Generative models are a class of artificial intelligence algorithms capable of producing new data that resembles the data on which they were trained. These models learn underlying patterns and distributions from existing datasets to create novel outputs, such as text, images, or code. Their application spans content creation, data augmentation, and the simulation of complex systems.
Context
The emergence of advanced generative models is a focal point in discussions regarding their impact on digital asset creation, content generation for marketing, and the potential for synthetic data generation in training other AI systems. Debates often touch upon issues of intellectual property, authenticity verification, and the ethical considerations surrounding AI-generated content within digital economies.
This research pioneers decentralized, verifiable multiparty computation for generative AI, safeguarding user privacy and model integrity against centralized control.
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