Embracing Constraints: The Key to Authentic Cognition in AI Development
The concept of cognition within the realms of both human minds and artificial intelligence raises a thought-provoking discussion: the necessity of cognitive constraints for cognition itself to exist. It suggests that the very ability to think, perceive, and understand is inherently tied to the limitations within which a cognitive system operates. This perspective has profound implications in the development of AI, especially in the field of Artificial General Intelligence (AGI). It leads us to consider that by designing AI with certain constraints, we are not just controlling its capabilities, but perhaps enabling it to 'think' and 'perceive' in ways more aligned with human cognition. This insight opens up a new avenue in AI development, focusing on how limitations are not just safeguards, but essential components that define the nature and quality of cognition itself.
PARADOX
Ramesh Ramloll
1/10/20241 min read
The Cognitive Constraint Conjecture and the Unconstrained AI Paradox
The Cognitive Constraint Conjecture, posits that innate limitations in mental bandwidth give rise to abstraction as a strategy for managing informational complexity. Under this model, finite cognitive capabilities manifest as selective focus on key details and general representation of concepts. Without such constraints, the drive to abstract away extraneous information may not develop. This view implies that since meaning stems from mental concepts and human emotions, which are simplified representations of more intricate information, without cognitive constraints the experience of a meaningful life becomes impossible. We find that this conjecture also has a consequent conjecture that is relevant to how we can construct AI agents with inner workings that can be inspected, controlled or communicated with easily.
The Cognitive Constraint Conjecture also gives rise to what we call the “Unconstrained AI Paradox”. This suggests that an AGI without limitations may lose the impetus for human-like abstraction and meaning, and may even find the quest for finding the true nature of reality not only an unsolvable problem but also an irrelevant one. An AI system with no constraints might never develop perception or intentions. This is because such systems, theoretically capable of processing all available information simultaneously and executing any tasks without limitations, would have no need for the strategic allocation of resources. It highlights the counterintuitive idea that the lack of constraints might actually hinder the development of advanced cognitive processes like perception and intentionality in AI systems.
Together, these conjectures and paradox present a pathway for AI development, where future AI systems might not only exhibit intelligence indistinguishable from humans but do so through cognitive processes akin to our own, shaped by their unique cognitive constraints. However, if we allow AI systems to freely evolve their own abstractions, then the constraints arising may result in AI systems devoid of some of the very cognitive traits that make human intelligence what it is, and we can expect break down in communications between humans and AGI.
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