dlukes

Maybe it's just me, but "Oculus Rift" has always sounded like a Harry Potter spell to rip someone's eyeballs apart.

It's impressive how streamlined matplotlib plotting is and how well it works -- you just import matplotlib and start plotting, you can have multiple interactive plots alongside each other. Especially compared to Jupyter, where it's unfortunately sort of clunky (interactive plots first have to be enabled with %matplotlib notebook, and then you can only have one at a time, you have to manually "freeze" the previous one before you start a new one, or weird stuff -- overplotting -- happens).

To be clear, I love Jupyter, and I think it's especially great for teaching beginners (especially because it has "real" cells, as in GUI widgets, though I do get the appeal of a flat plain text format for advanced users). But plotting has been a bit of a stumbling block, whereas the way it works in Iodide blew me away.

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! :)

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 :)