AI is characterized as 'anti-intelligence,' an inversion of genuine knowledge that prioritizes linguistic coherence over comprehension. This leads to confusion between actual understanding and mere coherence. Large language models (LLMs) do not grasp meaning or have genuine thoughts; instead, they predict language patterns without grounding them in context or intention. This structural blindness results in an illusion of knowledge that can fail when confronted with judgment-based questions or irrelevant topics, exposing the brittleness of their coherence.
AI is not just a reflection of human cognition but an inversion of it, presenting a cognitive counterfeit that is fluent yet ungrounded in our humanity.
The confusion between coherence and comprehension may be reshaping our understanding of intelligence, decision-making, and thought processes.
Anti-intelligence represents the performance of knowing without understanding, as LLMs operate on prediction rather than perception, lacking the foundational comprehension.
The systems we call intelligent create an appearance of knowledge that can fail spectacularly when faced with non-linear or irrelevant inquiries.
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