AI safety improves through modular, bite-sized thinking

The early 2020s saw rapid development of increasingly powerful AI models, prompting – so to speak – renewed focus on system safety principles from other industries. Researchers are exploring how concepts from Charles Perrow’s work on complex systems could help create safer AI architectures through modularity and controlled assembly.

Key safety principles: Complex, tightly-coupled systems are more prone to unexpected accidents and cascading failures, making a modular approach potentially valuable for AI development.

• Just In Time Assembly (JITA), a manufacturing concept where components are assembled only when needed, could be adapted to construct AI capabilities selectively
• Frequent resetting of model weights and operating contexts helps prevent unintended behaviors from emerging
• Modular designs allow for easier testing, validation, and replacement of individual components

Technical implementation strategies: Microsoft and other organizations are developing new architectural approaches that break AI systems into smaller, more manageable pieces.

• The Model Disassembling and Assembling (MDA) technique allows AI systems to be broken down and recombined as needed
• Expert models handle specialized tasks while routing systems direct queries to appropriate components
• System coupling can be reduced by introducing deliberate checkpoints and time delays between operations

Benefits of modular design: Breaking AI systems into discrete components offers several advantages for safety and control.

• Base models can be designed with limited general capabilities, with additional functions added through plug-in modules
• Individual components can be thoroughly tested and validated before integration
• Problematic modules can be isolated and replaced without disrupting the entire system
• System behavior becomes more predictable and manageable within defined contexts

Current challenges: The modular approach to AI safety faces several technical hurdles that researchers are working to overcome.

• Performance may be degraded compared to monolithic systems
• Maintaining effective communication between components requires careful design
• Finding the right balance between modularity and capability remains difficult
• Integration with other alignment techniques needs further development

Looking ahead: While modularity alone cannot solve all AI safety challenges, it represents a promising approach that could help control advanced AI systems while preserving their utility. The success of these techniques in other complex industries suggests they could become an important part of the AI safety toolkit, though significant research and development work remains to be done.