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As problems become more complex and interconnected, collective intelligence must evolve to keep pace, likely requiring AI systems as indispensable partners. 7 min read.
- Generative AI can aid human exploration of unintuitive solution spaces, but must be designed to facilitate cooperative social problem solving.
- Enhanced individual intelligence through AI does not guarantee collective wisdom—in fact, it may hinder it through polarization and loyalty to individual perspectives.
- AI could help groups synthesize diverse views and map dynamics, acting as a neutral facilitator and simulator, but care must be taken not to stifle human agency.
- Privacy is a concern as individual control diminishes—group benefits may impose individual costs. Concentrating power in opaque AI systems challenges realizing true collective intelligence.
- Balance is critical between structure and self-organization, memory and exploration, digital acceleration and tangible action. Diversity must be embraced without losing the ability to synthesize.
- As AI approaches human reasoning, "rooted intelligence" like nuanced understanding of contexts, cultures, and relationships becomes irreplaceably human. Only humans are accountable.
- Social solutions require designing systems that foster meaningful connections and human accountability. AI's limitations in applying knowledge must be considered.
- Trust and empathy become vital in discovering diverse perspectives. The goal is enabling groups to both challenge and belong through a mind for our minds.
Problems are growing more complex, demanding greater collective intelligence. The internet has interconnected us, fostering networks of influence and amplification. We grapple with machines and AI systems, seeking ways to harness their power for enhanced cognition and productivity.
In Big Mind, Geoff Mulgan argues that a comprehensive theory of collective intelligence—one which includes machines—must tackle the multidimensionality of our choices. As choices encompass increasingly diverse perspectives, varying influence, potential conflicts, and timing considerations, our approach to problem-solving must evolve in lock-step with the agency of machines and the complexity of global problems. In fact, it’s likely that machines are indispensable to solving our biggest problems, so where do we put them to work first?