§34 R&D automation is compatible with both strong and weak centralization
Reframing Superintelligence: Comprehensive AI Services as General Intelligence — K. Eric Drexler, FHI Technical Report #2019-1 (Markdown edition). Section 34 of 40. Source label: rd-centralization. Rendered PDF: https://ora.ox.ac.uk/objects/uuid:9c05427a-6390-4b42-9c55-ee45f73a26ad
Thesis. Advances in AI R&D automation are currently distributed across many independent research groups, but a range of pressures could potentially lead to strong centralization of capabilities.
Cites sections: §1 R&D automation provides the most direct path to an intelligence explosion; §10 R&D automation dissociates recursive improvement from AI agency
Cited by sections: §II
Summary
AI R&D is currently distributed across many independent research groups, and the architecture of R&D automation is compatible with continued decentralized development. Various pressures tend to favor greater centralization of development in leading organizations, yet centralization per se would neither force nor strongly favor a qualitative change in the architecture of R&D tasks. Alternative distributions of capabilities across organizations could provide affordances relevant to AI policy and strategy.
The R&D automation model is compatible with decentralized development
State-of-the-art AI research and development is currently decentralized, distributed across independent groups that operate within a range of primarily corporate and academic institutions. Continued automation of AI R&D tasks will likely increase the advantages provided by proprietary tool-sets, integrated systems, and large-scale corporate resources, yet strong automation is compatible with continued decentralization.
Accelerating progress could lead to strong centralization of capabilities
Fundamental arguments suggest that AI R&D automation provides the most direct path to steeply accelerating, AI-enabled progress in AI technologies. Steeply accelerating progress, if driven by proprietary, rapidly-advancing tool sets, could favor the emergence of wide gaps between competing groups, effectively centralizing strong capabilities in a leader.
Centralization does not imply a qualitative change in R&D tasks
Pressures that favor centralization neither force nor strongly favor a qualitative change in the architecture of R&D tasks or their automation. Organizational centralization, tool-chain integration, and task architecture are distinct considerations, and only loosely coupled.
Centralization and decentralization provide differing affordances relevant to AI policy and strategy
Considerations involving AI policy and strategy may favor centralization of strong capabilities (e.g., to provide affordances for centralized control), or might favor the division of complementary capabilities across organizations (e.g., to provide affordances for establishing cross-institutional transparency and interdependence). Unlike classic models of advanced AI capabilities as something embodied in a distinct entity (“the machine”), the R&D automation model is compatible with both alternatives.
Further Reading
- §1 R&D automation provides the most direct path to an intelligence explosion