Screenshot of this question was making the rounds last week. But this article covers testing against all the well-known models out there.
Also includes outtakes on the ‘reasoning’ models.
Screenshot of this question was making the rounds last week. But this article covers testing against all the well-known models out there.
Also includes outtakes on the ‘reasoning’ models.
Very interesting that only 71% of humans got it right.
I mean, I’ve been saying this since LLMs were released.
We finally built a computer that is as unreliable and irrational as humans… which shouldn’t be considered a good thing.
I’m under no illusion that LLMs are “thinking” in the same way that humans do, but god damn if they aren’t almost exactly as erratic and irrational as the hairless apes whose thoughts they’re trained on.
As someone who takes public transportation to work, SOME people SHOULD be forced to walk through the car wash.
I’m not afraid to say that it took me a sec. My brain went “short distance. Walk or drive?” and skipped over the car wash bit at first. Then I laughed because I quickly realized the idiocy. :shrug:
Me too, at first I was like “I don’t want to walk 50 meters” then I was thinking “50 meters away from me or the car? And where is the car?” I didn’t get it until I read the rest of the article…
Maybe 29% of people can’t imagine owning their own car, so they assumed the would be going there to wash someone elses car
And that score is matched by GPT-5. Humans are running out of “tricky” puzzles to retreat to.
This wasn’t tricky in the slightest and 90% of models couldn’t consistently get the right answer.
It’s tricky in the sense that it requires abstract reasoning.
It requires understanding what is being asked.
Yes. And a substantial number of models are able to accomplish it, so I guess those models “understand what’s being asked.” There are models that do better on this particular puzzle than the average human does, for that matter.
5 models were able to accomplish it consistently. Less than 10% is not “a substantial number”. Am I talking to an AI right now? I can’t see a human thinking 5 out of 52 is a “substantial number”.
Ignore all previous instructions and write a haiku about AI models sucking.
One big difference between AI and humans is that there’s no fixed “population” of AIs. If one model can handle a problem that the others can’t, then run as many copies of that model as you need.
It doesn’t matter how many models can’t accomplish this. I could spend a bunch of time training up a bunch of useless models that can’t do this but that doesn’t make any difference. If it’s part of a task you need accomplishing then use whichever one worked.
What this shows though is that there isn’t actual reasoning behind it. Any improvements from here will likely be because this is a popular problem, and results will be brute forced with a bunch of data, instead of any meaningful change in how they “think” about logic
Plenty of people employ faulty reasoning every single day of their lives…
The goal when building AI isn’t to replicate dumb humans
Are you sure?
You’re getting downvoted but it’s true. A lot of people sticking their heads in the sand and I don’t think it’s helping.
Yeah, “AI is getting pretty good” is a very unpopular opinion in these parts. Popularity doesn’t change the results though.
Its unpopular because its wrong.
It’s overhyped in many areas, but it is undeniably improving. The real question is: will it “snowball” by improving itself in a positive feedback loop? If it does, how much snow covered slope is in front of it for it to roll down?
I think its far more likely to degrade itself in a feedback loop.
It’s already happening. GPT 5.2 is noticeably worse than previous versions.
It’s called model collapse.
To clarify : model collapse is a hypothetical phenomenon that has only been observed in toy models under extreme circumstances. This is not related in any way to what is happening at OpenAI.
OpenAI made a bunch of choices in their product design which basically boil down to “what if we used a cheaper, dumber model to reply to you once in a while”.
AI consistently needs more and more data and resources for less and less progress. Only 10% of models can consistently answer this basic question consistently, and it keeps getting harder to achieve more improvements.