March 27, 2024, 4:46 a.m. | Uri Hacohen, Adi Haviv, Shahar Sarfaty, Bruria Friedman, Niva Elkin-Koren, Roi Livni, Amit H Bermano

cs.CV updates on arXiv.org arxiv.org

arXiv:2403.17691v1 Announce Type: new
Abstract: The advent of Generative Artificial Intelligence (GenAI) models, including GitHub Copilot, OpenAI GPT, and Stable Diffusion, has revolutionized content creation, enabling non-professionals to produce high-quality content across various domains. This transformative technology has led to a surge of synthetic content and sparked legal disputes over copyright infringement. To address these challenges, this paper introduces a novel approach that leverages the learning capacity of GenAI models for copyright legal analysis, demonstrated with GPT2 and Stable Diffusion …

abstract artificial artificial intelligence arxiv biases copilot copyright cs.cl cs.cv data data-driven diffusion domains enabling genai generative generative artificial intelligence github gpt intelligence openai openai gpt professionals quality stable diffusion synthetic technology type

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

AIML - Sr Machine Learning Engineer, Data and ML Innovation

@ Apple | Seattle, WA, United States

Senior Data Engineer

@ Palta | Palta Cyprus, Palta Warsaw, Palta remote