An exploration of a two-layered artificial intelligence: integrating neural codification with normative reasoning

Abstract

Recent work in artificial intelligence (AI) has produced models that excel at pattern recognition and language generation using deep neural networks. While large language models (LLMs) demonstrate emergent intelligence at a base level, they lack a higher-order normative layer that is capable of refining, validating, and ethically grounding raw outputs. Drawing on classical systems of epistemology such as the Nyāya tradition from India, and selected Western philosophical systems in epistemology, logic, ethics, and metaphysics, we argue that human intelligence is layered. The brain encodes knowledge in distributed neural networks, analogous to the parameters of LLMs, but a higher layer of normative reasoning actively evaluates and validates that raw information. We propose that future AI should integrate these two layers to produce systems that are not only statistically powerful at a lower level, but also transparent, logically consistent, and ethically accountable. We outline a conceptual architecture for such two-layered AI and discuss concrete steps and research directions for building this normative layer into AI architectures. By combining the low-level power of deep learning with the rigor of normative reasoning, the next generation of AI can better align with the encoding and usage of knowledge in humans.