reinforcement learning framework to inject prior knowledge into neural based NLG models and apply it to sentence simplification. Experimental results show promising performance using our reinforcement learning framework. Both poetry generation and sentence simplification are tackled with models following the sequence learning view, where sentences are treated as word sequences. In this thesis, we also explore how to generate natural language sentences as tree structures. We propose a neural model, which combines the advantages of syntactic structure and recurrent neural networks. More concretely, our model defines the probability of a sentence by estimating the generation probability of its dependency tree. At each time step, a node is generated based on the representation of the generated subtree. We show experimentally that this model achieves good performance in language modeling and can also generate dependency trees