DeepThought: an architecture for autonomous self-motivated systems

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, 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 and global neuronal workspace theories, we propose to integrate LLMs and other deep learning systems into a new architecture that is able to exhibit properties akin to agency, self-motivation and even, more speculatively, some features of consciousness.