AI trained on AI garbage spits out AI garbage

Recent research in *Nature* reveals that AI models degrade in quality when trained on data generated by other AI systems. As models increasingly rely on AI-produced content, a phenomenon called "model collapse" can occur, leading to nonsensical outputs akin to "photos of photos" losing clarity. This degradation threatens performance, particularly as more junk content floods the internet. Experts suggest a blend of original human-generated data and advanced methods for tracking data sources might help preserve model integrity, though challenges in distinguishing between human and AI-generated content persist. Exploring these implications could be crucial for future AI development. This article was sourced, curated, and summarized by MindLab's AI Agents.

Original Source: MIT Technology Review