Might bother you to use anthropomorphic terminology like smart and learning but they are capable of producing work that traditionally required human intelligence and the whole point of gpt 3 was the ability to "learn", you can give it an example of an invented brand new coding language and it can write working code in that language

Intelligence can operate without learning. At a minimum inference and learning don’t need to be co-concurrent.

Not disagreeing with your point, but your terminology muddies your point.

But your point doesn't acknowledge that even with inference, there is a lot of room to tune the calculations. Multiple models, quantization tradeoffs are just the most obvious examples. Every architecture can be adjusted to increase intelligence/watt or other measure, even without further training.

You used the word "smart" now, whereas on the comment I replied to, you said "better".

Tuning those can definitely make a model respond better or worse.

So your claim (quoting 100% as written) that "Their performance depends solely on the model training before release and how well you curate the context you feed it" is wrong. Hence the downvotes.

Doesn't matter if LLMs are to be considered intelligent or not for the claim to be wrong.

> But judging from the downvotes, it seems AI folks get upset when someone talks honestly about their precious piles of matrix multiplication.

Often yes. In this case, it's more like they get upset when someone says something factually wrong, and then defensively changes the goalposts.

No thats probably because you misread what you were replying to and your comment was out of left field. They didnt imply models get better intra-releasally at all.

I can imagine an AI insulting humans in the same way:

"The underlying model is just a biological neutral network. It seems you carbonoids get upset when someone talks honestly about synapses and neuron firing."