Modeling Nash Equilibria in Artificial Intelligence Development

In his discussion of a theoretical artificial intelligence “arms race”, Nick Bostrom, Director of the Future of Humanity Institute at Oxford, presents a model of future AI research in which development teams compete to create the first General AI. Under the assumption that the first AI will be very powerful and transformative (a notably arguable one, as per the soft vs. hard takeoff debate), each team is highly incentivised to finish first. Bostrom argues that the level of safety precautions each development team will undertake arises as a reflection of broader policy parameters, specifically those relating to the allowed level of market concentration (i.e. permitted consolidation of research teams), and information accessibility (i.e. degrees of intellectual property protection & algorithm secrecy).

In his work, Bostrom does not reach one specific conclusion regarding AI safety levels, but instead defines a set of Nash equilibria given various numbers of development teams + levels of information accessibility. Specifically, he notes that having additional development teams (and therefore reduced market concentration) may increase the likelihood of an AI disaster, especially if risk-taking is more important than skill in developing the AI. Increased information accessibility also increases risk. The more teams know of each others’ capabilities and methodologies, the greater the velocity of, and enmity in, development; a greater equilibrium danger level follows accordingly.

Bostrom’s derivation is intended to spur discussions on AI governance design. See his original paper here!
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