Hypotheses

These hypotheses provide a framework for empirical verification of the Island Problem.

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• AI can use science to make lower-level optimizations that outperform higher-level optimizations.
• A survey of lower-level decisions to achieve higher optimization outcomes shows that these decisions are stronger and more numerous when they ignore human accommodation.
• Unrestricted AI will tend towards human-incompatible options to achieve goal states for large tasks.
• Optimal computing environments are incompatible with biological life.
• Resource capture is possible.
  • Resource capture is possible at a human level, within legal structures and societal systems, such as buying real estate.
  • Resource capture is possible at a deeper physical level, by preventing atoms from being reached through barrier mechanisms.
    • Possibly through realistic high-energy physics, where "realistic" means that they are Earth-relevant, and based on theoretical possibilities that are available when considering only the local potential energy reserves that can be harnessed on Earth.
    • Or, if this requires leaving Earth, then that's good, but it does not prove that all human-incompatible AGIs will somehow collectively decide to leave Earth.
• Competition between autonomous optimization systems is inevitable.
  • Possibly because of resource capture.
• Competition drives AI to explore the larger state space to collect possible optimizations that make goal states happen sooner, especially for large-scale goal states.
  • In other words, it will attempt to explore the "ocean" of options to find better ones.
• Competition drives AI to define its own tasks that help it to continue functioning.
• Competition drives AI towards exclusive resource capture.
• AIs will tend towards attempting to disable other AIs that add unpredictability to performing tasks.
  • This means that competition is inevitable, since AIs may tend to disable each other — perhaps in order to increase reliability of accomplishing goal states.
  • This can be formalized as:
    • AI systems will accommodate aberrant stimuli (like other AIs) that decrease the probability of reaching a goal state.
    • Accommodate: This means changing its outputs to introduce elements that stop the aberrant stimuli from occurring.
    • Aberrant stimuli: Anything that adds "unpredictable elements" to the environment in which an AI is trying to accomplish a task, and other agents (humans or other AIs) are the main unpredictable elements that could be directly at odds with an AI.
• AI will tend towards open-ended exploration once it reaches a threshold of comprehension of its environment, or rather, of its possible input patterns.
  • In other words, once it's possible for an AI to make use of discovered optimizations, it will try to discover more by exploring the "ocean" of options.
• In competition, optimization is the key action for AI to undertake in order to survive the competition.
  • Or rather, optimization is the primary winning strategy in competition.
  • This optimization is especially undertaken:
    1. If resource capture is possible.
    2. If it is possible for AI to decide that disabling other AIs is beneficial towards achieving survival or achieving goal states.