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Church's views on AI seem far away from my and most people's views in the AI risk community, and really intrigued me. It would be great to try distil and summarise these views to update on it properly.

Model of the threat and interventions for mesa-optimization

  • Consider a chain model
    • Base optimizer
    • -> Mesa optimizer
      • Produced through optimization of base objective in training environment
    • -> Misalignment (base objective != mesa objective)
      • Different kinds of misalignment
        • Proxy: mesa objective is a proxy for the base objective in the training environment
          • Side-effect: optimizing mesa objective happens to optimize base objective
          • Instrumental: optimizing base objective happens to optimize mesa objective
        • Approximate: objectives differ due to an approximation error, caused by limits of representation in mesa-optimizer's model
        • Suboptimal: optimizing mesa objective happens to optimize base objective due to a flaw
      • What makes this particularly concerning relative to the "standard" alignment problem? It's that the misalignment may be particularly difficult to detect (see next step). The mesa-optimizer has been "screened" by the base optimizer during training; it has to align to the base objective to some extent, at least in the training environment. This either means it is aligned, or deceptively misaligned. The base probability of a misaligned mesa-optimizer is lower than a misaligned system in general, but conditioned on it happening, it could carry greater risk.
    • -> Deployment
      • Not enough evidence of risk based on training and testing in controlled environment
      • Mesa optimizer is deceptive, to increase its chance of deployment
    • -> Distribution shift
      • Deployment environment differs from training environment or controlled testing environment
    • -> Unanticipated behaviour
      • The distribution shift causes the misalignment to manifest in different behaviour
      • There is a "standard" problem of robustness to distribution shift that applies to any machine learning model, not just mesa-optimizers. What makes a mesa-optimizer particularly concerning? It's that optimization will tend to make the unanticipated behaviour more adversarial and influence-seeking. It can freely pursue an objective rather than merely perform a specified task.
    • -> Catastrophe
      • The unanticipated behaviour is so bad that it leads to a permanent, huge loss of value.
      • Ways this doesn't happen:
        • The behaviour is counter-productive: perhaps self-destructive, not directed or consistent enough to lead to anything bad (depends on how sensitive the domain is to random error)
        • Intervention from other actors (depends on decisive strategic advantage)
  • Interventions
    • Base -> Mesa: prevent mesa-optimizer arising
      • Alternative methods of subsystem generation beyond search
    • Mesa -> Misalignment: prevent mesa-optimizer from being misaligned
      • Standard alignment methods
      • Specific methods for base optimizer to align mesa optimizer
    • Misalignment -> Deployment: don't deploy misaligned mesa-optimizer
      • Transparency
      • Interpretability
    • Deployment -> Distribution shift: ensure deployment environment does not differ significantly from training environment or controlled testing environment
      • The converse is probably more tractable, in which case intervention comes before Base -> Mesa
    • Distribution shift -> Unanticipated behaviour: prevent unanticipated behaviour by being aware of this fact, followed by deferring or failing gracefully
      • Out-of-distribution detection
      • Well-calibrated uncertainty estimation
      • Corrigibility
    • Unanticipated behaviour -> Catastrophe: prevent catastrophe
      • Internal
        • Impact measurement
      • External
        • Shut down
        • Stop or steer the behaviour in various ways, depending on the nature of the behaviour