Machine learning won't solve natural language understanding
The author seems hung up on the idea that because NLU involves apparent &quot;discontinuities&quot; -- places where small variations in interpretation completely transform tasks -- it will not be amenable to smooth, continuous notions like PAC learning, compressibility, and so on.  While there&#x27;s a directional insight there, the terms aren&#x27;t well defined.  And the big story of ML-based NLP in the last decade has been that many tasks that were presumed too jagged for curve fitting are in fact tractable, given large data sets and clever shifts of the way discontinuities can be modeled (e.g. attention-based techniques like Transformers).<p>Finally, since humans don&#x27;t perform perfectly at these contrived tasks either, we must ask acknowledge that there is some degree of &quot;approximate correctness&quot; that satisfies our ideas of intelligence.