Raw LLM Responses
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G
Should have known this was a bullshit story when u saw ACLU on it. Facial Recogn…
ytc_UgzraHNbo…
G
What is digital drawing? drawing, but on a pad, with a pen with no ink, its stil…
ytc_UgyEJCPHl…
G
Maybe not in chess necessarily
But in Texas hold'em poker Yes the AI is cheati…
ytr_Ugx_S84AN…
G
02:27 Technically, the plane they took the name from was Italian, so by Italian …
ytc_UgwiyqGjt…
G
Even I have found AI made Fakes of myself and AI on Patreon tried to get me to l…
ytc_UgztMle_n…
G
What the A.I Developers HAVE TO UNDERSTAND is, that A.I have to be seen and trea…
ytc_UgzpaLbzp…
G
Honestly, it’s wild how quickly linguistic patterns become suspect. Like the mom…
rdc_my7nuic
G
@birolunal319think again, Ai like dall e or sora USE PICTURES AS RECOURSE BUT D…
ytr_Ugz1_Xvee…
Comment
Learned a lot from this video. Two thumbs up. For the specific example he gave, the number tile, I think "reverse engineering" approach, couples with the AI process he described, will solve the problem more efficiently. That means I start with the end sequence = numbers in ascending order left to right, top to bottom. Then I map out all possible paths to "chaos" state = all tile arrangements that are not the end sequence. I can determine all possible chaos states = 16! = 2.092279e+13 assuming the hole is also a tile. The possible paths should be much less than 16! because each move along the way to a most "severe" chaos state is a chaos state itself. The map will look like a family tree, starting with the end sequence, and the last progeny of each branch is the most "severe" chaos. When user enters a chaos state, the algo finds where it is on the family tree, follow the reverse path/moves back up to the end sequence. The reverse-engineering approach will only work well when the goal/end is well defined.
youtube
AI Governance
2023-10-11T05:1…
♥ 11
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | none |
| Reasoning | unclear |
| Policy | none |
| Emotion | approval |
| Coded at | 2026-04-27T06:24:53.388235 |
Raw LLM Response
[{"id":"ytc_UgwoqklYpulnTTI7UW14AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"},
{"id":"ytc_UgxkmpcQxxmO7LhdXjV4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"},
{"id":"ytc_UgzU33GwlROeKXXCI794AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"},
{"id":"ytc_UgyiAp0OZjid-D0FTCN4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"},
{"id":"ytc_Ugzd1bpl-FbaSohT2T94AaABAg","responsibility":"unclear","reasoning":"unclear","policy":"unclear","emotion":"mixed"},
{"id":"ytc_UgwFh-yHF-HcsMNlBLN4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"},
{"id":"ytc_UgySO1rE6aWBkC3m6t94AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"},
{"id":"ytc_Ugwv_ZaBlREQe0as8st4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytc_UgzKYL2mRURysmjAuo94AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"},
{"id":"ytc_UgyWOXoXdToOt6aNcBV4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"}]