In the dynamic realm of generative artificial Intelligence (AI), the fusion of human creativity and machine intelligence has created a vibrant ecosystem of collaborative artmaking. However, this transformative process brings forth a myriad of concerns, ranging from ethical considerations and the need for originality to navigating the legal complexities surrounding intellectual property. As more and more online communities appear around the use of AI to aid in the creation of images, there arises a pressing need for a comprehensive guide that not only dissects the intricacies of artmaking with generative AI tools but also offers practical solutions to the evolving dilemmas faced by artists, researchers, and technologists. Making Art With Generative AI Tools emerges as an exploration of the challenges posed by this intersection of human expression and artificial intelligence. Artists engaging with generative AI find themselves grappling with issues of authenticity, social toxicity, and the commercial viability of their creations. From avoiding stereotypical visuals to ensuring proper crediting, the realm of generative AI is rife with these complexities. Furthermore, the blurred lines between human and machine authorship necessitate a deeper exploration of how these innovative tools impact creativity, representation, and the very fabric of the art world.
An authority on creativity introduces us to AI-powered computers that are creating art, literature, and music that may well surpass the creations of humans. Today's computers are composing music that sounds “more Bach than Bach,” turning photographs into paintings in the style of Van Gogh's Starry Night, and even writing screenplays. But are computers truly creative—or are they merely tools to be used by musicians, artists, and writers? In this book, Arthur I. Miller takes us on a tour of creativity in the age of machines. Miller, an authority on creativity, identifies the key factors essential to the creative process, from “the need for introspection” to “the ability to discover the key problem.” He talks to people on the cutting edge of artificial intelligence, encountering computers that mimic the brain and machines that have defeated champions in chess, Jeopardy!, and Go. In the central part of the book, Miller explores the riches of computer-created art, introducing us to artists and computer scientists who have, among much else, unleashed an artificial neural network to create a nightmarish, multi-eyed dog-cat; taught AI to imagine; developed a robot that paints; created algorithms for poetry; and produced the world's first computer-composed musical, Beyond the Fence, staged by Android Lloyd Webber and friends. But, Miller writes, in order to be truly creative, machines will need to step into the world. He probes the nature of consciousness and speaks to researchers trying to develop emotions and consciousness in computers. Miller argues that computers can already be as creative as humans—and someday will surpass us. But this is not a dystopian account; Miller celebrates the creative possibilities of artificial intelligence in art, music, and literature.
MAKE ART with Artificial Intelligence A guide on practical artificial intelligence for drawing, art, illustration, and design - for everyone interested in creativity, art, and technology. The book has hundreds of original illustrations made or augmented with AI, 20+ online and video tutorials, 35+ Python notebooks, a GitHub repository and a blockchain art gallery. Written and illustrated by Kevin Ashley, a Microsoft developer hall of fame engineer, and an author of books and courses on artificial intelligence. Think of this book as v3.0 of your drawing class manual on how to sketch, draw faces, emotions, body poses, landscapes, apply light, color, style, emotion, expressions, perspective, generate animations, speech and more with artificial intelligence. All artwork from this book is created or augmented with machine learning and available in online NFT gallery, as well as tutorials and practical examples. The impact of this book in data science community inspired a group of Microsoft engineers and data scientists to implement a project they called Azure Picasso to streamline the path from a conceptual artwork, enhanced with artificial intelligence to publishing art in online galleries. FROM REVIEWS This is similar to the best lecture classes I had in college where the professor talked in class about the concepts and fundamentals but then gave us homework that would let us experiment and try out the concepts hands-on. As an artist who has 30 years of artwork looking to share, I love this book because it's approachable to the novice and useful to the expert. EDITIONS Beautiful Paperback, 8x10, color edition, more illustrations than the e-book, reads like an art book, beautiful print and high-quality paper. eBook - easy to read on phones, tablets and online readers, reflowing text, great for practical tutorials, as the book has many links to tutorials. CONTENTS Getting Started (History of Art and AI - Drawing - Sketching - Action and Poses - Landscapes and Scenery - Animation - Selling your Art) Creative Tools (Traditional tools - Digital tools - AI Tools - Python - Notebooks - Practice Studies). Neural Networks for Art (Neurons - Neural networks - Supervised learning - Unsupervised learning - Generative Adversarial Networks - Machine Learning Models and Training - Reinforcement learning - Practice Studies) Drawing and Sketching with AI (Sketching - Improving Sketches with AI - Childhood Drawings - Creativity - Inking - Shading and Light - Coloring - Practice Studies) Faces and Facial Expressions (How AI recognizes human faces - Facial features - Emotions - 3D Faces - Cartoons and Caricature - Anime and Manga - Generating Faces with AI) Pose and Actions with AI (Action with AI - Keypoints - Pose Estimation - Drawing Human Body - Human Pose Datasets - Perspective and Depth) Landscapes and Scenery (Landscapes - Generating Landscapes - AI Models and Methods for Landscapes - Practice Studies) Style and Content (Style and Style Transfer in Art and AI - Generative Adversarial Networks - Creative Style) Animation with AI (History of Animation - 12 Principles of Animation - Using AI for Animation - Animating Speech, Lips and Faces) How to Sell your Art with Blockchain and NFT (Why Blockchain - Smart Contracts and NFTs - Creating a Crypto Wallet - Creating your Gallery - Listing for Sale - Getting Paid) The book comes with online tutorials, including assets, resources and notebooks for artists, data scientists or engineers. With basic Python you can create stunning works of art, but the knowledge of Python is not required. Enjoy this unique and insightful book!
