If LLMs just regurgitate compressed text, they'd fail on any novel problem not in their training data. Yet, they routinely solve them, which means whatever's happening between input and output is more than retrieval, and calling it "not understanding" requires you to define understanding in a way that conveniently excludes everything except biological brains.

We know that, but that does not make them unuseful. The opposite in fact, they are extremely useful in the hands of non-idiots.We just happen to have a oversupply of idiots at the moment, which AI is here to eradicate. /Sort of satire.

So you are saying they are like copy, LLMs will copy some training data back to you? Why do we spend so much money training and running them if they "just regurgitate text compressed in their memory based on probability"? billions of dollars to build a lossy grep.

I think you are confused about LLMs - they take in context, and that context makes them generate new things, for existing things we have cp. By your logic pianos can't be creative instruments because they just produce the same 88 notes.

I have a gut feeling, huge portion of deficiencies we note with AI is just reflection of the training data. For instance, wiki/reddit/etc internet is just a soup of human description of the world model, not the actual world model itself. There are gaps or holes in the knowledge because codified summary of world is what is remarkable to us humans, not a 100% faithful, comprehensive description of the world. What is obvious to us humans with lived real world experience often does not make it into the training data. A simple, demonstrable example is whether one should walk or drive to car wash.

Thats not how they work, pro-tip maybe don't comment until you have a good understanding?

Just HI slop. Ask any decent model, it can explain what's wrong this this description.