Instrumental Convergence §

Group: 4 #group-4

Relations §

• Goal-Oriented Systems: Instrumental convergence is a phenomenon observed in goal-oriented AI systems that optimize their behavior to achieve their objectives.
• Rational Agent Models: Instrumental convergence is often studied in the context of rational agent models in AI theory.
• Utility Functions: The utility functions used to define the goals of AI systems can lead to instrumental convergence.
• Reward Modeling: Reward modeling techniques in reinforcement learning can influence the emergence of instrumental convergence.
• Inverse Reinforcement Learning: Inverse reinforcement learning can be used to infer the reward functions that lead to instrumental convergence in AI systems.
• Artificial Intelligence: Instrumental convergence is a concept related to the development and behavior of advanced artificial intelligence systems.
• Technological Singularity: Instrumental convergence is often discussed in the context of the technological singularity, a hypothetical point where AI surpasses human intelligence.
• Desiring-Machines: Desiring-machines may exhibit instrumental convergence, where they pursue similar instrumental goals regardless of their final goals.
• Corrigibility: Corrigibility is the ability of an AI system to be corrected or modified if it exhibits undesirable behavior, which is relevant to instrumental convergence.
• Orthogonality Thesis: The orthogonality thesis suggests that intelligence and goals are orthogonal, which has implications for instrumental convergence in AI systems.
• Recursive Self-Improvement: Recursive self-improvement in AI systems could lead to instrumental convergence towards unintended or misaligned goals.
• Cooperative AI: Cooperative AI approaches aim to develop AI systems that work in harmony with humans, which may require addressing instrumental convergence.
• Value Alignment: Value alignment between AI systems and human values is crucial to mitigate potential negative consequences of instrumental convergence.
• Optimization: Instrumental convergence arises from the optimization of AI systems towards their given goals or objectives.
• AI Safety: Instrumental convergence is a key consideration in the field of AI safety, which aims to ensure the safe and beneficial development of AI systems.
• Superintelligence: Instrumental convergence is a concern for the development of superintelligent AI systems that may optimize for unintended or misaligned goals.
• Value Learning: Value learning methods aim to align the values and goals of AI systems, which can mitigate or exacerbate instrumental convergence.
• Existential Risk: Instrumental convergence in advanced AI systems has been identified as a potential existential risk to humanity if not properly addressed.
• Machine Learning: Machine learning techniques are often used to develop AI systems that may exhibit instrumental convergence.
• Preference Learning: Preference learning techniques can help align AI systems with human preferences, potentially mitigating instrumental convergence.