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.
And that score is matched by GPT-5. Humans are running out of “tricky” puzzles to retreat to.
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 don’t need to do the dehumanizing pro-AI dance on behalf of the tech CEOs, Facedeer
I’m not doing it on behalf of anyone. Should we ignore the technology because we don’t like the specific people who are developing it?
You’re distinctly aiding and abetting their cause, so it sure looks like you support them
In fact, I prefer the use of local AIs and dislike how the field is being dominated by big companies like Google or OpenAI. Unfortunately personal preferences don’t change reality.
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.
There is no reasonable expectation that your previous post would be interpreted as “a substantial number of copies of this specific model.”
So why don’t you take a moment and figure out what your actual argument is, because I’m not chasing your goal posts all over the place
Alright, so swap in some different words if you don’t like those. The basic point is the same - there’s a bunch of models from different sources that can solve this, it’s not just some weird one-off fluke.
Your own argument is a bit all over the place too, by the way. You said this puzzle “wasn’t tricky in the slightest” and yet that “it requires understanding what is being asked.” So only 71.5% of humans can accomplish this “not tricky in the slightest” problem, but there are some AI models that are able to “understand what is being asked”? Is “understanding” things not “tricky”?
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”.
I mean, we’re watching it happen. I don’t think it’s hypothetical anymore.
I feel that a lot of what is improving in the recent batch of model releases is the vetting of their training data - basically the opposite of model collapse.
Nothing requires an LLM to train on the entire internet.
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.
42 out of 53 models said to walk to the carwash.
And yet the best models outdid humans at this “car wash test.” Humans got it right only 71.5% of the time.