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"Wallace, Rodrick"

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Wallace, R. (2025). On Embodied Machine Consciousness. In R. Wallace (Ed.), Mathematical Essays on Embodied Cognition: Insights from Information and Control Theories (pp. 191–219). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-83709-8_12

Consciousness in higher animals, typically characterized by a 100 millisecond time constant, is, by necessity, a greatly simplified and stripped-down version of more complex multiple tunable workspace cognition/regulation dyads like wound healing, immune function, gene expression, institutional function and the like. These more complex dynamic entities emerged through evolutionary exaptation of the inevitable information crosstalk between coresident cognitive modules. In consequence of the severely debrided nature of consciousness, it should not be difficult to construct a fast, single workspace ‘conscious machine’ that mimics the human tunable neuronal global workspace system. Like innate and acquired immune cognition, such a construction could be tied to a ‘backbrain’ AI that has learned hyperrapid stereotypic pattern responses to some particular set of likely challenges. The result would be an ‘emotional’ conscious machine. A particularly clever designer, however, may want to use available high-speed electronics to mimic—or even extend—the more capable multiple-workspace/workforce systems inherently less susceptible to inattentional blindness and related failings of overfocus and thrashing when interacting with an embedding environment that imposes it’s own grammar and syntax. Contrary to current social construction, the actual utility of a ‘conscious machine’ remains obscure, beyond raising a sudden influx of venture capital. Here, we explore these matters in formal detail, restricting argument to the asymptotic limit theorems of information and control theories.

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Wallace, R. (2006). Pitfalls in Biological Computing: Canonical and Idiosyncratic Dysfunction of Conscious Machines. Mind and Matter, 4(1), 91–113.

The central paradigm of artificial intelligence is rapidly shifting toward biological models for both robotic devices and systems performing such critical tasks as network management, vehicle navigation, and process control. Here we use a recent mathematical analysis of the necessary conditions for consciousness in humans to explore likely failure modes inherent to a broad class of biologically inspired computing machines. Analogs to developmental psychopathology, in which regulatory mechanisms for consciousness fail progressively and subtly understress, and toinattentional blindness, where a narrow 'syntactic band pass' de?ned by the rate distortion manifold of conscious attention results in pathological ?xation, seem inevitable. Similar problems are likely to confront other possible architectures, although their mathematical description may be far less straightforward. Computing devices constructed on biological paradigms will inevitably lack the elaborate, but poorly understood, system of control mechanisms which has evolved over the last few hundred million years to stabilize consciousness in higher animals. This will make such machines prone to insidious degradation, and, ultimately, catastrophic failure.

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