[Empirical135]

Felt quite off for me: Wrong salary guess, wrong food preferences, wrong political affiliation, partially correct hobbies, wrong ad targeting ideas. What was surprisingly accurate: location and the fact that I (an academic male in his thirties photographed close to the mountains) might like hiking and coffee.

If you knew which bike model I was googling yesterday, almost all of these guesses might have been more accurate.

[mysteria]

> If you knew which bike model I was googling yesterday, almost all of these guesses might have been more accurate.

I think this sort of guessing is intended to be combined with additional data the marketers already have, like purchase history, location, social media posts, and so on. Basically the VLM output is treated as another data point rather than the sole source, or the existing data could be fed into the model's prompt before reading the image.

[Empirical135]

That makes sense and is a fair point. And maybe the guesses from the picture would be more revealing if they had my purchase history as context. But I wanted to make the point that the last 10 web pages that I visited would probably allow you to make much more accurate predictions about me that would partly contradict the guesses the model made.

[dudefeliciano]

just knowing you were googling bikes i think i can guess your political affilition - unless there is some golden MAGA bike that I haven't heard about yet.

[throwaw3y]

Well, you'd be incorrect in my case. Guess that's the funny thing about stereotypes.