If AI companies avoid AI slop, shouldn't we? (www.machinesociety.ai)

AI companies are increasingly relying on pre-AI content, such as books, for training their models, paradoxically highlighting a preference for human-generated material while promoting AI-generated outputs. This shift stems from concerns about "model collapse," a scenario where AI trained predominantly on AI-generated data suffers from a decline in accuracy and creativity, resulting in homogenized outputs. Companies like ISBNdb are stepping in, providing access to their vast repository of over 111 million books, specifically those published before 2022, which are less likely to contain AI-derived content and thus retain diverse ideas that AI models need to function effectively. The phenomenon raises critical questions about the implications of using AI-influenced human content for further training. As writers increasingly leverage AI tools for creativity, the risk of "model collapse" persists, leading to a convergence of ideas and a diminishing range of perspectives. Studies suggest even when human input is involved, the interplay between AI-generated ideas and human creativity can still lead to homogenization and a decrease in originality. This ongoing cycle underscores the need for careful consideration in the selection of training data to ensure AI systems can maintain their effectiveness and creativity without succumbing to repetitive patterns.