  This paper examines the challenges associated with achieving life-long
superalignment in AI systems, particularly large language models (LLMs).
Superalignment is a theoretical framework that aspires to ensure that
superintelligent AI systems act in accordance with human values and goals.
Despite its promising vision, we argue that achieving superalignment requires
substantial changes in the current LLM architectures due to their inherent
limitations in comprehending and adapting to the dynamic nature of these human
ethics and evolving global scenarios. We dissect the challenges of encoding an
ever-changing spectrum of human values into LLMs, highlighting the
discrepancies between static AI models and the dynamic nature of human
societies. To illustrate these challenges, we analyze two distinct examples:
one demonstrates a qualitative shift in human values, while the other presents
a quantifiable change. Through these examples, we illustrate how LLMs,
constrained by their training data, fail to align with contemporary human
values and scenarios. The paper concludes by exploring potential strategies to
address and possibly mitigate these alignment discrepancies, suggesting a path
forward in the pursuit of more adaptable and responsive AI systems.
