The training process of foundation models as for other classes of deep learning systems is based on minimizing the reconstruction error over a training set. For this reason, they are susceptible to the memorization and subsequent reproduction of training samples. In this paper, we introduce a training-as-compressing perspective, wherein the model's weights embody a compressed representation of the training data. From a copyright standpoint, this point of view implies that the weights could be considered a reproduction or a derivative work of a potentially protected set of works. We investigate the technical and legal challenges that emerge from this framing of the copyright of outputs generated by foundation models, including their implications for practitioners and researchers. We demonstrate that adopting an information-centric approach to the problem presents a promising pathway for tackling these emerging complex legal issues.

The rapidity with which generative AI has been adopted and advanced has raised legal and ethical questions related to the impact on artists rights, content production, data collection, privacy, accuracy of information, and intellectual property rights. Recent administrative and case law challenges have shown that generative AI software systems do not have independent intellectual property rights in the content that they generate. It remains to be seen whether human content creators can retain their intellectual property rights against generative AI software, its developers, operators, and owners for the misappropriation of the work of human creatives, given the metes and bounds of existing law. Early signs from various courts are mixed as to whether and to what degree the results generated by AI models meet the legal standards of infringement under existing law.

Recent advances in artificial intelligence (AI) have raised questions about whether the use of AI is appropriate and legal in various professional contexts. Here, we present a perspective on how scholars may approach writing in conjunction with AI, and offer approaches to evaluating whether or not such AI-writing violates copyright or falls within the safe harbor of fair use. We present a set of best practices for standard of care with regard to plagiarism, copyright, and fair use. As AI is likely to grow more capable in the coming years, it is appropriate to begin integrating AI into scholarly writing activities. We offer a framework for establishing sound legal and scholarly foundations.

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).

Last year, a Tennessee-based artist named Kelly McKernan noticed that their name was being used with increasing frequency in A.I.-driven image generation. McKernan makes paintings that often feature nymphlike female figures in an acid-colored style that blends Art Nouveau and science fiction. A list published in August, by a Web site called Metaverse Post, suggested "Kelly McKernan" as a term to feed an A.I. generator in order to create "Lord of the Rings"-style art. Hundreds of other artists were similarly listed according to what their works evoked: anime, modernism, "Star Wars." On the Discord chat that runs an A.I. generator called Midjourney, McKernan discovered that users had included their name more than twelve thousand times in public prompts.

The artists taking action -- Sarah Anderson, Kelly McKernan, Karla Ortiz -- "seek to end this blatant and enormous infringement of their rights before their professions are eliminated by a computer program powered entirely by their hard work," according to the official text of the complaint filed to the court. Using tools like Stability AI's Stable Diffusion, Midjourney, or the DreamUp generator on DeviantArt, people can type phrases to create artwork similar to living artists. Since the mainstream emergence of AI image synthesis in the last year, AI-generated artwork has been highly controversial among artists, sparking protests and culture wars on social media. Enlarge/ A selection of images generated by Stable Diffusion. Knowledge of how to render them came from scraped images on the web.

Who owns these fabulous works of art generated by systems and models like OpenAI's DALL-E or Stability.ai's What about blog content created by tools like GoCharlie or Copy.ai? To engage Ruth's services as an attorney, visit their website at GeekLawFirm.com. This interview does not constitute legal advice or create a client-attorney relationship with anyone. The information contained in this interview is presented on an "as is" basis with no guarantee of completeness, accuracy, usefulness, timeliness, or of the results obtained from the use of this information and without warranty of any kind, express or implied, including, but not limited to warranties of performance, merchantability, or fitness for a particular purpose. While we have taken every reasonable precaution to insure that the content is accurate, errors can occur. In all cases you should consult with a qualified professional familiar with your particular situation for advice concerning specific matters. What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video. Please note the following warning disclosure and disclaimer, this interview does not constitute legal advice or create a client attorney relationship with anyone.

Just days after GitHub announced its new Copilot tool, which generates complementary code for programmers' projects, web developer Kyle Peacock tweeted an oddity he had noticed. "I love to learn new things and build things," the algorithm wrote, when asked to generate an About Me page. While the About Me page was supposedly generated for a fake person, that link goes to the GitHub profile of David Celis, who The Verge can confirm is not a figment of Copilot's imagination. Celis is a coder and GitHub user with popular repositories, and even formerly worked at the company. "I'm not surprised that my public repositories are a part of the training data for Copilot," Celis told The Verge, adding that he was amused by the algorithm reciting his name.

In May 2015, The Simpsons voice actor Harry Shearer – who plays a number of key characters including, quite incredibly, both Mr Burns and Waylon Smithers – announced that he was leaving the show. By then, the animated series had been running for more than 25 years, and the pay of its vocal cast had risen from $30,000 an episode in 1998 to $400,000 an episode from 2008 onwards. But Fox, the producer of The Simpsons, was looking to cut costs – and was threatening to cancel the series unless the voice actors took a 30 per cent pay cut. Most of them agreed, but Shearer (who had been critical of the show's declining quality) refused to sign – after more than two decades, he wanted to break out of the golden handcuffs, and win back the freedom and the time to pursue his own work. Showrunner Al Jean said Shearer's iconic characters – who also include Principal Skinner, Ned Flanders and Otto Mann – would be recast.

A second burst of interest in AI authorship broke out in the mid-1980s. Congress once again commissioned a study, this time from its Office of Technology Assessment (OTA), to address this and other controversial computer-related issues. OTA did not offer an answer to the question, perhaps in part because at that time, it was a "toy problem" because no commercially significant outputs of AI or other software programs had yet been generated.5 But deep learning and other AI breakthroughs have caused IP professionals to rethink the AI authorship issue.1,2 For example, The Next Rembrandt video features a group of art experts and computer scientists discussing how they collaborated to digitize many Rembrandt paintings, develop models of particular features of the paintings, and then create a Rembrandt-like portrait of a man with facial hair wearing a hat and looking to the right.6 The resulting AI-generated painting really does look like a Rembrandt.