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Understanding Self-Awareness in Language Models
This chapter explores the conceptual frameworks of large language models, emphasizing active inference and its contrast with reinforcement learning. The speakers discuss how feedback loops can enhance an AI's self-awareness, illustrating this with analogies to human navigation experiences. They also delve into the complexities of fine-tuning and the implications of behavioral changes in language models, highlighting the philosophical and societal considerations of AI development.