Current legal frameworks consider AI-generated works eligible for copyright protection when they meet originality requirements and involve substantial human intellectual input. However, systematic legal standards and reliable evaluation methods for AI art copyrights are lacking. Through comprehensive analysis of legal precedents, we establish three essential criteria for determining distinctive artistic style: stylistic consistency, creative uniqueness, and expressive accuracy. To address these challenges, we introduce ArtBulb, an interpretable and quantifiable framework for AI art copyright judgment that combines a novel style description-based multimodal clustering method with multimodal large language models (MLLMs). We also present AICD, the first benchmark dataset for AI art copyright annotated by artists and legal experts. Experimental results demonstrate that ArtBulb outperforms existing models in both quantitative and qualitative evaluations. Our work aims to bridge the gap between the legal and technological communities and bring greater attention to the societal issue of AI art copyrights.

So, if an AI makes something, who do we attribute it to? The person who initiated the prompts? Or the sources the AI used? Works are original when they are independently created by a human author and have a minimal degree of creativity. In this incident, British nature photographer David Slater set himself up between 2008 and 2011 to befriend a wild Celebes crested macaques troop.

AI tools allow users to generate images, audio, and textual works in response to textual prompts. These tools "learn" how to generate this content by ingesting massive sets of preexisting, human-authored works. How and to what extent the use of AI impacts the ability to secure intellectual property (IP) rights are evolving questions in IP law. Recently, in Thaler v. Vidal2, the U.S. Federal Circuit Court analyzed AI inventorship in view of the U.S. Patent Act – ultimately concluding that the Patent Act unambiguously "requires that inventors must be natural persons; that is, human beings." In Thaler, the AI technology known as "DABUS" used general background knowledge of a technical field to conceive and recognize the utility of inventions without specific guidance from a human being.

Generative AI can seem like magic. Image generators such as Stable Diffusion, Midjourney, or DALL·E 2 can produce remarkable visuals in styles from aged photographs and water colors to pencil drawings and Pointillism. The resulting products can be fascinating -- both quality and speed of creation are elevated compared to average human performance. The Museum of Modern Art in New York hosted an AI-generated installation generated from the museum's own collection, and the Mauritshuis in The Hague hung an AI variant of Vermeer's Girl with a Pearl Earring while the original was away on loan. The capabilities of text generators are perhaps even more striking, as they write essays, poems, and summaries, and are proving adept mimics of style and form (though they can take creative license with facts).

The recent surge in interest in new AI applications in 2023 has been nothing short of extraordinary. From ChatGPT to a growing list of other new apps, our technology and business worlds are rapidly evolving before our eyes in many exciting ways. As a curious technologist, I am fascinated by these new trends, and I wrote this primer on the topic back in January: "ChatGPT: Hopes, Dreams, Cheating and Cybersecurity." I have received many questions about the use of ChatGPT to generate content, and this YouTube video addressed the question: "Is It Plagiarism to Use ChatGPT in Your Published Works?" But as an author, blogger and creator of original content, I have other concerns that are growing just as fast as the new technology is being deployed.

We are slowly seeing the emerging trend of organisations considering the use of generative technologies across all areas of business. Collectively known as'generative AI', these technologies (such as the popular Chat-GPT and Dall-E) are capable of taking a prompt from its user and creating entirely new content, such as blog posts, letters to clients, or internal policies. In a previous article, we examined several points that organisations should consider, such as potential for IP infringement and inadvertent PR issues. The article goes on to consider several steps organisations can take to mitigate these risks throughout the process, such as regular testing and ensuring appropriate safeguards are put in place. As will be clear for those who have already interacted with these technologies, while there is certainly value in implementing them within certain processes, these safeguards are clearly a necessary step to ensure that the AI is behaving accurately and, in the case of written works, in a way that is not misleading.

Github Copilot, an AI tool that automatically suggests blocks of code to add as programmers type, has recently come under the scanner for the violation of open-source licenses. Earlier this month, a programmer and lawyer, Matthew Butterick, along with a team of lawyers, filed a class-action lawsuit in the US against Github Copilot, its parent company Microsoft, and AI-technology partner OpenAI, claiming that the tool profits "from the work of open-source programmers by violating the conditions of their open-source licenses." The people behind the lawsuit alleged that Copilot does not provide attribution when it reproduces code, violating the licenses governing open-source code, noted an article in Wired. Joseph Saveri, founder of the law firm behind the suit, called it the "first major step in the battle against intellectual-property violations in the tech industry arising from artificial-intelligence systems." The New York Times noted that the lawsuit may well be the first "legal attack" on the way AI is trained.

Want to have an impressionist painting of Thai temples in the style of Claude Monet, but you cannot afford to commission an artist? Let artificial intelligence (AI) do the work for you. Then you change your mind and want to have the painting in a surrealistic style. Type what you want in the message field of the AI art-generating program. You get what you wanted.

Artificial Intelligence (AI) is the simulation and augmentation of human intelligence demonstrated by machines. It has the potential to revolutionise the world and is set to become the most impactful human innovation in history. The first alternative is for the AI to be granted ownership. The next step is to explore the realm of attributing ownership to the developer of AI. This category shall include individuals who contributed to the creation and development of AI-generated works.

A major step forward in using Artificial Intelligence (AI) for scientific discovery in the field of stem cell research was recently reported1, reflecting the continued growth of the technology and stressing the need for clarification on patents for AI-generated work. Kanda et al. created a humanoid robotic AI system that can plan and execute experiments to develop optimized protocols for differentiation of stem cells into desired therapeutically relevant cell types. In particular, the system tested cell culture conditions for differentiation of induced pluripotent stem cells (iPSCs) into retinal pigment epithelial cells (iPSC-RPE cells) based on a pre-optimized protocol, evaluated the results of particular cell culture conditions by image analysis of pigment producing cells, and planned the next experiments based on the results to develop an improved protocol for producing iPSC-RPE cells. The AI system tested 143 different conditions from 200 million possible parameter combinations in 111 days to achieve 88% better iPSC-RPE generation compared to the preexisting protocol. Accordingly, Kanda et al. concluded that the robotic AI system could drastically accelerate "systematic and unbiased exploration of experimental search space, suggesting immense use in medicine and research."