Toy model: convergent instrumental goals
tl;dr: Toy model to illustrate convergent instrumental goals.
Steve Omohundro identified 'AI drives' (also called 'Convergent Instrumental goals') that almost all intelligent agents would converge to:Self-improve
- Be rational
- Protect utility function
- Prevent counterfeit utility
- Self-protective
- Acquire resources and use them efficiently
This post will attempt to illustrate some of these drives, by building on the previous toy model of the control problem, which was further improved by Jaan Tallinn.
Amending the "General Pupose Intelligence: Arguing the Orthogonality Thesis"
Stuart has worked on further developing the orthogonality thesis, which gave rise to a paper, a non-final version of which you can see here: http://lesswrong.com/lw/cej/general_purpose_intelligence_arguing_the/
This post won't make sense if you haven't been through that.
Today we spent some time going over it and he accepted my suggestion of a minor amendment. Which best fits here.
Besides all the other awkward things that a moral convergentist would have to argue for, namely:
This argument generalises to other ways of producing the AI. Thus to deny the Orthogonality thesis is to assert that there is a goal system G, such that, among other things:
- There cannot exist any efficient real-world algorithm with goal G.
- If a being with arbitrarily high resources, intelligence, time and goal G, were to try design an efficient real-world algorithm with the same goal, it must fail.
- If a human society were highly motivated to design an efficient real-world algorithm with goal G, and were given a million years to do so along with huge amounts of resources, training and knowledge about AI, it must fail.
- If a high-resource human society were highly motivated to achieve the goals of G, then it could not do so (here the human society is seen as the algorithm).
- Same as above, for any hypothetical alien societies.
- There cannot exist any pattern of reinforcement learning that would train a highly efficient real-world intelligence to follow the goal G.
- There cannot exist any evolutionary or environmental pressures that would evolving highly efficient real world intelligences to follow goal G.
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