wynalazki: Generative AI

Evidence: 100 • authority: 72

Generative artificial intelligence (GenAI) is a subfield of artificial intelligence (AI) that uses generative models to generate text, images, videos, audio, computer code or other forms of digital data. These models learn the underlying patterns and structures of their training data, and use them to generate new data in response to input, which often takes the form of natural language prompts. The prevalence of generative AI tools has increased significantly since the AI boom of the early 2020s. This boom was made possible by improvements in deep neural networks, particularly large language models (LLMs), which are based on the transformer architecture. Generative AI applications include c…

Evidence: 64 • authority: 92

Assessing the feasibility of collective licensing of in-copyright works as training data for generative AI systems.. Copyright owners have sued several developers of large-scale generative AI systems for copyright infringement because of their uses of massive quantities of in-copyright works as training data for building AI models. Fair use will be the main defense against these charges. If fair use defenses succeed, developers will be free to continue to commercially exploit models already built on copyrighted data as well as to use these data to train new models or fine-tune existing ones. If copyright owners prevail, developers may be liable for billions of dollars of damages. Developers…

Evidence: 64 • authority: 72

In the 2020s, the rapid advancement of deep learning-based generative artificial intelligence models raised questions about the copyright status of AI-generated works, and about whether copyright infringement occurs when such are trained or used. This includes text-to-image models such as Stable Diffusion and large language models such as GPT. Large language models are trained on all the data which can be scraped from the Internet, often utilizing copyrighted material. As of 2023, there were several pending U.S. lawsuits challenging the use of copyrighted data to train AI models, with defendants arguing that this falls under fair use.

Evidence: 64 • authority: 88

Copyright Protection Against Use of copyrighted Works Without Permission in AI Machine Learning: Focused on Introducing Blockchain-Based Extended Collective Licensing System. Regarding the issue of using copyrighted works as training data, there are two conflicting claims. One is that free use should be legalized for the development of the AI industry, and the other is that copyrighted works should not be used without permission due to reducing incentives for creation. Generative AI provides literary and artistic outputs much faster and cheaper based on learning from human works, putting human authors at risk of being replaced. In this situation, it is time to consider whether it is reasona…

Evidence: 54 • authority: 72

The New York Times Co. v. Microsoft Corp. et al. is a copyright infringement lawsuit filed by The New York Times (NYT) against Microsoft and OpenAI in December 2023. The lawsuit alleges that OpenAI trained its artificial intelligence (AI) models on NYT content without authorization and that OpenAI and Microsoft’s products can reproduce portions of copyrighted articles, which the Times states competed with its journalism. According to the Times, it is the first major news organization to sue OpenAI and Microsoft for alleged copyright infringements. OpenAI stated that it was surprised by the lawsuit and argued that its use of copyrighted materials to train AI models constitutes fair use under…

Evidence: 54 • authority: 80

May Artificial Intelligence use the existing copyrighted works without permission of the copyrighted owners?

Evidence: 45 • authority: 92

Limitations of current copyright frameworks for large language models trained on scientific literature.. Recent copyright lawsuits against artificial intelligence (AI) and large language model (LLM) developers have ignited debates over how to balance technological innovation with the public interest. In the scientific research field, the performance and reliability of LLMs trained on scientific literature (SciLit-LLMs) depend heavily on access to comprehensive, up-to-date full-text sources. This paper argues that the current copyright framework, including the U.S. fair use doctrine, often regarded as a flexible solution for AI-related copyright issues, is ill-suited for SciLit-LLMs. First,…

Evidence: 45 • authority: 80

Memorization of Copyrighted Works by AI Models Under EU Law

Evidence: 45 • authority: 80

Exploring Remuneration Mechanisms for Copyrighted Works in Generative AI Systems

Evidence: 45 • authority: 88

Exploring remuneration mechanisms for copyrighted works in generative AI systems. Abstract Generative artificial intelligence (AI) reconfigures the foundations of copyright by introducing multi‐actor creation and platform‐mediated dissemination. Within a dual‐dimensional framework combining the expressive function of originality and the disseminative function of platform power, four scenarios are developed to calibrate incentive structures and institutional efficiency. Comparative analysis of US, EU, and China's judicial practices reveals divergent yet complementary approaches: US courts emphasize market substitution and fair use's fourth factor; the EU advances restriction‐based remunerati…