We explore "sleep-time compute," a method for optimizing the efficiency of large language model inference by pre-processing context during idle time. This involves giving an AI a body of information, which it then analyzes offline to anticipate potential future queries. Subsequent questions can be answered more quickly and potentially with less computational cost.

Keywords: AI, Artificial Intelligence, LLMs, Large Language Models, AI Consciousness, Machine Thinking, AI Understanding, Philosophy of AI, Chinese Room Argument, John Searle, Self-Awareness, Machine Learning, Deep Learning, Technological Singularity, AI Limitations, Genuine Intelligence, Simulated Intelligence, AI Ethics, Future of AI, Apple AI Research, Symbolic Reasoning, Syntax Semantics.