Raw LLM Responses

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I distinction I believe is gravely unrecognized is that LLM based AIs are incapable of actually thinking. They do not possess semantic, causal, or goal-directed intelligence, only a statistical imitation fueled by pattern recognition from their training data. The easiest example of this is mathematics. LLMs are not capable of calculating equations. Chatgpt will answer math questions correctly only because it has an external module for calulated math to call on when needed. AIs like chatgpt use a number of external modules to supplement their shortcomings, but they still use the LLM as the primary brain to operate. The LLM being the primary brain is the problem and no amount of training or data will ever be enough to make it safe and reliable....but LLM's are cheaper to make. The alternatives are extremely expensive and difficult to make, so companies are throwing all their eggs into the LLM basket and trying to finagle it into a functional state with post processing rules and supplemental modules. It's a dead end. Something like a Neurosymbolic AI is the only real path forward. A million times harder to create but the only way if you want the AI to actually be safe and reliable.
youtube 2025-12-17T16:1…
Coding Result
DimensionValue
Responsibilityunclear
Reasoningunclear
Policyunclear
Emotionunclear
Coded at2026-04-26T23:09:12.988011
Raw LLM Response
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