Artificial intelligence sheds light on how the brain processes language

From my reading of where neuroscience is at the moment, it is a tenable hypothesis that the posterior neocortex (a major functional component of human cognition) is essentially a statistical inference machine.

There are a number of facts suggestive of this viewpoint:

For one, the apparent generality of the algorithms, resulting in a great deal of neuroplasticity and data-agnosticism. For example, you can wire visual input into auditory cortex and that auditory cortex will learn to see. [1]

For two, the similarity of Gabor filters learned automatically by statistical inference algorithms and the response properties of visual neurons. [2]

For three, the fantastic success, relative to other techniques at least, of such algorithms on natural language tasks such as machine translation.

Even its deficits are suggestive, such as the fact that the neocortex, like most statistical inference algorithms, can't easily do one-shot learning — for that you need a hippocampus.

Present-day machines obviously lack many abilities which humans have. Until we have a fully-functional AGI, it's hard to know for sure that there isn't a major missing piece which is nothing like the algorithms now under development. However, I'm not aware of any other algorithmic hypotheses of how the mind works which seem to so naturally fit the neuroscientific facts.

[1] Sur et al (1988). Experimentally Induced Visual Projections into Auditory Thalamus and Cortex. https://doi.org/10.1126/science.2462279

[2] Olshausen and Field (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. https://doi.org/10.1038/381607a0

What is the complex answer then? The parent's question is salient. Using your analogy as well, you could say humans see the world through the visual spectrum and reason about the world through that, although there is certainly much more to be observed beyond that spectrum - yet that is clearly sufficient for some subset of understanding.

I'm working on a side project right now where one of the applications is a platform to have AI engage in negotiation without a general purpose natural language. My project is an extension of the order writing system in Diplomacy into a domain specific language. I call it Dipspeak (like Newspeak from 1984). Dipspeak is a language for a universe where the only physical things are bits of plastic on a map of Europe and the only physical interactions between bits of plastic are hold, move, support and convoy.

This thesis implies language carries all of the meaning required to have an effect on the world.

To use a crude metaphor, language could be just an API schema. Is there content accessible via the schema that is more meaningful than the schema itself? For APIs, yes. I think so for language too.

Not to mention the possibility of side channels. That, at the same moment any human reads any text, they receive extra information to help them understand that information.