Raw LLM Responses
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G
AI will never be conscious. It is an impossibility. Without the spark of source,…
ytc_UgwN9fSFJ…
G
obviously they will be tools for people go get better. but you can tell every AI…
ytc_UgzfMtv1S…
G
Abundance will be created, but not for the many just the few controlling the AI …
ytc_Ugz8B5Pl9…
G
oh look another white liberal man mocking AI slop and using his evidence of AI b…
ytc_UgyMt0Phg…
G
Finally the wars between humans and machines have begun.
Much better than humans…
ytc_UgyIMsG6c…
G
I'm currently working on a novel I've been wanting to write since I was 13 (curr…
ytc_Ugz16X6z8…
G
The counter to all this, is anything that can be automated, is no longer a high …
ytc_UgxGGeKgD…
G
"All these skills cannot be replaced... for now."
Or EVER. Not by LLMs/chat bots…
ytr_Ugz7lQSzp…
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"}]