This paper presents an information-processing theory of consciousness and argues that instantiating conscious subsystems in AI could enable superior adaptation without extensive training, potentially leading to AGI.
This new paper develops an information-processing theory of consciousness and uses it to identify how consciousness can be instantiated in AI, paving the way for genuine AGI and beyond (the paper demonstrates that conscious functioning is the missing ingredient that enables a toddler to navigate an obstacle-strewn room or an 18 year-old to learn to drive with massively less training than is required by a robot or autonomous vehicle): Abstract An acceptable information-processing theory of consciousness should be able to identify the adaptive advantages that drove the emergence of consciousness during the evolution of life. It should also predict the specific dynamical architecture of information processing that would need to be instantiated in AI to produce consciousness and the superior adaptation it enables. Whether such an instantiation produces AI that is actually conscious and also more adaptable would provide the ultimate test of the theory. A prime candidate for such a theory is the Subject-Object Emergence Theory of consciousness. It argues that consciousness first evolved because it enabled organisms to achieve adaptive body-environment coordination without extensive trial-and-error learning. It postulates that the subject in an appropriate Subject-Object subsystem would be able to use depictive (iconic) visual representations of the relative positions of its body and the environment to guide motor actions that will produce adaptive body-environment coordination. The depictive representations will 'light up' for such a subject, producing subjective experience that is used to deliver adaptive benefits. Hand-eye coordination is a familiar example in humans—novel and intricate coordination tasks can be undertaken without additional reinforcement learning, provided focused conscious attention is employed to provide us (the subject) with relevant depictive images. The paper identifies how such a conscious Subject-Object subsystem could be instantiated in AI systems, enabling hand-eye and other body-environment coordination without the extensive reinforcement learning or complex computational programming needed at present. Drawing further on the Subject-Object theory of consciousness, the paper also identifies how these simple conscious subsystems evolved further in organisms to establish the conscious modelling that enables conscious planning, imagining, abduction and other higher cognitive functions. It demonstrates that current approaches to incorporating world modelling in AI will fail to achieve key elements of the general intelligence found in humans that require consciousness. The full paper can be accessed freely at: https://ssrn.com/abstract=6911039
The article argues that understanding human consciousness is key to bridging quantum mechanics and general relativity, and to achieving AGI, suggesting that internal observer mechanisms in AI could lead to breakthroughs.
A new analysis published in Neuron argues that current scientific methods may conflate consciousness with general information processing, raising questions about claims of consciousness in AI, animals, fetuses, and organoids.
This position paper argues that incorporating metacognition as a design principle can lead to more accurate, secure, and efficient AI systems, and demonstrates the concept through a Federated Learning case study and a software framework for experimentation.
A philosophical discussion questioning whether humans can objectively prove their consciousness in a way that current AI cannot, highlighting the subjective nature of such proof.
A critique of the popular debate around AI consciousness, arguing that people are misled by fluent language outputs and that a mechanistic theory like Integrated Information Theory is needed to properly assess consciousness in AI systems.