DeepThought: an architecture for autonomous self-motivated systems

Arlindo Oliveira, Tiago Domingos, Mario Figueiredo, Pedro Lima

Keywords: Artificial Intelligence, Deep Learning, Cognition Architecture, Cognitive Language Agents, Global Workspace Theory, Complementary Learning Systems, Attention Schema Theory, Large Language Models

TL;DR: The paper describes a proposal to integrate large language models and other deep learning models into a new architecture that is able to exhibit properties akin to agency, self-motivation, even some features of meta-cognition.

Abstract: 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.

Submission Number: 9