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#1
In NLP you convert complex data to numerical data - that is how machine learning approach works, which is a form of compute.
However, machines can also figure out by way of knowledge representation - this is semantic machine readable metadata.

The author assumes that the machine cannot semantically contextualize but can only compute. This is not sufficient for reading comprehension for which machines do need to understand semantically and contextualize. You cannot do this in machine learning. But, you can certainly build a knowledge graph to represent and replicate the associated memory of how humans build relationships and patterns between existing and new knowledge. This is how humans generalize over every day experiences, and assimilate new information through transfer learning. One must look at semantics as well as machine learning to process NLP. In this manner, machines can indeed figure out and compute which enables for artificial general intelligence. The mind processes information by means of perception, language semantics, and associative memory.

I disagree with the tip assumption for 'compute' and 'figure out' for machines on page 117. Machines can indeed compute and figure out.