Value Alignment
The challenge of ensuring AI systems pursue goals and exhibit behaviors that are consistent with human values, preferences, and ethical principles — considered the central problem of AI safety.
Value alignment encompasses multiple sub-problems: specifying what values the AI should follow, training it to internalize those values, and verifying that it actually behaves according to them in novel situations. Current approaches include RLHF, constitutional AI, and debate (where two AI models argue and a human judges). The challenge intensifies as AI systems become more capable — misaligned AI with limited capabilities causes limited harm, but misaligned superintelligent AI could be catastrophic. Research funding for alignment remains under $300 million annually — less than 1% of total AI R&D spending.
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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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