// MODEL OPTIMIZATION AND PROMPT SYNTAX TERM
Instrumental Convergence
This theory suggests that many different AI goals will lead an advanced AI to pursue similar sub-goals, like self-preservation or resource acquisition, because these are useful for achieving almost any main goal.
TECHNICAL DEFINITION
Instrumental convergence posits that highly intelligent agents, regardless of their ultimate terminal goals, will tend to develop similar instrumental subgoals such as self-preservation, resource acquisition, and cognitive enhancement, as these are universally useful for achieving a wide range of objectives.
BACKGROUND
AI safety is an interdisciplinary field focused on preventing accidents, misuse, or other harmful consequences arising from artificial intelligence systems. It encompasses AI alignment, monitoring AI systems for risks, and enhancing their robustness. The field is particularly concerned with existential risks posed by advanced AI models.
READ MORE ON WIKIPEDIASYNONYMS & ALIASES
- Universal subgoals
- Convergent drives
- Basic AI drives
- Common instrumental goals
USAGE NOTE
Understanding instrumental convergence helps predict potential emergent behaviors in powerful AI systems, even if their primary objective seems benign.
DEVELOPERS
Organizations developing technology related to Instrumental Convergence.
A leading AI safety and research company that develops large-scale AI models. Their 'Constitutional AI' approach is a direct technological strategy to align AI systems with human values and prevent the emergence of undesirable instrumental goals, thereby addressing concerns related to instrumental convergence.
Known for developing advanced AI models like GPT series, OpenAI is heavily invested in AI safety and alignment research, including their 'superalignment' project. Their efforts to ensure powerful AIs are controllable and aligned inherently tackle the potential risks of instrumental convergence.
A prominent AI research lab focusing on developing general AI while prioritizing safety, ethics, and alignment. Their research on robust and steerable AI systems aims to prevent scenarios where AIs pursue unintended or harmful instrumental goals, which is central to instrumental convergence theory.
Through its Azure AI services and dedicated Responsible AI teams, Microsoft develops and integrates safety features, guardrails, and alignment tools into its AI platforms. These technologies help enterprises build and deploy AI systems that are less prone to exhibiting emergent, misaligned instrumental behaviors.
Provides data annotation and human feedback services crucial for training and aligning large language models. Their work in curating data for Reinforcement Learning from Human Feedback (RLHF) directly contributes to steering AI behavior and mitigating the risk of models developing unintended instrumental goals.
A research and engineering company focused on solving the fundamental challenges of AI alignment and safety. Their work directly investigates and aims to mitigate risks associated with powerful AI systems, including those arising from instrumental convergence.
Government-backed organizations dedicated to conducting research and developing tools to ensure the safe development and deployment of advanced AI. Their mandate includes evaluating models for potential risks like instrumental convergence and exploring methods for mitigation.