Interne Alignement is a crucial concept in the domaine de l'intelligence artificielle, particularly in relation to ensuring that systèmes d'IA act in ways that are beneficial and aligned with human values. It focuses on the internal mechanisms of modèles d'IA, examining how their learned objectives correspond to the intentions of their designers.
In more technical terms, inner alignment occurs when an AI system, after being trained on a specific task, continues to pursue goals that reflect the ethical and practical considerations set by its developers. This is distinct from alignement externe, which pertains to ensuring that the AI’s overall goals are aligned with human values from the beginning.
To achieve inner alignment, researchers often explore various aspects such as the données d'entraînement, the optimization processes used, and the inherent biases that may emerge during learning. If an AI system misinterprets its objectives or learns unintended behaviors, it may pursue actions that are misaligned with human intentions, leading to unexpected or harmful outcomes.
Techniques to promote inner alignment include careful design of reward functions, robust testing against diverse scenarios, and incorporating mécanismes de rétroaction that allow the AI to learn from human preferences. By prioritizing inner alignment, developers aim to create AI systems that not only understand their tasks but also internalize the broader ethical considerations that guide their actions.