All #learning must be open-ended. The learning agent (the #observer) must have the #autonomy to set its own learning goals as well as plan and execute a #sequence of #exploration activities to achieve these goals.
One can never learn *all existing data* but rather refine their understanding of the data that is available to them. As true for human intelligence, you can either have "deep and narrow" specialized #AI agents or "average and broad" #AGI. You can't have both in the same entity. Time and #memory "limitation" are the main inspirations for #diversity and #cooperation between learning agents.
People should have figured it out by now that the #distribution of processing power, not the #centralization in gargantuan data and control centers is the right thing to do.
Stop working on LLMs (Large Language Models) and start working on PCAs (Personal Customizable Assistants).
Seems right to me. 
The centralised expert models are not in anyones long term interests. Each institution becomes increasing unhelpful, instead of liberating and augmenting.
