large-scale hybrid (humans and AIs) society and economy
AI lab, not to be confused with an “AI org” above: an AI lab is an org composed of humans and increasingly of AIs that creates advanced AI systems. See Hendrycks et al.′ discussion of organisational risks.
I’ve earlier suggested a principled taxonomy of AI safety work with two dimensions:
System level:
monolithic AI system
human—AI pair
AI group/org: CoEm, debate systems
large-scale hybrid (humans and AIs) society and economy
AI lab, not to be confused with an “AI org” above: an AI lab is an org composed of humans and increasingly of AIs that creates advanced AI systems. See Hendrycks et al.′ discussion of organisational risks.
Methodological time:
design time: basic research, math, science of agency (cognition, DL, games, cooperation, organisations), algorithms
manufacturing/training time: RLHF, curriculums, mech interp, ontology/representations engineering, evals, training-time probes and anomaly detection
deployment/operations time: architecture to prevent LLM misuse or jailbreaking, monitoring, weights security
evolutionary time: economic and societal incentives, effects of AI on society and psychology, governance.
So, this taxonomy is a 5x4 matrix, almost all slots or which are interesting, and some of them are severely under-explored.