alt Hacker NewsI’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.
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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".
Knowing where it fails is just as important as knowing where is excels.
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.
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.
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.
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.