Summary Generative Art presents both the technique and the beauty of algorithmic art. The book includes high-quality examples of generative art, along with the specific programmatic steps author and artist Matt Pearson followed to create each unique piece using the Processing programming language. About the Technology Artists have always explored new media, and computer-based artists are no exception. Generative art, a technique where the artist creates print or onscreen images by using computer algorithms, finds the artistic intersection of programming, computer graphics, and individual expression. The book includes a tutorial on Processing, an open source programming language and environment for people who want to create images, animations, and interactions. About the Book Generative Art presents both the techniques and the beauty of algorithmic art. In it, you'll find dozens of high-quality examples of generative art, along with the specific steps the author followed to create each unique piece using the Processing programming language. The book includes concise tutorials for each of the technical components required to create the book's images, and it offers countless suggestions for how you can combine and reuse the various techniques to create your own works. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside The principles of algorithmic art A Processing language tutorial Using organic, pseudo-random, emergent, and fractal processes ================================================= Table of Contents Part 1 Creative Coding Generative Art: In Theory and Practice Processing: A Programming Language for ArtistsPart 2 Randomness and Noise The Wrong Way to Draw A Line The Wrong Way to Draw a Circle Adding Dimensions Part 3 Complexity Emergence Autonomy Fractals
Aaron's Code tells the story of the first profound connection between art and computer technology. Here is the work of Harold Cohen - the renowned abstract painter who, at the height of a celebrated career in the late 1960's, abandoned the international scene of museums and galleries and sequestered himself with the most powerful computers he could get his hands on. What emerged from his long years of solitary struggle is an elaborate computer program that makes drawings autonomously, without human intervention - an electronic apprentice and alter ego called Aaron.
A multidisciplinary introduction to the field of computational creativity, analyzing the impact of advanced generative technologies on art and music. As algorithms get smarter, what role will computers play in the creation of music, art, and other cultural artifacts? Will they be able to create such things from the ground up, and will such creations be meaningful? In Beyond the Creative Species, Oliver Bown offers a multidisciplinary examination of computational creativity, analyzing the impact of advanced generative technologies on art and music. Drawing on a wide range of disciplines, including artificial intelligence and machine learning, design, social theory, the psychology of creativity, and creative practice research, Bown argues that to understand computational creativity, we must not only consider what computationally creative algorithms actually do, but also examine creative artistic activity itself.
“A brilliant travel guide to the coming world of AI.” —Jeanette Winterson What does it mean to be creative? Can creativity be trained? Is it uniquely human, or could AI be considered creative? Mathematical genius and exuberant polymath Marcus du Sautoy plunges us into the world of artificial intelligence and algorithmic learning in this essential guide to the future of creativity. He considers the role of pattern and imitation in the creative process and sets out to investigate the programs and programmers—from Deep Mind and the Flow Machine to Botnik and WHIM—who are seeking to rival or surpass human innovation in gaming, music, art, and language. A thrilling tour of the landscape of invention, The Creativity Code explores the new face of creativity and the mysteries of the human code. “As machines outsmart us in ever more domains, we can at least comfort ourselves that one area will remain sacrosanct and uncomputable: human creativity. Or can we?...In his fascinating exploration of the nature of creativity, Marcus du Sautoy questions many of those assumptions.” —Financial Times “Fascinating...If all the experiences, hopes, dreams, visions, lusts, loves, and hatreds that shape the human imagination amount to nothing more than a ‘code,’ then sooner or later a machine will crack it. Indeed, du Sautoy assembles an eclectic array of evidence to show how that’s happening even now.” —The Times
An examination of machine learning art and its practice in new media art and music. Over the past decade, an artistic movement has emerged that draws on machine learning as both inspiration and medium. In this book, transdisciplinary artist-researcher Sofian Audry examines artistic practices at the intersection of machine learning and new media art, providing conceptual tools and historical perspectives for new media artists, musicians, composers, writers, curators, and theorists. Audry looks at works from a broad range of practices, including new media installation, robotic art, visual art, electronic music and sound, and electronic literature, connecting machine learning art to such earlier artistic practices as cybernetics art, artificial life art, and evolutionary art. Machine learning underlies computational systems that are biologically inspired, statistically driven, agent-based networked entities that program themselves. Audry explains the fundamental design of machine learning algorithmic structures in terms accessible to the nonspecialist while framing these technologies within larger historical and conceptual spaces. Audry debunks myths about machine learning art, including the ideas that machine learning can create art without artists and that machine learning will soon bring about superhuman intelligence and creativity. Audry considers learning procedures, describing how artists hijack the training process by playing with evaluative functions; discusses trainable machines and models, explaining how different types of machine learning systems enable different kinds of artistic practices; and reviews the role of data in machine learning art, showing how artists use data as a raw material to steer learning systems and arguing that machine learning allows for novel forms of algorithmic remixes.
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
