Internet is eating itself. What's next? Model collapse and AI data crisis (sderosiaux.substack.com)

Recent discussions highlight a growing concern in the AI and machine learning community regarding model collapse and an impending data crisis, signaling a critical juncture for the future of AI development. The internet's vast amount of data, once a boon for training models, is now facing challenges of diminishing returns as datasets become increasingly repetitive and less representative. As AI systems rely heavily on this data to learn and improve, encountering stale or biased datasets could lead to less effective models that fail to innovate or genuinely understand the complexities of the real world. This situation poses significant implications for AI and ML, particularly as researchers and developers grapple with the need for high-quality, diverse data. The risk of model collapse—the phenomenon where models, despite having access to vast information, produce monotonous or suboptimal outputs—threatens the integrity of AI applications across industries. By addressing these challenges through innovative data sourcing, curation methods, and improved training techniques, the community could steer AI development back on track, ensuring its evolution remains robust and progressive.