[famouswaffles]

The most useful definitions have predictive power.

When you say upsetting things to bing chat, you'll find the conversation prematurely end.

You can cry all you want about how bing isn't really upset. How it doesn't really have intention to end the chat but those are evidently useless defitions because the chat did end.

A definition that treats Bing as an intentful system is more accurate to what happens in reality.

[version_five]

That might be useful in helping a child learn to use it, it has no value when actually studying neural networks. You could equally pretend the sun sets every night because it's upset from shining all day.

[famouswaffles]

>That might be useful in helping a child learn to use it

It is useful for anyone looking to use such systems. A LLM piloted robot could potentially pick up a knife and stab you because you obstructed some goal or said mean words and pretending it didn't have intent to do so won't bring you back to life. Acting like it does could help avoid such a scenario.

>You could equally pretend the sun sets every night because it's upset from shining all day.

No you couldn't.

The conversation ended prematurely because of your input. There is zero ambiguity on the relation.

But because you ascribe intent to bing, you can predict (accurately) that saying nice things will not end the conversation.

LLMs act like they have intent. This is a matter of conceding they do or not. Conceding so is more useful because it has more accurate predictive power than the alternative. This becomes plain when LLMs are allowed more actions than just conversation.

>it has no value when actually studying neural networks.

On the contrary. Now you know that certain things are unnecessary to build a system that acts like it has intent.

[lawlessone]

>You could equally pretend the sun sets every night because it's upset from shining all day.

That would describe quite a lot of ancient religious beliefs about the Sun