As AI systems become increasingly powerful, the need for safe AI has become more pressing. Humans are an attractive model for AI safety: as the only known agents capable of general intelligence, they perform robustly even under conditions that deviate significantly from prior experiences, explore the world safely, understand pragmatics, and can cooperate to meet their intrinsic goals. Intelligence, when coupled with cooperation and safety mechanisms, can drive sustained progress and well-being. These properties are a function of the architecture of the brain and the learning algorithms it implements. Neuroscience may thus hold important keys to technical AI safety that are currently underexplored and underutilized. In this roadmap, we highlight and critically evaluate several paths toward AI safety inspired by neuroscience: emulating the brain's representations, information processing, and architecture; building robust sensory and motor systems from imitating brain data and bodies; fine-tuning AI systems on brain data; advancing interpretability using neuroscience methods; and scaling up cognitively-inspired architectures. We make several concrete recommendations for how neuroscience can positively impact AI safety.

本研究解决了随着人工智能系统日益强大而对安全性需求日益迫切的问题。通过借鉴神经科学的原则，本文提出了一种新颖的方法，强调模仿大脑的表征、信息处理和结构，以提升AI系统的安全性。研究发现，神经科学为技术AI安全提供了重要的未开发资源，对推动AI的可靠性和可解释性具有重大影响。

神经人工智能与人工智能安全