Bubble animation showing the stages of a relationship, i.e., first met, romantic, living together, and married, over time with two halves showing the differences between the 1970s and 2010s. Key insight, living together is much more prevalent now.
Kieran Healy starts with a dichotomy, c.f. Section 1.2. There are two computer revolutions. One revolution is trying to abstract out the technology and present people with an easy, touch interface to accomplish specific tasks. Using your phone to take a picture, send a text message, post to social media, play YouTube videos, etc. are all examples of this type of technology. It’s probably the dominate form of computing now.
The other revolution are the complex computing tools that are being developed that cannot be used via a touch interface. At this point, there is no way to use an open source neural net like Google’s TensorFlow in a way that is going to make sense to the vast majority of people.
As we move to using a keyboard, this tension can be seen in the different types of tools we can use to write, research and do analysis. Microsoft Word, PowerPoint, Excel, Access, etc. were designed to be digital equivalents to their analog predecessors – the typewriter, the overhead projector, the double entry account book or the index file. Of course, the digital equivalents offered additional capabilities, but it was still tied to the model of the business office. The goal for these tools, even as they include PivotTables and other features, is to be relatively easy to learn and use for the average person in an business office.
The other computing revolution is bringing tools to the fore that are not tied to these old models of the business office and is combining them in interesting new ways. But, these tools have a difficult learning curve. For example, embedding programming code that can be written into a text analysis to generate calculations when it is typeset is not a feature the average person working in a typical office needs. But, it clearly has some advantages in some contexts, such as for data analysts.
Complexity makes mistakes easier to make. So, it requires a different way of working. We have to be careful to document the calculations we use, track versions from multiple sources, be able to fold changes back into a master document without introducing errors, and so forth. The Office model of handing a “master document” back and forth and the process bottlenecked waiting for individuals making revisions isn’t going to work past a certain minimum baseline level of complexity that we are slowly evolving past.
So, laying out this case, he then suggests various tools to consider: a text browser such as Emacs, Markup for formatting, git for version control, Pandoc for translating text documents into other formats, backup systems, a backup cloud service, etc. All of these tools are equally important to complex writing of any sort, whether it be for writing long works of fiction, research analysis, collaborative writing, and other circumstances we are more likely to find ourselves in, which these more powerful tools help make possible.