| Sumario: | This essay argues that the experience of perplexity, as discussed in this special issue, is negated or foreclosed by the statistical measures used in artificial intelligence—which includes, interestingly enough, a specific measure of uncertainty termed perplexity. This argument proceeds in two parts. The first reviews recent discussions of generative AI to argue that current versions of AI are distinct from the symbolic logic that guided earlier forms of AI and instead create an environment for human experience that cannot itself be grasped by human sensation. The inequivalence between human sensation and the technical foundations of this "world" results in the many hyperbolic and hysteric discussions of the potentials and problems of artificial intelligence today. The second part details the technical specifics of how statistical logic is implemented in well-known generative AI systems. It follows how generative AI derives from the measures of probability first described in the information theory of Claude Shannon and discusses specific datasets used to train and manage the "perplexity" of specific AI models, including ChatGPT and DALL-E. This essay concludes with a brief discussion of how the technical operations of generative AI relate to a distinction between words, images, and numbers, and how this distinction—or conflation—demonstrates that, in some way, human beings are aware that a world or environment shaped and conditioned by generative AI is, effectively, at odds with and exists beyond human comprehension.
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