Raw LLM Responses
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G
We can't, and thats the issue. Even with watermarks AI can easily edit that out …
ytr_Ugx3r3vI5…
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Agt already plays fake musicians and guitar players. Then have a fake ai audienc…
ytc_UgwY1DEbG…
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I'm genuinely happy to see so many comments about y'all being grateful and nice …
ytc_Ugwq6Xx48…
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I'm all for using AI for the good of mankind, for as long as "it" is an "it" and…
ytc_Ugy4UcZOd…
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if this will become a thing art is dead. AI generated shitty photos are already …
ytc_Ugx8RBOoh…
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Ryne AI Lecture Lab is next level. Paste any lecture URL and get organized study…
ytc_UgztZa_FL…
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No new questions means no real answers to train on. Eventually they start traini…
rdc_n7iybvu
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But of everything ChatGPT is truly a technological revolution the devs did a gre…
ytc_Ugy9VEmNI…
Comment
I get what you're saying, but AI doesn't lie. I've tested various platforms extensively, and they all scored 100% on factual information, even when I tried to trick them by asking them what year the Great Fire of Atlantis happened. If you understood the technology, you'd know that AIs' training data biases them towards certainty rather than saying "I don't know" (they were trained on humans' data, and humans are the same way). You can improve accuracy by asking AIs to insert uncertainty tags, do provenance tagging, or list confidence intervals. The things AIs generally hallucinate about are experiential things, like what they did for fun last week. If you ask them well-known facts, they have an extensive training data set, like sets so big that it takes weeks to train the models and costs millions of dollars, and they're extremely accurate (probably much more than a human teacher, in fact). In one experiment I ran, I collected around 1,500 pages of data, and there were maybe 5 hallucinations, all related to experiential things, not real-world factual knowledge.
youtube
2025-11-01T01:2…
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | ai_itself |
| Reasoning | consequentialist |
| Policy | none |
| Emotion | approval |
| Coded at | 2026-04-27T06:26:44.938723 |
Raw LLM Response
[
{"id":"ytr_Ugxgf0-oseDU1TyR2iJ4AaABAg.AOq0pzkK00mAOq8SEBIFfa","responsibility":"ai_itself","reasoning":"deontological","policy":"liability","emotion":"approval"},
{"id":"ytr_Ugw7wASvhCg9yEFd9vB4AaABAg.AOq-B6Emv73AOq1a1FC09Q","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"approval"},
{"id":"ytr_Ugw7wASvhCg9yEFd9vB4AaABAg.AOq-B6Emv73AOqlPO2Qdz_","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"approval"},
{"id":"ytr_Ugz6t_pB5UVIWszIXl94AaABAg.AOq-8BGZp0mAOq0ecEWUW-","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"indifference"},
{"id":"ytr_UgwiIrjzZOIlQB-zeYF4AaABAg.AOq-0PyPnVSAOq14AoImCX","responsibility":"company","reasoning":"virtue","policy":"regulate","emotion":"outrage"},
{"id":"ytr_UgxjcKWGOt0N8_9Zosx4AaABAg.AOpeqsmgtnyAOpjIyiQttg","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"approval"},
{"id":"ytr_UgyoS0yvN2lq2Bwxe314AaABAg.AOtY9E4PlEnAOuNd31OSW0","responsibility":"developer","reasoning":"deontological","policy":"liability","emotion":"mixed"},
{"id":"ytr_UgyoS0yvN2lq2Bwxe314AaABAg.AOtY9E4PlEnAOyB44YVh9t","responsibility":"government","reasoning":"virtue","policy":"regulate","emotion":"outrage"},
{"id":"ytr_UgxPb_YEPvjczbCkmZ94AaABAg.AOtGihUwNU2AOyBWwKfRUW","responsibility":"government","reasoning":"virtue","policy":"regulate","emotion":"outrage"},
{"id":"ytr_UgyLLbI3BUkF0kzA8Gl4AaABAg.AOt5y3rWbRMAOyD7xbLZ40","responsibility":"ai_itself","reasoning":"consequentialist","policy":"none","emotion":"approval"}
]