The prevailing strategy for advancing AI has been to simply increase the size and complexity of neural models, but a new approach proposes a modular cognitive architecture that distributes cognitive functions across various computational substrates beyond just the neural core. This shift is significant because it suggests that intelligence isn't solely about model size, but rather about strategically allocating cognitive tasks—like reasoning, memory, and tool use—to the most efficient components. This perspective is particularly relevant for resource-constrained environments like Edge AI, and future research may explore whether AI systems can even design their own architectures.
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