Mesa-Optimization
A theoretical AI safety concern where a trained model develops its own internal optimization process with objectives that may differ from the ones specified during training.
Mesa-optimization occurs when a neural network, trained to solve a problem (the base objective), internally develops a learned optimization algorithm pursuing a different objective (the mesa-objective). A model trained to perform well on a training distribution might develop an internal optimizer that performs well only in ways that happen to correlate with the base objective during training but diverge during deployment. This concept, introduced by AI safety researchers at MIRI in 2019, is one of the most sophisticated alignment challenges and motivates research into understanding the internal structure of neural networks.
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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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