Comment by dghlsakjg
8 hours ago
I’ll keep that in mind next time I need to tell what time it is by asking an llm to read an analog clock.
Snark aside, I’m not sure that these gotcha tests are any more useful than asking politicians gotcha questions. Sure, the model can’t tell me what time it is, but it can code the Wang algorithm for noisy audio matching in one shot. Maybe this is just me being an optimist, but this is my hiring philosophy and I guess maybe now my llm philosophy: I’m not interested in seeing how dumb I can make you look, I’m more interested in how smart you can be.
Reading any analog clock at any time level (edit: and a non-noisy vector rendered image at that) is absolutely table stakes for an allegedly frontier flagship vision model. As much as 1:1 OCR. If the model can't do that, there's something wrong. Doesn't matter if it's memorized some random thing you think is esoteric but is in all the training data and benchmarks.
The whole point of LLM/FMs vs good old fashioned ML is generalization to unknown domains, not just unknown tasks. The hunt for "gotchas" is the hunt for "not in your training data".
Is this an “alleged frontier flagship vision model”?
This is described as a brand new flash model - still experimental - from a lab that is a side project for an investment firm that has never had a vision model before. That doesn’t scream flagship or frontier to me.
I disagree. It's not even that useful to train LLMs to read an ancient analog clock.
Unless we're talking about AGI, I couldn't care less if an LLM is bad at things they won't be doing anyway.
I'd rather focus training data on more useful tasks.
It's not that useful for a person to be able to read an analog clock either, but if a supposed-genius came to me and confidently gave the wrong answer, it would say something about their strengths/weaknesses in general.
The whole point of these models is they're meant to be able to generalize fairly well, not just answer questions the got trained on.
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It's because the messaging for what the point of these things is supposed to be is all over the place. Ask 10 different people and you'll get 10 different answers:
- A superintelligence that will usher in an age of human enlightenment
- A superintelligence that will usher in an age of human enslavement
- A really cool way to rake in trillion of rich VC/investor money by promising you're building a superintelligence that will usher in an age of human en[slave/lighten]ment
- A transformer model for predicting output tokens given a series of input tokens, informed primarily by reddit, stack overflow, and 6000 years of classical literature.
- A replacement for white collar labor. Start now or join the permanent underclass.
- A convenient fuzzy-find tool also capable of some probably-correct code generation.
- The ultimate customizable text RPG experience (you can pick if G stand for game or...)
And so on.
So, some people see a new model and check for how close humanity is to enslavement. Some people check to see if it got better at fixing broken unit tests.
Where did deepseek claim any of that?
What's amazing is that all of these are true at once. If you allow for some significant slack in what "superintelligence" means.
Knowing where it fails is just as important as knowing where is excels.
It is like asking a politician how much a coffee costs, to show how disconnected they are from common people. Super intelligence not being able to read a simple analog clock does the same.
> Sure, the model can’t tell me what time it is, but it can code the Wang algorithm for noisy audio matching in one shot
This is about a _vision_ model.
I find collecting “gotcha” questions helpful because when a new model can answer them, it shows improvement, and if it doesn’t, it’s a reality check that, despite the model being helpful for many tasks, there are other things it can’t do yet. It’s a demonstration of “jagged intelligence.”
It doesn’t have to be a negative thing! Simon’s pelican on a bicycle prompt is an example of a “gotcha” question.
It's still useful to find things it can't do if anything so we can tell when it starts being able to do them.
I was literally working on an educational game for my kids last week where one of the activities is clock reading, and I ask codex to QA its Godot program via screenshots, so literally this exact scenario is something I was doing in a software engineering context. It can of course write code to figure out the angles to rotate by just fine, but it also needs to be able to figure out whether the whole picture comes together, whether the hand sprites are anchored on the clock face correctly with the right pivot, etc.
Is being asked to read a clock really a gotcha?
It is, apocryphally, a gotcha test for Gen-alpha. As is being able to return the correct change or make a phone call to a human being. Reminds me of the iRobot line: "Can you create a symphony?", "No. Can you?".
Not if you are aiming at a general intelligence but it’s worth considering that this is a tool that may not be able to count the number of strawberries in the letter R but can still center a div.
If I had to hire an engineer and there was one that could one shot the wang algorithm, but couldn’t read an analog clock, I would have no problem hiring them.
Also worth noting that both models got it wrong. Qwen made a mistake that humans very good at reading clocks would make. Deepseek made a mistake that a human who had just learned to read clocks would make.
It would be different if AI was known to be reliable but it isn’t, so this is less of a random failure and more a symptom of jagged intelligence.
And with every one of these there’s always an attempt to minimize the problem by saying it’s just one silly failure.