The advent of generative AI images has completely disrupted the art world. Distinguishing AI generated images from human art is a challenging problem whose impact is growing over time. A failure to address this problem allows bad actors to defraud individuals paying a premium for human art and companies whose stated policies forbid AI imagery. It is also critical for content owners to establish copyright, and for model trainers interested in curating training data in order to avoid potential model collapse. There are several different approaches to distinguishing human art from AI images, including classifiers trained by supervised learning, research tools targeting diffusion models, and identification by professional artists using their knowledge of artistic techniques. In this paper, we seek to understand how well these approaches can perform against today's modern generative models in both benign and adversarial settings. We curate real human art across 7 styles, generate matching images from 5 generative models, and apply 8 detectors (5 automated detectors and 3 different human groups including 180 crowdworkers, 4000+ professional artists, and 13 expert artists experienced at detecting AI). Both Hive and expert artists do very well, but make mistakes in different ways (Hive is weaker against adversarial perturbations while Expert artists produce higher false positives). We believe these weaknesses will remain as models continue to evolve, and use our data to demonstrate why a combined team of human and automated detectors provides the best combination of accuracy and robustness.

Late last month, after a year-plus wait, OpenAI quietly released the latest version of its image-generating AI program, DALL-E 3. The announcement was filled with stunning demos--including a minute-long video demonstrating how the technology could, given only a few chat prompts, create and merchandise a character for a children's story. But perhaps the widest-reaching and most consequential update came in two sentences slipped in at the end: "DALL-E 3 is designed to decline requests that ask for an image in the style of a living artist. Creators can now also opt their images out from training of our future image generation models." The language is a tacit response to hundreds of pages of litigation and countless articles accusing tech firms of stealing artists' work to train their AI software, and provides a window into the next stage of the battle between creators and AI companies. The second sentence, in particular, cuts to the core of debates over whether tech giants like OpenAI, Google, and Meta should be allowed to use human-made work to train AI models without the creator's permission--models that, artists say, are stealing their ideas and work opportunities.

In recent years Generative Machine Learning systems have advanced significantly. A current wave of generative systems use text prompts to create complex imagery, video, even 3D datasets. The creators of these systems claim a revolution in bringing creativity and art to anyone who can type a prompt. In this position paper, we question the basis for these claims, dividing our analysis into three areas: the limitations of linguistic descriptions, implications of the dataset, and lastly, matters of materiality and embodiment. We conclude with an analysis of the creative possibilities enabled by prompt-based systems, asking if they can be considered a new artistic medium.

In brief Everyone agrees that text-to-image models are here to stay, though opinions are divided over AI-generated art.… Some artists are enthralled by the ability to create completely new digital images using text prompts and see it as a new tool to be creative. Other artists, however, detest the technology and believe it will take away their jobs and devalue their work. A machine can be trained to recreate a particular artist's style and outpace human artists, as RJ Palmer, a concept artist, told the BBC. "Right now, if an artist wants to copy my style, they might spend a week trying to replicate it. That's one person spending a week to create one thing. With this machine, you can produce hundreds of them a week/" AI is "directly stealing their essence in a way", and artists are currently powerless to stop it from happening.

In brief Everyone agrees that text-to-image models are here to stay, though opinions are divided over AI-generated art. Some artists are enthralled by the ability to create completely new digital images using text prompts and see it as a new tool to be creative. Other people who make their living from art, however, detest the technology – believing it will cost them their jobs and devalue their work. A machine can be trained to recreate a particular artist's style and outpace human artists, as RJ Palmer, a conceptual artist, told the BBC. "Right now, if an artist wants to copy my style, they might spend a week trying to replicate it. That's one person spending a week to create one thing. With this machine, you can produce hundreds of them a week."

I'm trying to explain to Arthur I. Miller why artworks generated by computers don't quite do it for me. The works aren't a portal into another person's mind, where you can wander in a warren of intention, emotion, and perception, feeling life being shaped into form. What's more, it often seems, people just ain't no good, so it's transcendent to be reminded they can be. Art is one of the few human creations that can do that. No matter how engaging the songs or poems that a computer generates may be, they ultimately feel empty. They lack the electricity of the human body, the hum of human consciousness, the connection with another person. Miller, a longtime professor, a gentleman intellect, dressed in casual black, is listening patiently, letting me have my say.

Prior to becoming a full-time musician, Grimes learned how to use the production software Logic for her neuroscience studies at Montreal's McGill University. The Vancouver native brought her unique perspective to Sean Carroll's Mindscape podcast, where she spoke about artificial intelligence's growing capacity to create music. "I feel like we're in the end of art, human art." said Grimes, who is now going by the name c in reference to the speed of light. "Once there's actual AGI (Artificial General Intelligence), it's gonna be so much better at making art than us… Once AI can totally master science and art, which could happen in the next 10 years, probably more like 20 or 30 years." She also predicted that AI will reach a point when it will be building and creating art for itself.

The following is an excerpt of You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It's Making the World a Weirder Place by Janelle Shane. Listen to a radio interview with Janelle Shane about the mistakes artificial intelligence can make. Will the music, movies, and novels of the future be written by AI? Maybe at least partially. AI-generated art can be striking, weird, and unsettling: infinitely morphing tulips; glitchy humans with half-melted faces; skies full of hallucinated dogs. AT. rex may turn into flowers or fruit; the Mona Lisa may take on a goofy grin; a piano riff may turn into an electric guitar solo.

You are about to hear a speech supporting the idea that Gambling should be banned…" The 332-word speech arguing that gambling should be banned offered three reasons (with evidence) to support its case: "Gambling is addictive," "facilitates criminal activity," and "has ruined many individuals and families." The second speech--arguing that that gambling should not be banned--also provided three reasons. Regular readers of my column know that "the rule of three" is a fundamental component of persuasion. Overloading a listener with too much information at any one time makes it difficult for humans to process the content. Project Debater already knows it. Project Debater marks a major milestone toward understanding language. The AI system can complement human decision-making by bringing in facts and evidence in a persuasive, logical structure. By understanding people's opinions on different topics, politicians, public servants and business leaders can get a better understanding of what people think about a policy or corporate decision--and why they think the way they do.

"The most significant arousal-raising properties for aesthetics are novelty, surprisingness, complexity, ambiguity, and puzzlingness," say Elgammal and co. "Novelty refers to the degree a stimulus differs from what an observer has seen/experienced before. "Too little arousal potential is considered boring, and too much activates the aversion system, which results in negative response," say Elgammal and co. That has important implications for the way their generative adversarial network, or agent, is set up. "The agent's goal is to generate art with increased levels of arousal potential in a constrained way without activating the aversion system," they say. Some of the machine-generated images were produced by the creative adversarial network, but others were produced by the generative adversarial network that simply reproduces artistic styles it has learned.