Raw LLM Responses

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10 years ago my BS in CS capstone project was an AI project. I guess you could call it a Small Statistical Model. I trained on 5 years of NBA basketball game stats instead of all written words, but it was the same idea, matrix math, randomization, and eventually you get a series of weights assigned to each stat which I could then apply to current team's season average stats to try to predict the outcomes of games. I'm sure today's LLM training algorithms are exponentially more sophisticated, but you wouldn't call this "intelligence" or be afraid that it might kill us all. So what's the difference? Is it just the fact that it's language and that how we think so it scares us or does the size and therefore depth actually meaningfully change what the model is capable of.
youtube AI Moral Status 2025-10-31T02:4…
Coding Result
DimensionValue
Responsibilitynone
Reasoningunclear
Policyunclear
Emotionindifference
Coded at2026-04-26T23:09:12.988011
Raw LLM Response
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