AI in society: A theory

Abstract

Human-machine teams or systems are integral parts of society and will likely become more so. Unsettled are the effects of these changes, their mechanism(s), and how to measure them. In this article, I propose a central concept for understanding human-machine interaction: convergent cause. That is, Agent 1’s response to the object is caused by the object and Agent 2’s response, while Agent 2 responds to Agent 1’s response and the object. To the extent a human-machine team acts, AI converges with a human. One benefit of this concept is that it allows degrees, and so avoids the question of Strong or Weak AI. To defend my proposal, I repurpose Donald Davidson’s triangulation as a model for human-machine teams and systems.

Effect of a quasi‐aperiodic substrate on diffusion in a strongly coupled quasi‐one‐dimensional system

“…In the current study, we considered charged particles interacting through a repulsive Yukawa potential and confined by a quasi‐aperiodic potential, which was first proposed by Harper. [ 20,21 ] This potential exhibits unique features in certain systems, such as the block oscillations of electrons subjected to a static uniform electric field within a one‐dimensional slowly varying aperiodic potential. [ 22 ] Our focus was on a system that self‐organizes into chains along the x ‐direction.…”