  Some claim language models understand us. Others won't hear it. To clarify, I
investigate three views of human language understanding: as-mapping,
as-reliability and as-representation. I argue that while behavioral reliability
is necessary for understanding, internal representations are sufficient; they
climb the right hill. I review state-of-the-art language and multi-modal
models: they are pragmatically challenged by under-specification of form. I
question the Scaling Paradigm: limits on resources may prohibit scaled-up
models from approaching understanding. Last, I describe how as-representation
advances a science of understanding. We need work which probes model internals,
adds more of human language, and measures what models can learn.
