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"Generative art."
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Generative AI art for beginners : Midjourney & the tactics of killer text prompts
Learn to use powerful tools like DALL-E and Midjourney to create stunning visuals and explore the exciting world of generative AI art. About This Video: Advance your skills in generating text prompts and modifiers. Learn how to create stunning visuals with AI-generated art software. Discover techniques to incorporate AI-generated art in your creative work. In Detail: Generative AI has revolutionized the art world by providing an entirely new way to create unique and stunning visuals. In today’s age of technology, it’s essential to keep up with the latest tools and techniques, and this is where this comprehensive course on AI-generated art comes in. In this course, you will learn how to create stunning visuals using the latest AI art software, including DALL-E, Midjourney, and other tools. You will begin by learning the basics of generative AI and its applications in art. From there, you will explore various AI art software and learn how to use them to create beautiful pieces of art. The course will cover everything from generating simple images to creating complex visuals and textures. You will learn how to tweak parameters, adjust styles, and create custom datasets to get the exact output you want. Additionally, you will explore the different types of generative art, including style transfer, GANs, and neural style transfer. By the end of this course, you will be well-versed in generative AI and its applications in art. You will have a solid understanding of how to use various AI art software, including DALL-E, Midjourney, and others, to create stunning visuals.
Streaming Video
The uncanny muse : music, art, and machines from automata to AI
An acclaimed critic, journalist and songwriter-musician tells the story of art's relation to machines, from the Baroque period to the age of AI.
BOOK
Deep Else: A Critical Framework for AI Art
From a small community of pioneering artists who experimented with artificial intelligence (AI) in the 1970s, AI art has expanded, gained visibility, and attained socio-cultural relevance since the second half of the 2010s. Its topics, methodologies, presentational formats, and implications are closely related to a range of disciplines engaged in the research and application of AI. In this paper, I present a comprehensive framework for the critical exploration of AI art. It comprises the context of AI art, its prominent poetic features, major issues, and possible directions. I address the poetic, expressive, and ethical layers of AI art practices within the context of contemporary art, AI research, and related disciplines. I focus on the works that exemplify poetic complexity and manifest the epistemic or political ambiguities indicative of a broader milieu of contemporary culture, AI science/technology, economy, and society. By comparing, acknowledging, and contextualizing both their accomplishments and shortcomings, I outline the prospective strategies to advance the field. The aim of this framework is to expand the existing critical discourse of AI art with new perspectives which can be used to examine the creative attributes of emerging practices and to assess their cultural significance and socio-political impact. It contributes to rethinking and redefining the art/science/technology critique in the age when the arts, together with science and technology, are becoming increasingly responsible for changing ecologies, shaping cultural values, and political normalization.
Journal Article
Generative design : visualize, program, and create with JavaScript in p5.js
Generative design, once known to insiders as a revolutionary method of creating artwork, models, and animations with programmed algorithms, has in recent years become a popular tool for designers. By using simple languages such as JavaScript in p5.js, artists and makers can create everything from interactive typography and textiles to 3D-printed furniture to complex and elegant infographics-- Provided by publisher.
Book
Evaluating photographic authenticity: How well do ChatGPT 4o and Gemini distinguish between real and AI-generated images?
As artificial intelligence (AI) advances, differentiating between actual and AI-generated images has become an increasingly difficult task. The present exploratory research looks at the capacity of two leading AI models, ChatGPT and Gemini, to correctly classify genuine vs. synthetic photographs in a variety of categories, including wildlife, portraiture, and abstract art. The present research evaluates the accuracy, mistake rates, and handling of complex visual content of both models, shedding light on their strengths and limits and providing significant insights into their possible uses in combatting visual falsehood.
Journal Article
Generative Art Theory
This chapter shows that generative computer art is in fact a subset of the larger field of generative art. It is seen that generative art can leverage virtually any kind of system, not just computers, and that it in fact is as old as art itself. The key element in generative art is the use of an external system to which the artist cedes partial or total control. This understanding moves generative art theory into discussions focused primarily on systems, their role, their relationship to creativity and authorship, system taxonomies, and so on. The chapter also considers a series of problems in generative art theory. It is notable that, for the most part, these problems equally apply to both digital and non‐digital generative art; to generative art past, present, and future; and to ordered, disordered, and complex generative art.
Book Chapter
GenerativeGI: creating generative art with genetic improvement
Generative art is a domain in which artistic output is created via a procedure or heuristic that may result in digital and/or physical results. A generative artist will typically act as a domain expert by specifying the algorithms that will form the basis of the piece as well as defining and refining parameters that can impact the results, however such efforts can require a significant amount of time to generate the final output. This article presents and extends
GenerativeGI
, an evolutionary computation-based technique for creating generative art by automatically searching through combinations of artistic techniques and their accompanying parameters to produce outputs desirable by the designer. Generative art techniques and their respective parameters are encoded within a grammar that is then the target for genetic improvement. This grammar-based approach, combined with a many-objective evolutionary algorithm, enables the designer to efficiently search through a massive number of possible outputs that reflect their aesthetic preferences. We included a total of 15 generative art techniques and performed three separate empirical evaluations, each of which targets different aesthetic preferences and varying aspects of the search heuristic. Experimental results suggest that
GenerativeGI
can produce outputs that are significantly more novel than those generated by random or single objective search. Furthermore,
GenerativeGI
produces individuals with a larger number of relevant techniques used to generate their overall composition.
Journal Article
An Abstract Painting Generation Method Based on Deep Generative Model
Computer technology provides new conditions and possibilities for art creation and research, and also expands the forms of artistic expression. Computer-created art has thus become one of the important forms of art. In this paper, we proposed a novel method of generating abstract paintings. We used the public painting dataset WikiArt and designed a
K
-Means algorithm that automatically finds the optimal
K
value to perform color segmentation on these images, and divide the picture into different color blocks. We proposed the concept of the collection of color block (CoCB), which records all color block information of the segmented image and serves as an intermediate vector for the generation of abstract painting. We extracted the CoCB as an empirical sample and used a learning model based on deep learning to automatically generate brand-new CoCBs. We then converted the CoCBs into an abstract painting, so that the generated abstract painting also followed certain aesthetic rules. Experiments showed that the resulting abstract painting have great visual impact, and some of them have been installed as decorations in public and private spaces, as well as art institutions. Also, some artists and designers have used the results in their work.
Journal Article
Patient Zer0: Creating Online Generative Art During the COVID-19 Pandemic
Patient Zer0 is an interactive generative artwork, designed around the poem “In Memory of Anyone Unknown to Me” by Elizabeth Jennings and created during the first confinements imposed by the COVID-19 pandemic in 2020. While it has been showcased in several online exhibitions, this article details and analyses, for the first time, the artwork's algorithmic approach, as well as its aesthetics, the different media components, and the artist's intentions behind their inclusion and combination. In line with Springgay's, Irwin's, and Kind's a/r/tography, a recent creative research method is presented here, a/r/cography, which is complemented by a phenomenological dimension, as the author experienced the events from a physical, intellectual, and emotional perspective, in isolation, while at home in Portugal. The whole process was documented in a digital journal, which not only supported the underlying research but also documented the artwork variants and evolution. Patient Zer0 was entirely coded in Processing.js.
Journal Article