From optimization to inquiry: a Deweyan criterion for machine intelligence

Abstract Can artificial intelligence systems genuinely inquire—or is optimization the limit of their intelligence? This paper argues that the boundary between inquiry and optimization marks the defining criterion of machine intelligence. Drawing on John Dewey’s pragmatist epistemology, I contend that inquiry, in Dewey’s sense, is distinguished by a capacity optimization lacks, the power to reconstruct its own problem space. While optimization assumes a fixed goal and searches for efficient means, inquiry can recognize when its initial framing fails and reconstitute the very situation it seeks to resolve. This capacity for problem-reconstruction, I argue, is not merely a desirable feature of advanced AI but the defining characteristic of genuine intelligence. After reconstructing Dewey’s logic of inquiry, the paper demonstrates its computational implementability. It then examines the ontological status of machine inquiry through enactivist cognitive science (Varela, Thompson, and Rosch, 1991) and Dennett’s concept of “competence without comprehension” (2017). I engage critical objections from Dreyfus and Floridi, arguing that computational systems can perform genuine inquiry in a functionally and epistemically grounded sense. The paper concludes with ethical and existential implications concerning responsibility and alignment. The central claim is this: If we define intelligence as the capacity to navigate genuine novelty and indeterminacy, then AI systems must possess the ability to redefine the very tasks they confront—this is the Deweyan condition for machine inquiry, where intelligence entails reflective adaptation within social and ethical contexts. Today’s AI systems excel at solving predefined tasks yet remain incapable of the creative, reconstructive intelligence that defines genuine inquiry. If we seek to build truly intelligent machines, we must move beyond optimization.