Description
Critical examination of vulnerabilities, copyright jurisprudence, adversarial manipulation, and systemic failure modes in contemporary machine learning.
Key Topics
- Data Poisoning & Artist Protection: How tools like Glaze and Nightshade inject adversarial perturbations into artwork to disrupt AI scraping.
- Model Collapse (Nature Study): The recursive degradation of generative models trained on synthetic AI data (the autophagous loop).
- Copyright & Fair Use: Current judicial precedents on copyrightability of AI outputs and training set fair-use challenges.