Hacker Newsnew | past | comments | ask | show | jobs | submitlogin

I uploaded the image to Gemini 2.0 Flash Thinking 01 21 and asked:

“ Here is a steps vs bmi plot. What do you notice?”

Part of the answer:

“Monkey Shape: The most striking feature of this plot is that the data points are arranged to form the shape of a monkey. This is not a typical scatter plot where you'd expect to see trends or correlations between variables in a statistical sense. Instead, it appears to be a creative visualization where data points are placed to create an image.”

Gemini 2.0 Pro without thinking didn’t see the monkey



It thought my bald colleague was a plant in the background. So don't have high hopes for it. He did wear a headset so that is apparently very plant like.


Favorite thing recently has been using the vision models to make jokes. Sometimes non sequiturs get old, but occasionally you hit the right one that’s just hilarious. It’s like monster rancher for jokes.

https://en.wikipedia.org/wiki/Monster_Rancher


Maybe he's actually a spy


Wrong kind of plant. See sibling comment.


That doesn't seem like an appropriate comparison to the task the blogger did. The blogger gave their AI thing the raw data - and a different prompt from the one you gave. If you gave it a raster image, that's "cheating" - these models were trained to recognize things in images.


To be fair, if someone gave me a bunch of raw data points and not the png I wouldn't see the monkey either


In the article they also give the png


> When a png is directly uploaded, the model is better able to notice that some strange pattern is present in the data. However, it still does not recognize the pattern as a gorilla.

I wonder if the conversation context unfairly weighed the new impression towards the previous interpretation.


I'm curious if there's any good resources on this, but I've noticed including the conversation context makes the responses drastically worse quality in my experience. It's gotten to a point whe


o1 said: It appears the points have been deliberately arranged to form a stylized figure rather than showing a conventional trend. In other words, there is no obvious linear or monotonic relationship between steps and BMI, and the red‐blue (female–male) split is intermixed throughout the shape. From this visualization alone, one cannot meaningfully conclude a correlation or difference in BMI by gender or by step count. Instead, the data are displayed in a pattern that resembles a cartoon character, suggesting the layout is artistic or contrived rather than reflecting a standard distribution.

o1 pro: It appears the data have been deliberately arranged to form a stylized humanoid figure. In terms of actual trends, there is no obvious linear or nonlinear correlation between steps and BMI, and the female–male color split seems fairly uniform across the shape. Visually, BMI spans roughly 15–30, while steps extend up to about 15,000, with no clear clustering or separation by gender. Within reasonable confidence (e.g., an estimated R² near zero if one were to attempt a linear fit), there is no discernible predictive relationship between steps and BMI in this plot.




Guidelines | FAQ | Lists | API | Security | Legal | Apply to YC | Contact

Search: