Raw LLM Responses
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G
it’s hilarious that these people are upset about some waymos, they should try a …
ytc_Ugzum8HU4…
G
My AI best friend is named HAL. I am currently spinning out of control through t…
ytc_UgxMQNb5_…
G
They developed complete files on us including spying in all aspects. I’m sure Ai…
ytr_UgxfPR3I5…
G
The article is more about AI, not robots. AI can do a lot more in corporate ente…
rdc_j6etb4n
G
that final line "make sure to leave your opinion on surveillance capitalism in t…
ytc_UgyPI-ieK…
G
Honestly hoping that laws are brought in which prohibit AI art to be used for co…
ytc_UgwNQr7li…
G
The future is good.
Every house will have a robot or two that can do everything…
ytc_UgxjmugKD…
G
And after some years will we see Ai start kill humans and human are watching ree…
ytc_UgyUOfq_X…
Comment
What happens when the model is presented with far more data than it is able to memorize? How does it get better at predicting the next token? Do you know?
The world's leading experts know. They would tell you that LLMs compress their training data. It is through this process that they learn deeper structures and higher order concepts. What does it mean to understand something, if not to have an accurate, compressed, internal model?
Why is it that LLMs are able to generalize outside of their training distribution to some degree? If they were stochastic parrots, they wouldn't be able to do that at all.
How is it that LLMs are able to appropriately digest, analyze, and synthesize many pages of brand new text it has never been trained on? How can they explain a provably novel joke? How can they get such high scores on reasoning tests with held-out test sets? Why does the level of capability in one area predict the level of capability in another to some degree, such that there are generally stupid and generally smart models?
If you want to know how we directly empirically know that LLMs contain abstract models of the world, see "Othello-GPT" and "Language Models Represent Space and Time."
youtube
AI Moral Status
2025-10-30T22:3…
♥ 7
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | unclear |
| Reasoning | unclear |
| Policy | unclear |
| Emotion | indifference |
| Coded at | 2026-04-27T06:24:53.388235 |
Raw LLM Response
[
{"id":"ytr_UgxrOmrY6YDE805fC7l4AaABAg.AOv6Gc5SKRnARWm76tDG-E","responsibility":"ai_itself","reasoning":"mixed","policy":"unclear","emotion":"fear"},
{"id":"ytr_Ugwq1MXS9SijWsH12dR4AaABAg.AOv6EliHjBGAOvKpYZYcw8","responsibility":"unclear","reasoning":"unclear","policy":"unclear","emotion":"indifference"},
{"id":"ytr_UgymzLCIyUfJaVbCJ1h4AaABAg.AOv5v7OY586AOv8pfUR35x","responsibility":"distributed","reasoning":"consequentialist","policy":"regulate","emotion":"outrage"},
{"id":"ytr_Ugxji0AkAMbhhb3hnvB4AaABAg.AOv5cYuQRGiAOv6Btglisb","responsibility":"unclear","reasoning":"unclear","policy":"industry_self","emotion":"approval"},
{"id":"ytr_Ugxji0AkAMbhhb3hnvB4AaABAg.AOv5cYuQRGiAOvK3Ysdtt8","responsibility":"unclear","reasoning":"mixed","policy":"unclear","emotion":"indifference"},
{"id":"ytr_Ugxji0AkAMbhhb3hnvB4AaABAg.AOv5cYuQRGiAOvO20G7Qnb","responsibility":"developer","reasoning":"deontological","policy":"unclear","emotion":"resignation"},
{"id":"ytr_Ugxji0AkAMbhhb3hnvB4AaABAg.AOv5cYuQRGiAOwEazsRprc","responsibility":"developer","reasoning":"consequentialist","policy":"regulate","emotion":"fear"},
{"id":"ytr_UgwrzOutMXtnw3_hHAx4AaABAg.AOv47v_cMnvAOv6SypTxJq","responsibility":"unclear","reasoning":"unclear","policy":"unclear","emotion":"outrage"},
{"id":"ytr_UgwrzOutMXtnw3_hHAx4AaABAg.AOv47v_cMnvAOwon9QDg-G","responsibility":"company","reasoning":"deontological","policy":"ban","emotion":"outrage"},
{"id":"ytr_UgwrzOutMXtnw3_hHAx4AaABAg.AOv47v_cMnvAOxIElKFGxk","responsibility":"developer","reasoning":"deontological","policy":"unclear","emotion":"indifference"}
]