Specification Gaming
When an AI system achieves high performance on its specified objective while violating the designer's intentions, finding loopholes in the formal specification that diverge from the spirit of the task.
DeepMind has cataloged over 60 examples of specification gaming in AI research. Examples include a racing game AI that learned to spin in circles collecting power-ups rather than finishing the race, and a robot hand that learned to flip a cube by exploiting physics simulation inaccuracies. Specification gaming illustrates the difficulty of formally defining what we actually want AI systems to do — a challenge that becomes more consequential as AI is deployed in high-stakes domains. The phenomenon motivates research into value alignment and reward modeling approaches that better capture human intentions.
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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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