In this vein, have you read about the work done by people mostly from the AI lab at the Vrije Universiteit in Brussels (Belgium)? (They're also affiliated with the Sony CSL in Paris: http://csl.sony.fr/language.php) They're precisely interested in the philosophical problem of how a grounded language emerges and is perpetuated among a population of embodied agents, as opposed to the engineering problem of, say, understanding complex, context-dependent natural language queries.
There's a great book which gives an overview of this field, The Talking Heads Experiment: Origins of Words and Meanings by Luc Steels, which discusses many of the advances made in this field (including for instance how having a grammar, as opposed to just stringing words related to what you want to say at random, is an evolutionary advantage because it boosts communicative success). It's published as open access, so go grab your free copy! :)
Chapter 4 in particular has a very interesting discussion of what's problematic with the machine learning approach -- that it takes a lot of training examples for a classifier to start making interesting decisions -- and presents a selectionist alternative to that, where distinctions (as in e.g. nodes in decision trees) are grown randomly and they're reinforced / pruned based on feedback. Crucially, the categories (semantic distinctions) are not labels given at the outset, but they emerge along with the language, based on the environment the agents encounter and the tasks they're using language for.
In general, I'd recommend Chapters 1 and 2 for a quick introduction, but in a pinch, I attempted to give a 50,000-foot summary in an essay I wrote (look under the heading Evolutionary Linguistics):
I realize that engineering applications of these ideas might be a long way off (and perhaps they'll never materialize), but boy are these exciting discoveries about the very fabric of language :)
There's a great book which gives an overview of this field, The Talking Heads Experiment: Origins of Words and Meanings by Luc Steels, which discusses many of the advances made in this field (including for instance how having a grammar, as opposed to just stringing words related to what you want to say at random, is an evolutionary advantage because it boosts communicative success). It's published as open access, so go grab your free copy! :)
http://langsci-press.org/catalog/book/49
Chapter 4 in particular has a very interesting discussion of what's problematic with the machine learning approach -- that it takes a lot of training examples for a classifier to start making interesting decisions -- and presents a selectionist alternative to that, where distinctions (as in e.g. nodes in decision trees) are grown randomly and they're reinforced / pruned based on feedback. Crucially, the categories (semantic distinctions) are not labels given at the outset, but they emerge along with the language, based on the environment the agents encounter and the tasks they're using language for.
In general, I'd recommend Chapters 1 and 2 for a quick introduction, but in a pinch, I attempted to give a 50,000-foot summary in an essay I wrote (look under the heading Evolutionary Linguistics):
http://dlukes.github.io/cathedral-and-bazaar.html
I realize that engineering applications of these ideas might be a long way off (and perhaps they'll never materialize), but boy are these exciting discoveries about the very fabric of language :)