Demolish Monsters on the Rocks: Prompting Through an Ensemble

bruce gilchrist

As interaction with corporate artificial intelligence increasingly becomes a precondition for contemporary life, artists need to see beyond Generative AI (GenAI) technology as a discrete tool that makes generic products. Instead, they can imagine combinatorial approaches and conceptual frameworks for AI-enabled artworks. Through my practice-based research, the act of prompting multimodal GenAI models has been informed by comprehending an assemblage as a “framework of instruction” held together through poetic alliances, within which the output from one component feeds the process of another. Practical experiments explored an interrelation of body, text, and predictive technology, where an algorithmic prediction of human action conjured “biometric poetry” that was used to stimulate a language model. Working with archival film footage and digital puppets animated with motion-capture files gave rise to the idea of a camera’s field of view – with its bounded contents acting like a key – eliciting value from a language model in a novel form of story making. Potential erroneous inferences were perceived as a new form of chance operation and a characteristic of algorithmic remix as defined by Steve F. Anderson. This method has been further developed in a project that combines performance, waste material, object recognition, and a language model to explore how the manipulation of garbage can be rationalised by a machine to produce poetic texts as a commentary to action portrayed on a screen.

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01 Introduction

The aim of this project is to recast Generative AI (GenAI) as part of a dynamic assemblage of predictive technologies and performance-based media, through which prompting is approached not as a technical command but as a compositional act within a broader framework of instruction; output from one system informs the input of another in a recursive, intermedia process. This experimental interplay of text, body, and algorithm produces what might be termed biometric poetry through a speculative methodology that treats algorithmic error, misclassification, and ambiguity as a productive force. This combinatorial approach aligns with broader critical discourses that reveal the sociopolitical entanglements of AI, to envision alternative ways of seeing, knowing, and making in the age of machine vision.

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As interaction with corporate artificial intelligence (AI) increasingly becomes a precondition of modern life, and the AI industry reconfigures itself in the multimodal era, artists are challenged to expand their perspective beyond viewing Generative AI (GenAI) as a discrete tool for creating generic outputs. The increasing availability of new tools and techniques offers unprecedented opportunities to repurpose and manipulate materials in innovative ways, raising complex questions about authorship, copyright, appropriation, and collaboration. Steve F. Anderson’s notion of a “classical age of remix”, which spans both analogue and digital realms, hinges on the remixability of discrete elements from visual and written culture. We are now in what Anderson has referred to as “an algorithmic period of remix”, in which machine learning algorithms analyse the characteristics of training datasets to generate “new” outputs based on those traits (Anderson 2021). While remixing as an artistic practice continues to hold cultural significance through its perpetual recycling of materials, algorithmic remix introduces new processes. The traditional “cut/copy and paste” metaphor of remix is now replaced by “digestion” and “synthesis”, where autoencoders decode and synthesise inputs. GenAI presents possibilities for reimagining artistic methodologies, potentially transforming how artists both conceptualise and produce their work. My project builds on the notion of algorithmic remix through the placement of GenAI within an assemblage of predictive technologies and performance-based narrative media. Central to this approach is an engagement with the aesthetics of error, which includes erroneous classifications and ambiguities that emerge from within the systems themselves, viewed as productive material capable of disrupting dominant narratives of computational precision and control. This strategy situates artistic practice within a wider critical discourse that questions the constructed nature of digital systems and the ways in which they mediate knowledge, identity, and agency. As datasets and algorithms increasingly shape how knowledge is produced, organised, and disseminated, a parallel movement within academic and artistic circles seeks to expose the sociopolitical entanglements embedded in these systems.

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