Agree with Sébastien's stance that understanding isn't just zero or one. I'd argue current LLMs do already to some extent satisfy Emily's definition of "underst

Agree with Sébastien's stance that understanding isn't just zero or one. I'd argue current LLMs do already to some extent satisfy Emily's definition of "understanding" by mapping from language to an internal semantic space - just not necessarily as well as humans. Broadly they seemed to be of the views that: * (Sébastien) Pragmatically what we care about is the model's functionality/behavior. If for instance it's capable of solving unseen math puzzles, whether we say it "truly understood" the puzzle is mostly immaterial * (Emily) Whether or not it understands is important because it will determine what tasks it's capable of. If shown a parrot and a foreign-language word for parrot, a human could pick up from context that the word refers to a parrot but model could not due to lack of understanding I normally lean towards the former view because I feel "understanding" is a fairly nebulous term. Claims that "machines can never do X because X requires intelligence/understanding/..." tend to just move onto the next X once machines accomplish that X, opposed to actually being convinced that machines now have understanding, effectively decoupling whether it's said to have "understanding" from any practical limitations in its behavior. I do think the latter view makes sense if you hold "understanding" as something fixed/specific, and are at some point willing to say "this system can do X, so has understanding" to the tasks you said required understanding. Emily did for instance seem willing to say home assistants have understanding.