Instrumental Convergence

Instrumental convergence or convergent instrumental values is the theorized tendency for most sufficiently intelligent agents to pursue potentially unbounded instrumental goals such as self-preservation and resource acquisition [1]. This concept has also been discussed under the term basic drives. The idea was first explored by Steve Omohundro. He argued that sufficiently advanced AI systems would all naturally discover similar instrumental subgoals. The view that there are important basic AI drives was subsequently defended by Nick Bostrom as the instrumental convergence thesis, or the convergent instrumental goals thesis. On this view, a few goals are instrumental to almost all possible final goals. Therefore, all advanced AIs will pursue these instrumental goals. Omohundro uses microeconomic theory by von Neumann to support this idea. Omohundro’s Drives Omohundro presents two sets of values, one for self-improving artificial intelligences [2] and another he says will emerge in any sufficiently advanced AGI system [3]. The former set is composed of four main drives: * Self-preservation: A sufficiently advanced AI will probably be the best entity to achieve its goals. Therefore it must continue existing in order to maximize goal fulfillment. Similarly, if its goal system were modified, then it would likely begin pursuing different ends. Since this is not desirable to the current AI, it will act to preserve the content of its goal system. * Efficiency: At any time, the AI will have finite resources of time, space, matter, energy and computational power. Using these more efficiently will increase its utility. This will lead the AI to do things like implement more efficient algorithms, physical embodiments, and particular mechanisms. It will also lead the AI to replace desired physical events with computational simulations as much as possible, to expend fewer resources. * Acquisition: Resources like matter and energy are indispensable for action. The more resou