Raw LLM Responses
Inspect the exact model output for any coded comment.
Look up by comment ID
Random samples — click to inspect
G
This all great except these schools are very EXPENSIVE. They usually have a few …
ytc_UgwmuUHzn…
G
I asked ChatGPT myself & it seems like it further retconned its answers. Now it …
ytc_Ugw38vzi_…
G
I do miss the time when AI art was just a small thing that wasn't super accurate…
ytc_Ugzl_B701…
G
Then why the HELL would you invent A.I in the first place bro, then warning peop…
ytc_Ugy_vh_CO…
G
More AI scapegoat propaganda. Smh!
As if 80% of corporate/office jobs were e…
ytc_UgxlH-AbZ…
G
What country is that! AI is at the finger points of everyone in every country of…
ytr_UgxZ90z2A…
G
Will you be equally outraged once the tech advances enough to the point driverle…
ytr_UgwIgz_Ne…
G
There's a difference between inspiration and using someones work. All ai artbis …
ytc_UgwBIOypm…
Comment
Eliezer's take on the 'paperclip maximizer' argument doesn't seem particularly applicable to current LLM architectures. When I ask ChatGPT for an answer, it neither gets stuck in an infinite loop nor produces endless responses in an attempt to 'maximize' its objective. Working with agents also involves setting constraints: we can specify a finite number of actions the model should run, and there's a system of permissions to accept or deny subroutine actions. It's unclear why Mr. Wolfram didn't tie this argument to known, practical AI procedures.
Also, if AGI truly achieves human-level general intelligence, it would presumably possess practical judgment capabilities. ChatGPT, for instance, provides finite responses rather than infinite outputs, and an AGI would theoretically have even more refined judgment. Just as adults have better risk assessment skills than children, an AGI should theoretically evaluate actions within realistic limits rather than pursuing infinite maximization of a single goal.
youtube
AI Governance
2024-11-13T16:0…
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | developer |
| Reasoning | consequentialist |
| Policy | none |
| Emotion | indifference |
| Coded at | 2026-04-27T06:24:53.388235 |
Raw LLM Response
[
{"id":"ytc_UgwfYHnRIec_UjaORrV4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"approval"},
{"id":"ytc_UgycnzNreGpB3a7a5Hp4AaABAg","responsibility":"none","reasoning":"mixed","policy":"none","emotion":"indifference"},
{"id":"ytc_Ugzd-ma0ujZAb5HhHFp4AaABAg","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"mixed"},
{"id":"ytc_UgzsZtPkhMQCcCOmHgB4AaABAg","responsibility":"developer","reasoning":"consequentialist","policy":"none","emotion":"indifference"},
{"id":"ytc_UgxYn9JXLlg20G_a09d4AaABAg","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"outrage"},
{"id":"ytc_Ugz2_DwgYk7tALNnvm54AaABAg","responsibility":"ai_itself","reasoning":"consequentialist","policy":"none","emotion":"fear"},
{"id":"ytc_Ugwad4p8PY-nWvnjzPN4AaABAg","responsibility":"none","reasoning":"consequentialist","policy":"none","emotion":"outrage"},
{"id":"ytc_Ugx0w3H6RV1sNvUp1ZV4AaABAg","responsibility":"none","reasoning":"mixed","policy":"none","emotion":"mixed"},
{"id":"ytc_UgyR6_fTp_kjrcdO_SV4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"indifference"},
{"id":"ytc_UgwxlrHOJKfspbgJ1TZ4AaABAg","responsibility":"none","reasoning":"unclear","policy":"none","emotion":"mixed"}
]