No One Rung to Rule Them All: Addressing Scale and Expediency in Knowledge-Based AI

Can we drive effectiveness and efficiency of AI at the same time? If we want our systems to be more intelligent, do they have to become more expensive? Our goal should be to significantly increase the capabilities and improve the results of AI technologies while minimizing power and system cost, not by increasing it. Achieving this could be possible if we follow the architectural design observed time and again in natural control systems, that is, a hierarchy of specialized levels. This article challenges the single neural network's current large language model (LLM) approach, which attempts to encompass all world knowledge.