Computer based analysis of the Semantics of language expressed as text is an AI level problem. Existing methods almost universally use Models of Language (Dictionaries, Grammars, Word Nets, Taxonomies, and Ontologies). The two simplest and most pervasive Models claim that Languages have Words and that those Words have Meanings. While acknowledging that good alternatives do not yet exist, this talk attempts to make plausible that these two "obvious" but fatally incorrect Models result, automatically, in a cascading series of forced engineering decisions that each discard a fraction of the available semantics until we end up with brittle systems that fail in catastrophic and memorable ways. The proposed alternative to word-centric Model Based methods of language analysis is Understanding Machines - capable of learning languages the way humans learn languages in babyhood - using new classes of algorithms based on Model Free Methods.

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