Raw LLM Responses
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We already have two forms of income similar to ubi. Unemployment and social secu…
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Fun fact: Ai has no copyright
So taking the images is completely legal.
And wi…
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G
You left one very important piece of data, AI is getting exponentially better an…
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YouTube using ai to ban ai is a joke. and regular non ai creators are being caug…
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The AI artist depicted here isnt a straw man, its just a straight up straw mount…
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What gets me interested in this video?
What's the video about
Sophia the robot
W…
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The purpose of most machine learning applications (ai is a subset of machine lea…
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If dry van and reefer get automated there will be more drivers competing for tho…
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Comment
>Nothing available so far is going to let these things drive in a snowstorm.
While it is true that current *civilian* [GPS capability](https://en.wikipedia.org/wiki/GPS_Block_IIIA) is typically limited to an accuracy of approximately 10-13 meters (approx. 32-42 feet), plans have already long been in-motion to [upgrade civilian-accessible GPS-capabilities](https://en.wikipedia.org/wiki/GNSS_enhancement) to 20-30 centimeters (approx. 8-12 inches) of so-called "absolute" accuracy.
Coupled with recently commodified multi-axis inertial and other kinematic sensors (like those now found in numerous "drone-type" aircraft these days), the resultant positional accuracy can be boosted to less than 10 centimeters (approx. 4 inches).
Going further, automotive "vision-systems" that are equipped with hyper-spectral or multi-spectral sensors (which are also on their way to becoming a commodity-item) enable computer-systems in vehicles to easily see through even the most problematic of environmental conditions.
Multi-spectral and hyper-spectral "camera-sensors" can "see" right through dense fog, smoke, clouds, rain, direct-sunlight, and pitch-black nights. These types of sensors can even differentiate, with much greater certainty than any human, whether or not there is snow, ice, frost, water, or dry pavement on the road ahead. Said sensors also allow autonomous-driving computers to determine, well in-advance, if the path of the vehicle will cross any such hazards ahead.
Said computers can essentially "pre-calculate" several options for the safest evasive or corrective control-actions to take if a hazard-encounter is detected as imminent. They also tend to have pre-programmed "contingency-actions" they can instantly perform, in the case of careless pedestrians, wayward bicyclists, random pot-holes, and falling debris.
Regarding automated-handling and sensing in off-road or degraded road-surface conditions, Consider, if you would, the (relatively ancie
reddit
AI Harm Incident
1455316100.0
♥ 5
Coding Result
| Dimension | Value |
|---|---|
| Responsibility | unclear |
| Reasoning | unclear |
| Policy | unclear |
| Emotion | indifference |
| Coded at | 2026-04-25T08:33:43.502452 |
Raw LLM Response
[{"id":"rdc_czy4rgr","responsibility":"ai_itself","reasoning":"consequentialist","policy":"ban","emotion":"outrage"},{"id":"rdc_czxhtj3","responsibility":"unclear","reasoning":"consequentialist","policy":"unclear","emotion":"fear"},{"id":"rdc_czxumr2","responsibility":"unclear","reasoning":"unclear","policy":"unclear","emotion":"indifference"},{"id":"rdc_czy3iib","responsibility":"unclear","reasoning":"consequentialist","policy":"regulate","emotion":"approval"},{"id":"rdc_czxpe57","responsibility":"unclear","reasoning":"consequentialist","policy":"unclear","emotion":"approval"}]