Looking at an inkjet cartridge datasheet: apparently the ink is tested on animals, including guinea pigs, rats, rabbits, cats, dogs, mice, rats, and invertebrates. We think of these kinds of technologies as affecting humans only, but this is a surprising impact on animals as well.
The Mac OS is great for creative production, but for computers embedded in new media software installations, Linux gives a lot more flexibility and control, ranging from the ability to schedule automated tasks to the full-on tweaking of most every part of the operating system.
The price you pay for that control, of course, is in complexity. Setting up Linux machines can mean hours of spent in forums and lots of trial and error. This tutorial is meant to save some of those headaches. It explains how to set up a Linux computer for running software installations, and some settings that will make maintaining them easier, too.
Below are some things I’ve found helpful when setting up Linux computers for this purpose. Casey Reas has some great tips for OS X users, and Rafael Lazano-Hemmer’s best practices for new media artworks is also very useful.
We’ll cover two common Linux distributions, though this may work on other versions as well:
- Raspberry Pi computers running Raspbian – great for low-power needs, when a cheap, small computer will work
- A “regular” computer running Ubuntu – I often use old Mac Minis for this, for when you need more computing power or a more fully-featured OS
This tutorial assumes you know the basics of the command line (but really, if you don’t know how to do that, using Linux for your project might not be the best choice anyway) and that you can figure out how to install Linux on your machine.
Above: a dissected Mac Mini running Ubuntu. Less overheating = less fan noise, and looks super cool too.
Word2Vec is cool. So is tsne. But trying to figure out how to train a model and reduce the vector space can feel really, really complicated. While working on a sprint-residency at Bell Labs, Cambridge last fall, which has morphed into a project where live wind data blows a text through Word2Vec space, I wrote a set of Python scripts to make using these tools easier.
This tutorial is not meant to cover the ins-and-outs of how Word2Vec and tsne work, or about machine learning more generally. Instead, it walks you through the basics of how to train a model and reduce its vector space so you can move on and make cool stuff with it. (If you do make something awesome from this tutorial, please let me know!)
Above: a Word2Vec model trained on a large language dataset, showing the telltale swirls and blobs from the tsne reduction.
Weird blur/glitching when zoomed in really close on the cursor in Mac OS X.
A model of the penicillin molecule by Dorothy Hodgkin from 1945. Sculpture + data visualization + scientific work.
The amazing Seiko UC-2100 watch from 1984.