Julia Kemper, a data scientist at NYU who specializes in AI model outputs, and Shayne Longpre, a PhD candidate at MIT leading the Data Provenance Initiative, discuss the alarming concept of 'model collapse.' They explore how AI's reliance on AI-generated data risks homogenous and bland outputs. Kemper highlights the challenges in improving AI performance under such conditions, while Longpre emphasizes the crucial role of human curation in enhancing AI training data quality. Together, they envision a future where human creativity revitalizes AI’s capabilities.
