DeepThought: An Architecture for Autonomous Self-motivated Systems
Arlindo L. OliveiraTiago DomingosM\'ario FigueiredoPedro U. Lima
Arlindo L. OliveiraTiago DomingosM\'ario FigueiredoPedro U. Lima
Nov 2023
0被引用
1笔记
摘要原文
The ability of large language models (LLMs) to engage in credible dialogues with humans, taking into account the training data and the context of the conversation, has raised discussions about their ability to exhibit intrinsic motivations, agency, or even some degree of consciousness. We argue that the internal architecture of LLMs and their finite and volatile state cannot support any of these properties. By combining insights from complementary learning systems, global neuronal workspace, and attention schema theories, we propose to integrate LLMs and other deep learning systems into an architecture for cognitive language agents able to exhibit properties akin to agency, self-motivation, even some features of meta-cognition.
