I think you might have stumbled upon something surprisingly profound.
https://www.psychdb.com/cognitive-testing/clock-drawing-test
I think you might have stumbled upon something surprisingly profound.
https://www.psychdb.com/cognitive-testing/clock-drawing-test
Interestingly, clocks are also an easy tell for when you're dreaming, if you're a lucid dreamer; they never work normally in dreams.
For me personally, even light switches have been a huge tell in the past, so basically almost anything electrical.
I've always held the utterly unscientific position that this is because the brain only has enough GPU cycles to show you an approximation of what the dream world looks like, but to actually run a whole simulation behind the scenes would require more FLOPs than it has available. After all, the brain also needs to run the "player" threads: It's already super busy.
Stretching the analogy past the point of absurdity, this is a bit like modern video game optimizations: the mountains in the distance are just a painting on a surface, and the remote on that couch is just a messy blur of pixels when you look at it up close.
So the dreaming brain is like a very clever video game developer, I guess.
So the idea is to develop habits called “reality checks” when you are awake. You look for the broken clock kind of anomalies that the grandparent comment mentioned. You have to be open to the possibility of dreaming, which is hard to do.
Consider this difficulty. Are you dreaming?
…
…
How much time did it take to think “no”? Or did you even take this question seriously? Maybe because you are reading a hn comment about lucid dreams, that question is interpreted as an example instead of a genuine question worth investigating, right? That’s the difficulty. Try it again.
The key is that the habit you’re developing isn’t just the check itself — it’s the thinking that you have during the check, which should lead you to investigate.
You do these checks frequently enough you end up doing it in a dream. Boom.
There’s also an aspect of identifying recurring patterns during prelucidity. That’s why it helps to keep a dream journal for your non-lucid dreams.
There are other methods too.
Maybe reality is a world of broken clocks, and they only “work” in the simulation.
An amusing pattern that dates back to "1kg of steel is heavier of course" in GPT-3.5.
Obviously, humans failing in these ways ARE in the training set. So it should definitely affect LLM output.
Second: the failures go away with capability (raw scale, reasoning training, test-time compute), on seen and unseen tasks both. Which is a strong hint that the model was truly failing, rather than being capable of doing a task but choosing to faithfully imitate a human failure instead.
I don't think the influence of human failures in the training data on the LLMs is nil, but it's not just a surface-level failure repetition behavior.
Or maybe something like https://www.youtube.com/watch?v=dhZxdV2naw8