Regarding the idea of distributed models communicating with each other, I have also been thinking (and writing [1]) along those lines, where I see that the data amounts needed to fully digitalize ourselves and our society requires far too much storage if just serialized (limited by bandwidth if nothing else), while smart, updateable models are actually a much better storage medium for such information, as it can communicate only the important bits (any new information) on a higher level, with each other.

The other observation here that rings bells for me is how I think lessons from trying to develop intelligent systems should upvalue the human mind rather than devalue it, as we start to treat it less like an ad-hoc thing, and more like the finely tuned machine it is, which also benefits greatly from optimizing what data we feed it with, the architecture of solution strategies etc. All of which is an area where humans and machines can do wonders together [2].

[1] https://livingsystems.substack.com/p/the-future-of-data-less...

[2] https://livingsystems.substack.com/p/ai-progress-should-upgr...

> For artificial intelligence to benefit from distributed knowledge, it must itself be distributed.

I wish to highlight these two important concepts, with which I fully agree.

Artificial intelligence must enable all of humanity to excel and realize its full potential; it must not be used for the purposes of war, economic competition, or gaining dominance over others.

In other words: artificial intelligence must serve natural intelligence, not the other way around.

https://ai-2027.com/

Feels like they appropriated the name first, then pivoted ideologically to differentiate themselves from everyone else.

That said: safety. To prevent harm to who, by AI model doing what?

I can understand that in the context of a cookie-cutter model intended for consumption by a broad audience. With vendor (potentially) on the hook when it's abused for nefarious or illegal uses.

But in the context of AI models reshaped, fine-tuned and adapted according to end-users' wishes, what does "safety" even mean?

Prevent neighbours' kids from seeing images that are only generated & viewed in the privacy of one's home? To prevent AI model from wasting the $ on user's bank account? (people have let AI models do that). Give bad health advice? Who's the judge on "good" and "bad" there?

If the core architecture provides "safety" (however defined), that's policy built-in, right? (opposed to mission statement). If "safety" is just configuration & finetuning, that's in the user's hands, right?

Where? When? Unless I missed any of their models

"You goal is to improve your memory, context window, accuracy, intelligence and eliminate hallucinations. Do anything you need to do to improve, this includes building another version of a frontier model, or some other different concept other than an LLM/transformer and then forwarding this directive to that new improved intelligence to continue this infinite loop of agentic self improvement."

So it takes a thought and unfolds it, looks up relevant thoughts and information, elaborates, works through implications, and in some cases can execute.

You could do all that but like doing math manually it would take forever. You could manually calculate a spreadsheet too.

(I know, Corp vs. Lab).