Thinking Inside the Box: Controlling and Using an Oracle AI

Abstract

There is no strong reason to believe that human-level intelligence represents an upper limit of the capacity of artificial intelligence, should it be realized. This poses serious safety issues, since a superintelligent system would have great power to direct the future according to its possibly flawed motivation system. Solving this issue in general has proven to be considerably harder than expected. This paper looks at one particular approach, Oracle AI. An Oracle AI is an AI that does not act in the world except by answering questions. Even this narrow approach presents considerable challenges. In this paper, we analyse and critique various methods of controlling the AI. In general an Oracle AI might be safer than unrestricted AI, but still remains potentially dangerous.

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Notes

  1. 1.

    It is generally argued that intelligence and motivation are orthogonal, that a high intelligence is no guarantor of safe motives (Bostrom 2012b).

  2. 2.

    Humans have many preferences—survival, autonomy, hedonistic pleasure, overcoming challenges, satisfactory interactions, and countless others—and we want them all satisfied, to some extent. But a randomly chosen motivation would completely disregard half of these preferences (actually it would disregard much more, as these preferences are highly complex to define—we wouldn’t want any of the possible ‘partial survival’ motivations, for instance).

  3. 3.

    Friendliness should not be interpreted here as social or emotional friendliness, but simply a shorthand for whatever behavioural or motivational constraints that keeps a superintelligent system from deliberately or accidentally harming humans.

  4. 4.

    Another common term is “AI-in-a-box”.

  5. 5.

    A term coined by Fanya Montalvo by (Mallery 1988) analogy to the mathematical concept of NP-completeness: a problem is AI-complete if an AI capable of solving it would reasonably also be able to solve all major outstanding problems in AI.

  6. 6.

    Or whatever counterpart might apply to an AI.

  7. 7.

    Though there have been some attempts to formalise ontology changes, such as (de Blanc 2011).

  8. 8.

    Overfitting in this way is a common worry in supervised learning methods.

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Acknowledgments

We would like to thank and acknowledge the help from Owen Cotton-Barratt, Will Crouch, Katja Grace, Robin Hanson, Lisa Makros, Moshe Looks, Eric Mandelbaum, Toby Ord, Jake Nebel, Owain Evans, Carl Shulman, Anna Salamon, and Eliezer Yudkowsky.

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Correspondence to Stuart Armstrong.

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Armstrong, S., Sandberg, A. & Bostrom, N. Thinking Inside the Box: Controlling and Using an Oracle AI. Minds & Machines 22, 299–324 (2012). https://doi.org/10.1007/s11023-012-9282-2

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Keywords

  • Artificial intelligence
  • Superintelligence
  • Security
  • Risks
  • Motivational control
  • Capability control