I’ve always hated reading translations. It is really surprising that mainstream USians have pushed back on this for so long.
The ecosystem of built-ins and open source libraries around different programming languages can make solving certain problems trivial or nearly impossible. While we should be able to express the same ideas across any turing complete language, thinking of concurrency in Go or Erlang is much simpler than in C or Python. As Alvaro explains, this is not so much because of the language itself acts as a lens through which we see the world, but because each language (and its community!) packages different groups of ideas into self-contained tools and abstractions. When a Java programmer switches over to Lisp, they bring with them a bunch of ideas about how programming should be done, and while they are constrained by the framework of their new tool they also inject their own set of constructs into their new community by creating libraries that others can use and work on top of. In my opinion, the work you can do with a language depends much more on its ecosystem of bolted on tools than its basic syntax.
As part of my current Coursera class on Modernism and Postmodernism, I am currently reading Madame Bovary. Given my interest in languages and translation, I ended up doing some research of my own into which translation was worth reading, and it seemed like Lydia Davis’ was the recommended one. During my search, I found this piece by her on the process of translating it.
This was a recommendation from Jon Evans from a very long time ago. I finally got around to it, and it is as good as advertised. It discusses hegemony through language, how we assign authority to individuals and institutions via standardized language, and more. This is a 20k word essay about the dictionary, and how language shapes our thoughts. I enjoyed it so much that decided to buy DFW’s Consider The Lobster and read more of his essays. Reading it in perspective 15+ years after it was written, in our Orwellian political environment made it extra interesting.
By now most people who deal with machine learning or natural language processing in some way are familiar with the King - Man + Woman = Queen example from word2vec. Here, Schmidt brings up a similarly relatable example: What word is between duck and soup? What words sit between the middle point and those extremes? Iterating these chains brings up really cool patterns.
One of the amazing things of working at a large corporation like Apple is that a seeminlgy small or inconsequential task can end up affecting the way millions of people interact with the world. In this post, Guzman tells the story of her internship at Apple ten years ago, and how she and her mentor changed language forever. The way we communicate with each other is now permeated with their ideas, forever. Emoji are essential to language today. This is the story of the couple of people at Apple who made the first icons on our phones.