Model of human cognition
This work addresses the problem of developing more explainable and efficient AI systems for researchers and practitioners, though it appears incremental as it builds on existing neuro-theoretical concepts.
The paper tackles the limitations of large language models, including lack of explainability and high costs, by proposing a neuro-theoretical framework for intelligence emergence, offering theoretical insights into cognitive processes and a computationally efficient approach for explainable AI.
The development of large language models (LLMs) is limited by a lack of explainability, the absence of a unifying theory, and prohibitive operational costs. We propose a neuro-theoretical framework for the emergence of intelligence in systems that is both functionally robust and biologically plausible. The model provides theoretical insights into cognitive processes such as decision-making and problem solving, and a computationally efficient approach for the creation of explainable and generalizable artificial intelligence.