throwaway6s1df·7 giorni fa·discussI don’t know how anyone with a neutral view can confidently take this:> Direction: All 21 pelican-bicycle images, across all seven labs, face right. No other animal/vehicle combination does that.> However, facing right is common: 60% of all 1,008 images do it…and the tables in “Evidence #5” to be anything but evidence the models have likely trained on pelican on bicycle data more than others.The data clearly shows:- 100% pelican on bicycle facing right- significant skew to the right for bicycle-like vehicles- significant preference for right facing for birdsAveraging those extreme results to “60%” to make it sound like it’s pretty fair because it’s close to “50%” isn’t statistically sound.The methodology is generally unsound. There is no actual scoring with a well defined rubric, it’s just vibed with a single model (GPT 5.6 Luna).The “not better at drawing” evidence are equally hard to take seriously when there is no clear, non-subjective indication of what better or worse is.
> Direction: All 21 pelican-bicycle images, across all seven labs, face right. No other animal/vehicle combination does that.
> However, facing right is common: 60% of all 1,008 images do it…
and the tables in “Evidence #5” to be anything but evidence the models have likely trained on pelican on bicycle data more than others.
The data clearly shows:
- 100% pelican on bicycle facing right
- significant skew to the right for bicycle-like vehicles
- significant preference for right facing for birds
Averaging those extreme results to “60%” to make it sound like it’s pretty fair because it’s close to “50%” isn’t statistically sound.
The methodology is generally unsound. There is no actual scoring with a well defined rubric, it’s just vibed with a single model (GPT 5.6 Luna).
The “not better at drawing” evidence are equally hard to take seriously when there is no clear, non-subjective indication of what better or worse is.