Ultimately, the brain is just a bunch of neurons activating in a specific pattern. This observation does not really tell us anything though. It doesn't acknowledge the difference between a 2500 Neuron fruit fly brains and a human brain.
Likewise, the fact that LLMs are a stochastic autoregressive process (which is a class of systems every bit as rich as the ODEs used to model neurons) tells us nothing a priori.
Absolutely. If someone makes the weights do continuous learning etc then perhaps an llm can internalise morals. Of course, just like a human, it will be possible to talk it out of those morals. Another recent thread about this is https://news.ycombinator.com/item?id=49744420
If I repeatedly call an LLM in a loop with a markdown document it can edit, would that make it qualify for you?
If I give an LLM to compact its context window, so the context it carries can evolve iteratively over time as more and more things come in, is that enough?
Compacting the context is really a very, very interesting example here. The "next token predictor" is telling an external tool to change all "previous" tokens. So an LLM + a harness that allows compacting the context is no longer just a token predictor at all!
You don't need continuous learning to get interesting dynamics. You just need feedback loops.
One is an observation the other is not, it's a description of what it is; one is a posteriori, the other is a priori (contrary to what you say).
They're not comparable.