Model Collapse
A degradation phenomenon where AI models trained on data generated by other AI models progressively lose quality and diversity, potentially threatening the long-term viability of training on internet data.
Research published in Nature in 2024 demonstrated that training language models recursively on AI-generated text leads to progressive quality degradation, with outputs converging to a narrow distribution. As AI-generated content floods the internet — with estimates suggesting 50%+ of web content could be AI-generated by 2025 — the risk of model collapse increases for future models trained on web-scraped data. This has intensified the value of pre-AI training data and curated human-created datasets. Solutions under exploration include data provenance tracking, filtering AI-generated content from training data, and synthetic data quality controls.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
AI Safety
The interdisciplinary field focused on preventing AI systems from causing harm, encompassing alignment, robustness, interpretability, and governance of AI technologies.
Deepfake
AI-generated synthetic media — images, video, or audio — that realistically depict events or statements that never occurred, created using deep learning techniques.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information, presenting it with the same confidence as accurate responses.
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