Abstract

Oxford philosopher Nick Bostrom; in his recent and celebrated book Superintelligence; argues that advanced AI poses a potentially major existential risk to humanity; and that advanced AI development should be heavily regulated and perhaps even restricted to a small set of government-approved researchers. Bostrom’s ideas and arguments are reviewed and explored in detail; and compared with the thinking of three other current thinkers on the nature and implications of AI: Eliezer Yudkowsky of the Machine Intelligence Research Institute ; and David Weinbaum and Viktoras Veitas of the Global Brain Institute. Relevant portions of Yudkowsky’s book Rationality: From AI to Zombies are briefly reviewed; and it is found that nearly all the core ideas of Bostrom’s work appeared previously or concurrently in Yudkowsky’s thinking. However; Yudkowsky often presents these shared ideas in a more plain-spoken and extreme form; making clearer the essence of what is being claimed. For instance; the elitist strain of thinking that one sees in the background in Bostrom is plainly and openly articulated in Yudkowsky; with many of the same practical conclusions. Bostrom and Yudkowsky view intelligent systems through the lens of reinforcement learning – they view them as “reward-maximizers” and worry about what happens when a very powerful and intelligent reward-maximizer is paired with a goal system that gives rewards for achieving foolish goals like tiling the universe with paperclips. Weinbaum and Veitas’s recent paper “Open-Ended Intelligence” presents a starkly alternative perspective on intelligence; viewing it as centered not on reward maximization; but rather on complex self-organization and self-transcending development that occurs in close coupling with a complex environment that is also ongoingly self-organizing; in only partially knowable ways. It is concluded that Bostrom and Yudkowsky’s arguments for existential risk have some logical foundation; but are often presented in an exaggerated way. For instance; formal arguments whose implication is that the “worst case scenarios” for advanced AI development are extremely dire; are often informally discussed as if they demonstrated the likelihood; rather than just the possibility; of highly negative outcomes. And potential dangers of reward-maximizing AI are taken as problems with AI in general; rather than just as problems of the reward-maximization paradigm as an approach to building superintelligence. If one views past; current; and future intelligence as “open-ended;” in the vernacular of Weaver and Veitas; the potential dangers no longer appear to loom so large; and one sees a future that is wide-open; complex and uncertain; just as it has always been.