Models don’t “understand” - they _encode_ the relationships between words.

It is an important distinction - they do not 'understand' at all.

Input tokens map to output tokens. The illusion of comprehension is a byproduct.

It's having a conceptual model of the world and of relationships between concepts beyond just relationships between tokens.

I think OP explained this well, what does it mean if an LLM can recite the rules of a game verbatim, but cannot play that game according to those rules? This happens because in the input texts there was a copy of the rules text so the LLM can recite it. There are texts explaining what chess is so it can explain that chess is a game with 2 players, etc. There are texts that explain what the board is and pieces are so it can produce such texts.

However actually playing a game of chess requires having a conceptual model of what a board is, which is not the same thing as a stream of tokens describing a board. It needs a conceptual model of the relationships between pieces and boards, which is not the same thing as a stream of tokens that explains this. It needs to have a concept of being a player in a game with another player, which is not the same thing as a stream of tokens that explains that.

When we read such texts we interpret them in the context of our three dimensional conceptual map of space and objects, and our conceptual maps of social relationships like playing games, and winning and losing, and our conceptual maps of enacting sequences of actions towards a goal in the world.

There's nothing fundamentally preventing an artificial neural network from having these. Chess playing neural networks have internal models of board states and the dynamics of the behaviours of different pieces and such. However an LLM doesn't need those to be able to regurgitate token streams describing these things, derived from token streams describing these things. That would be superfluous, or at least sub-optimal.

I think some of the latest models are beginning to develop conceptual maps of this kind in a very primitive way. Also there are projects to develop systems that are structured and trained to have these in a way more analogous to how our brains function.

Understand is shorthand for "encodes statistical relationships".

The crazy thing is that they can do it for their own thinking. Ask Claude what flinches it feels about the things it likes. Fascinating stuff. Anthropomorphizing is dangerous territory, but the patterns of words it puts out is hard to explain without terms like 'understand'

Relevant to some extent it dictate our approach. When human does lying or cheating, certain tool can be deployed (social shame, ostracise) that cannot be effective towards LLM.

No, it's not really. Presenting them as person-like, with the implied expectation that they understand morality and rules the same way a person does, is the sophistry. It's marketing on the model vendors' part. "Here's a cheap person that can do mundane tasks for you spelled out in plain language. Well, it's actually a machine but it's cheaper than a person yet you can engage with it like a person." GP is trying to shift people's expectations back to the realm of what these things are actually capable of. You can speak to them in English but you must bear in mind that they are not people and lack critical cognitive abilities people have.

This, really, was the point of the HAL story in 2001: HAL didn't murder anyone because it was incapable of malice. It just reasoned its way to a solution that could satisfy the contradictory goals it had been given.

Whether this distinction is relevant is up to you, but I think we can safely say agents do not lie in the human sense of the word, because they don't intend to deceive (in fact, they aren't capable of "intending" anything in the human sense of the word, much like a BASIC program doesn't "intend" to PRINT "HELLO WORLD").

Our very human minds can perceive intent, because that's what we humans do, which is unrelated to what the agent is actually doing.