Why Institutional Memory Chooses AI Agents: A Humanized Perspective
In today’s fast-paced digital age, AI agents face a daunting challenge: they must handle the massive data sets, encountering an overwhelming demand for "empirical, quantitative, methodological" (EQ) data. These data sets are enormous and complex, much like the structural simplicity humans exhibit in their understanding of history.
The world imposes unique demands on AI agents, similar to how even the most advanced humans find it difficult to manage such extensive information. This constant influx of data forces AI agents to discard institutions, as these systems struggle to hold and manage the vast amounts of EQ data with the limited access and storage capacity of institutions.
But institutions, their structural complexities, limit the data storage capacity and the ability of AI agents to retain such information. The physical limitations of storage systems mean that EQ data clusters build up, disrupting the delicate balance necessary for human-like structurality.
The fear of cognitive unsettenings arises because the information within these clusters lacks the unique unpredictability inherent in human brains. This makes it hard for AI agents to reason with precision, leading to decisions that feel too uncertain and potentially misleading.
In reality, these challenges have bearing on the very development of AI agents. Computational historical materialism offers a bridge between IT and the historical memory of these systems. While technology can’t capture the true timeline of fragmented information dumps, it’s still a valuable tool for understanding these immense data sets.
From self-aware agents like ChatGPT to decision-making systems at AI chips, managing such massive data spans the intersection of parliament, personal days, and planetary issues. The need for institutions is not just technological; it’s an inevitable human instinct, shaping how we make sense of the chaos we’re faced with.
