meander_water

Well if you read the article

> According to its own projections, the data center Google wanted to build in Cerrillos was poised to use 169 liters of water every single second. In just one day, the data center would consume as much water as all 80,000 residents of Cerrillos use for drinking in a year. To make matters worse, it wanted to draw that water from an underground aquifer that had been under a decree that restricted how much water could be removed since 2005.

> for far less valuable purposes

You think AI is more valuable than food? Finding this really hard to compute

> The most prophetic movie of all time

I think we need to collectively get our heads out of our asses. The world is literally flying headfirst into an unimaginable crisis (food and water shortages, loss of inhabitable land, massive inequality, power shortages) via human induced climate change.

We won't have the luxury of having enough power to even keep an AI alive for more than 5 minutes. People need to get a grip and deal with real problems.

Can someone who understands maths more than me explain why it could only solve 372/8000 problems?

What was it about the other problems that made them unsolvable? Was it just a time constraint, or are they just harder problems?

Sure, but you the user also didn't indend to plagiarize, yet that was the end result. And it was a direct result of plagiarized data from the training set.

Adobe is not generating the image.

AI models are generating the image

Pretty simple.

Just looking through your results, seems like gpt-6 luna was run with reasoning:off for a lot (all?) results. Seems like an unfair comparison.

> This contrasts with the world of Large Language Models (LLMs), which are largely non-deterministic, but are open-ended enough to reason...

This seems misleading. Decision models do not produce deterministic output. Repeated calls can product different decisions just like an LLM with structured outputs.

I actually think this might be a false positive. I think it got tripped up by the higher than average use of jargon (which I don't mind here because the article itself flowed well and raised good points).

Gpt-zero scores "human", and I've always found it to be a better judge

Sounds like you could just use an autocomplete model for the exact same benefit.

And that's not a criticism, that's what I've come back to myself.