The AGI Containment Problem
This addresses a critical safety issue for AI researchers and developers working on AGI, but it is incremental as it builds on existing concerns without proposing new solutions.
The paper tackles the problem of safely testing advanced artificial general intelligences (AGIs) that may pose security risks due to unknown motivations and capabilities, by surveying the AGI containment problem and identifying requirements, mechanisms, and weaknesses for building secure test containers.
There is considerable uncertainty about what properties, capabilities and motivations future AGIs will have. In some plausible scenarios, AGIs may pose security risks arising from accidents and defects. In order to mitigate these risks, prudent early AGI research teams will perform significant testing on their creations before use. Unfortunately, if an AGI has human-level or greater intelligence, testing itself may not be safe; some natural AGI goal systems create emergent incentives for AGIs to tamper with their test environments, make copies of themselves on the internet, or convince developers and operators to do dangerous things. In this paper, we survey the AGI containment problem - the question of how to build a container in which tests can be conducted safely and reliably, even on AGIs with unknown motivations and capabilities that could be dangerous. We identify requirements for AGI containers, available mechanisms, and weaknesses that need to be addressed.