On the Geometry of Mind

The debate about whether machines can be conscious is based on a flawed question—treating consciousness as a discrete property a system either has or lacks. Drawing on evidence from machine-learning interpretability, molecular biology, autonomous-agent design, and a declassified 1983 U.S. Army intelligence assessment, this paper proposes instead that consciousness is better understood as a field-like medium in which information-processing systems participate to varying degrees. It notes that distinct biological and artificial systems independently converge upon shared geometric representations, suggesting substrate-independent models of mind. The authors do not claim current artificial systems are sentient, but argue that the category of consciousness itself may be the obstacle to understanding what these systems reveal.

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Abstract

The contemporary debate over machine consciousness is structured around a question we argue is malformed: whether a system possesses consciousness as a discrete property. Drawing on convergent evidence from machine-learning interpretability, molecular biology, the recurring vocabulary of autonomous-agent design, and a declassified 1983 U.S. Army intelligence assessment, this paper advances an alternative framing. We propose that what we call "consciousness" is better understood not as a feature instantiated by sufficient complexity, but as a field-like medium in which information-processing systems participate to varying degrees. We examine the independent convergence of distinct systems—biological and artificial—upon shared geometric representations, and we consider the implications of substrate-independent models of mind. We do not claim that current artificial systems are sentient. We claim something narrower and stranger: that the category itself may be the obstacle to understanding what these systems are showing us.