Haven’t read details, but the classic way is to have a system visit: site.com/badimage.gif?data=abcd

Note: That s is also how things like email open rates are tracked, and how marketers grab info using JavaScript to craft image URLs.

The memory feature of ChatGPT is basically like a human taking notes. Of course, the AI can also use other documents as reference. This technique is called RAG. -> https://en.wikipedia.org/wiki/Retrieval-augmented_generation

Sidenote. This isn’t the place to ask technical questions about AI. It’s like asking your friendly neighborhood evangelical about evolution.

Sort of, but not really.

In basic terms, if an LLM’s training data has:

Bob is 21 years old.

Bob is 32 years old.

Then when it tries to predict the next word after “Bob is”, it would pick 21 or 32 assuming somehow the weights were perfectly equal between the two (weight being based on how many times it occurred in training data around other words).

If the user has memories turned on, it’s sort of like providing additional training data. So if in previous prompts you said:

I am Bob.

I am 43 years old.

The system will parse that and use it with a higher weight, sort of like custom training the model. This is not exactly how it works, because training is much more in-depth, it’s more of a layer on top of the training, but hopefully gives you an idea.

The catch is it’s still not reliable, as the other words in your prompt may still lead the LLM to predict a word from it’s original training data. Tuning the weights is not a one-size fits all endeavor. What works for:

How old am I?

May not work for:

What age is Bob?

For instance.

No, basically. They would love to be able to do that, but it’s approximately impossible for the generative systems they’re using at the moment

It’s a very OpenAI

And extremely predictable