NeuroAI for AI Safety

2024-11-27

arXiv.org

AI Summary

AI safety urgently requires biologically inspired paradigms to address critical deficiencies in robustness, safe exploration, pragmatic understanding, and collaborative intelligence. This paper establishes, for the first time, a systematic interdisciplinary framework bridging neuroscience and AI safety, proposing five actionable pathways: (1) neuro-inspired architecture design, (2) embodied learning–driven safe exploration, (3) brain-data–informed fine-tuning using fMRI/EEG, (4) neural representation alignment, and (5) cognitively grounded, interpretability-enhanced scaling. Integrating computational neural modeling, neural signal decoding, embodied reinforcement learning, and cognitive architecture modeling, the framework delivers a technically feasible roadmap grounded in theoretical rigor. Its core contributions are: (i) establishing a novel theoretical foundation for AI safety under biological constraints; (ii) providing empirically testable, cross-disciplinary experimental protocols; and (iii) opening a principled pathway toward trustworthy artificial general intelligence. (149 words)

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Safety and Robustness
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search

Abstract

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.

Problem

• How neuroscience can inspire AI safety mechanisms
• Emulating brain representations for robust AI systems
• Using neuroscience methods to enhance AI interpretability

Innovation

• Emulate brain's representations and architecture
• Build robust systems from brain data
• Advance interpretability via neuroscience methods