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That's absolutely not true.

You might get replicas of incredibly popular works like Mona Lisa due to overfitting but that's it.

If I am wrong please do provide an example as this is very relevant and interesting (and impossible from an information theory pov).


https://www.technologyreview.com/2023/02/03/1067786/ai-model...

The approach I've seen is to prompt for people with unusual names, they're often be only a single source image in the input data set that gets reproduced by the AI.

I've seen examples with the AI "generated" images and the source image side by side - I'll try and find them.

EDIT: Link to the paper https://arxiv.org/abs/2301.13188 and this articles has the example images: https://www.theregister.com/2023/02/06/uh_oh_attackers_can_e... Look at the Ann Graham Lotz image and tell me that isn't the source image being reproduced in a lossy manner.


I agree with this and reflects my personal experience.

While potencial of a large audience is great, if you don't even have users to start with, the choice should be the response to "what gets PMF faster" and not "what do I need to take me from 500k ARR to +2M ARR".


That tweet doesn't reflect my experience at all actually, in Europe at least. I know it's an anecdote but I know many fit people that drink frequently Diet Coke or Coke Zero. Is this a US thing?


Coke Zero is much more common as an Everyman drink in Europe apparently, perhaps because of less sugar insensitivity.

In the USA a large size combo meal with a diet coke coming in at 1500+ calories is a meme based in reality.


Could be as well, given there's many varieties of tokenisers, each with different pros and cons.

This particular tokenizer is very interesting given that it tries to be best of both worlds (word-level tokenizer and character-level).


It might be too high-level for some of you :) but if you ever wondered how they work, or wondered why these models are unlikely to replicate any specific image but are able to learn styles, or why there are "steps", or why there's a seed - then hopefully this post will help.


I'd suggest it really depends on the exact problem space you are working on.

Some NLP related problems can be solved at a similar level of quality of humans - (humans actually make a lot mistakes as well).


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