Real-time fine tuning would be one approach that probably helps with some things (improving performance at a task based on feedback) but is probably not well suited for others (remembering analogous situations, setting goals; it's not really clear how one fine-tunes a context window into persistence in an LLM). There's also the concern that right now we seem to need many, many more examples in training data than humans get for the machine to get passably good at similar tasks.

I would also say that I believe that long-term goal oriented behavior isn't something that's well represented in the training data. We have stories about it, sometimes, but there's a need to map self-state to these stories to learn anything about what we should do next from them.

I feel like LLMs are much smarter than we are in thinking "per symbol", but we have facilities for iteration and metacognition and saving state that let us have an advantage. I think that we need to find clever, minimal ways to build these "looping" contexts.

> I most probably am anthropomorphizing completely wrong. But point is humans may not be any more creative than an LLM, just that we have better computation and inputs.

I think creativity is made of 2 parts - generating novel ideas, and filtering bad ideas. For the second part we need good feedback. Humans and LLMs are just as good at novel ideation, but humans have the advantage on feedback. We have a body, access to the real world, access to other humans and plenty of tools.

This is not something an android robot couldn't eventually have, and on top of that AIs got the advantage of learning from massive data. They surpass humans when they can leverage it - see AlphaFold, for example.

Are there theoretical models that use real time weights? Every intro to deep learning focuses on stochastic gradient descent for neural network weights; as a layperson I'm curious about what online algorithms would be like instead.