Cut/Generate: Montage and AI

Journal of Perceptual Imaging, 2021

Recent developments in neural network image processing motivate the question, how these technologies might better serve visual artists. Research goals to date have largely focused on either pastiche interpretations of what is framed as artistic “style” or seek to divulge heretofore unimaginable dimensions of algorithmic “latent space,” but have failed to address the process an artist might actually pursue, when engaged in the reflective act of developing an image from imagination and lived experience. The tools, in other words, are constituted in research demonstrations rather than as tools of creative expression. In this article, the authors explore the phenomenology of the creative environment afforded by artificially intelligent image transformation and generation, drawn from autoethnographic reviews of the authors’ individual approaches to artificial intelligence (AI) art. They offer a post-phenomenology of “neural media” such that visual artists may begin to work with AI techno...

ISEA PROCEEDINGS WHY SENTIENCE? ONLINE FROM MONTREAL, CANADA , 2020

This paper considers the Artificial Intelligence (A.I.) renderings from the website “This Person Does Not Exist” (TPDNE), published in early 2019, which are high resolution images that are often indistinguishable from actual photographs. While learning algorithms are widely critiqued for amplifying bias and hierarchy within already biased data sets, the learning algorithm that generates the TPDNE images proposes an alternative potential. The TPDNE image makes an opening to experience the co-composition of personhood and photographic media. What is at play in the TPDNE image is not the replacement of a human intelligence for an artificial one, but a reconstitution of the photographic portrait as a spacetime of generative encounter. This encounter both exposes and dampens the way the photographic image is enmeshed in subjective affects and agencies aligned with a human-centered concept of personhood, capture and extraction. In this paper, the TPDNE portrait is figured as a gestural opening to problematize such agencies as they are reconfigured in computational spacetime. The TPDNE image performs the possibility of a sense transformation that allows for imagining new and heterogeneously co-constituted subjectivities that traverse registers of representation, technology and corporeality.

2024

The use of terms: In this chapter, the terms generative media, synthetic media, or generative AI refer to the process of synthesizing media objects with artificial neural networks. The examples of such objects are include text, voice, music, 3D models, datasets, and computer code. The terms generative image, AI Image or visual AI refer to specially synthesized visual objects. These objects can be still images that imitate the appearance and structure of all types of visual media, from photographs to drawings, and also moving images that imitate appearances of animation and video. Artificial Aesthetics (Chapter 7)-2 Separate and Reassemble AI image represents a further logical evolution of the process that begins with digital media algorithms in the 1970s and continues in the following decades. The first computer paint programs were created in the 1970s, but could not yet simulate different paint types, brushes, and textured surfaces like canvas. But in the 1990s, software such as Coral 1 Painter (1991-) started to offer these features. Similarly, the first 3D computer graphics 2 algorithms for rendering solid shapes, Gouraud shading (1971) and Phong shading (1973), couldn't yet simulate the looks of different materials. Later, in the 1970s and 1980s, computer graphics researchers created numerous algorithms to simulate the appearance of various materials and textures, such as cloth, hair, and skin, as well as shadows, transparency, translucency, depth of field, lens flares, motion blur, reflections, water, smoke, fireworks, explosions, and other natural phenomena and cinematography techniques and effects. Simulating many of these phenomena and techniques requires multiple separate algorithms that were developed over time. Thus, we find distinct sessions devoted to such algorithms with names like Volumes and Materials, Fluid Simulation, or Cloth and Shells in the annual proceedings of SIGGRAPH, the main conference in CG field. As an 3 example, the paper "Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networks" presented in 2023 conference describes "a learning algorithm that uses bone-driven motion networks to predict the deformation of loose-fitting garment meshes at interactive rates." Another conference paper "Rendering Iridescent Rock Dove Neck Feathers" describes a new approach for modeling and rendering bird feathers; and so on.

2022

Now you can find the whole text of the thesis which resulted from this research in the repo: https://urn.fi/URN:ISBN:978-952-353-460-5

NECSUS European Journal of Media Studies, 2020

Abstract The article introduces the NECSUS Spring 2020 Special Section #Intelligence (https://necsus-ejms.org/portfolio/spring-2020_intelligence/#toggle-id-2), that includes seven essays addressing the impact of Artificial Intelligence on cinema and media from a cultural perspective. More particularly, three levels of pertinence are focused. At a first level, selected papers analyse several representations of non-human intelligence confronted with human one, as provided by film, television series, and video games. At a second level, a set of mutual functioning dynamic between A.I. and the media are identified and scrutinised. Finally, the contributing authors consider how A.I. algorithms lead cinema and media theory to deeply rethink its assumptions about creating and viewing moving images.

Journal of Human-Technology Relations, 2023

Artificial intelligence (AI); DALL-E; art; aesthetics; philosophy of technology; process philosophy; performance AI image generators such as DALL-E 2 are deep learning models that enable users to generate digital images based on natural language text prompts. The impressive and often surprising results leave many people puzzled: is this art, and if so, who created the art: the human or the AI? These are not just theoretical questions; they have practical ethical and legal implications, for example when raising authorship and copyright issues. This essay offers two conceptual points of entrance that may help to understand what is going on here. First it briefly discusses the question whether this is art and who or what is the artist based on aesthetics, philosophy of art, and thinking about creativity and computing. Then it asks the question regarding humantechnology relations. It shows that existing notions such as instrument, extension, and (quasi) other are insufficient to conceptualize the use of this technology, and proposes instead to understand what happens as processes and performances, in which artistic subjects, objects, and roles emerge. It is concluded that based on most standard criteria in aesthetics, AI image generation can in principle create art, and that the process can be seen as poietic performances involving humans and non-humans potentially leading to the emergence of new artistic (quasi)subjects and roles in the process.

2022

This dataset captures cultural attitudes towards machine vision technologies as they are expressed in art, games and narratives. The dataset includes records of 500 creative works (including 77 digital games, 190 digital artworks and 233 movies, novels and other narratives) that use or represent machine vision technologies like facial recognition, deepfakes, and augmented reality. The dataset is divided into three main tables, relating to the works, to specific situations in each work involving machine vision technologies, and to the characters that interact with the technologies. Data about each work includes title, author, year and country of publication; types of machine vision technologies featured; topics the work addresses, and sentiments associated with that machine vision usage in the work. In the various works we identified 874 specific situations where machine vision is central. The dataset includes detailed data about each of these situations that describes the actions of...

Applications, Problems and Solutions

This chapter attempts to consider the consequences of machine vision technologies for the role of the image in the visual arts. After a short introduction, the text gives a practical overview of image processing techniques that are relevant in surveillance, installation, and information art practice. Example work by practitioners in the field contextualizes these more technical descriptions and shows how computational approaches to digital imagery can radically expand the use of the image in the arts. A final note on possible future areas of investigation is included.

Digital Da Vinci, 2014

Since the development of Artificial Intelligence (AI) systems, their pervasiveness has rapidly grown to the point of gaining access to one of the most typical among human activities, the one of art. In the latest years we have indeed witnessed advances outlining AI’s “creative” abilities, now finding applications in the fields of visual art, literature, poetry, and music. As a matter of fact, in many cases, people are no longer able to easily discern what is AI-made from what is human-made and show often a negative bias towards artistic products that are declared to be AI-made. Even though such technologies are capable of rapidly and efficiently generating images, texts, and music, that often are also pleasant, the history of art and aesthetics suggests that the works of art – those that we recognize as such over the centuries – have little to do with technical ability, and rather rely on aesthetic principles, of which the artwork per se is merely representative. We therefore propos...