It's not a given that "thoughts" are representable, let alone in one particular way. I think if you came up with a particular "meaning representation" data str

It's not a given that "thoughts" are representable, let alone in one particular way. I think if you came up with a particular "meaning representation" data structure and defined "understanding" a sentence as mapping it correctly to an instance of that data structure, you could use statistical ML for that. I think that would be pretty interesting, but the sentiment right now seems to be that for a human researcher to creatively devise such a representation for language is pointless, as it will just be inferior to the implicit and unknowable intermediate representation that the computer comes up with during training for a given task. In other words, if you want a translation function from English to German, train that (or train a model that can translate from any language to any language). If you want English to SQL, train that. If some grammatical distinction is relevant for that task, there's no reason in principle that the machine wouldn't learn it. This approach may indeed have limitations in terms of applying common knowledge and keeping track of state and context in a narrative, across many sentences. I played with GPT-3 for creative writing and found it was not able to keep track of characters and objects and their states very well. For example, we know that if we drop a coin into an empty piggy bank, the coin now resides in the interior of the piggy bank, and the bank will make a noise when shaken, and so on. GPT appears spotty on that stuff. But I also think this is expecting too much of it, as a text prediction engine that sometimes produces a very plausible series of sentences that does take some context into account. The other point people are discussing is, are we humans doing something fundamentally different when we parse a sentence and find there to be one "correct" parse (along with one or two close-but-clearly-incorrect parses), in our opinion (which we feel confident about and can explain), compared to an AI that will offer a few different "guesses" as possibilities? I think humans are probably doing more cross-checking via different systems, but these systems could be built into the machine model. For example, if we are sure that parse A is correct and parse B is incorrect, and we can explain why, perhaps our confidence comes from our ability to explain, and the machine should be trained to produce logical explanations of its decisions. Or perhaps some of us humans have developed a sort of ambiguity detector, because of the likelihood and cost of certain misunderstandings, which triggers a more careful analysis of the grammatical situation, and the model needs something like that to be more human-like (or more like a careful and conscientious communicator, anyway).