Machine learning won't solve natural language understanding
Going through this, it has the old &quot;A full understanding of an utterance or a question requires understanding the one and only one thought that a speaker is trying to convey. &quot; claim, which continues to not make any sense, because obviously people don&#x27;t do that; as much as I would like to be understood in precisely the way I mean, down to the most subtle nuance&#x2F;shade of meaning and connotation, at least much of the time, this is not something we can actually get across in an at all reasonable amount of time.<p>Also, claiming that natural language is infinite, if taken literally, would imply a large and contrary to the common consensus claim about physics, contradicting the Bekenstein bound and all that.<p>But one thing which seemed, at least initially, like a point that could have some merit, was the point about compression vs decompression.<p>But the alleged syllogism about it, is, pretending to be much more formal&#x2F;rigorous than it is, and is also kind of nonsense? Or, like, it conflates &quot;NLU is about decompression&quot; with, &quot;NLU \equiv not COMP&quot; which I assume is meant to mean, --
well, actually, I&#x27;m not sure what it is supposed to mean. Initially I thought it was supposed to mean &quot;NLU is nonequivalent to compression&quot;, but if so, it should be written as like, &quot;NLU \not\equiv COMP&quot; (where \not\equiv is the struckthrough version of the \equiv symbol) , but if it is supposed to mean &quot;NLU is equivalent to the inverse or opposite of compression&quot; (which I suppose better fits the text description on the right better), then I don&#x27;t think &quot;not&quot; is the appropriate way to express that.
And, if by &quot;not&quot; the author really means &quot;the inverse of&quot;, then, well, there&#x27;s nothing wrong with something being equivalent to its own inverse!
Nor, does something being equivalent to the inverse of something else imply that it is &quot;incompatible&quot; with it.<p>For something talking about communicating ideas and these ideas being understood precisely by the recipient of the message, the author sure did not work to communicate precisely.<p>The value in formalization comes not in its trappings, but in actually being careful and precise, etc., not merely pretending to be.<p>The part on intensional equality vs extensional equality was interesting, but the claim that neural networks cannot represent intension is, afaict, not given any justification (other than just &quot;because they are numeric&quot;).