Technical writing is something that may get you someplace on this planet of literature. As an AI practitioner already well immersed within the literature, I had included the sector’s taste for technical formalization so thoroughly into my very own cognitive model that I literally couldn’t read the literatures of nontechnical fields at anything past a preferred stage.
This was very tough because my technical training had instilled in me two polar-reverse orientations to language – as precisely formalized and as impossibly vague – and a single clear mission for all discursive work – transforming vagueness into precision through formalization (Agre 1992).
There are seven rules to guide technical writing: bear in mind your objective (to inform or persuade), bear in mind your viewers (their considerations, background, angle towards your goal), make your content specific to its goal and viewers, write clearly and exactly (lively voice, applicable language to viewers), make good use of visuals (good web page design and graphics), and be ethical (truthful, full disclosure, no plagiarizing).
As we speak, DTU is ranked as one of many foremost technical universities in Europe, continues to set new information within the number of publications, and persistently increases and develops our partnerships with trade, and assignments accomplished by DTU’s public sector consultancy.
However the conceptual analysis and philosophical critique that accompany them must be understood as mental contributions in their very own right, grounded both in a priori evaluation of the phenomena and in detailed, critically knowledgeable reflection on the difficulties encountered in getting AI models to work.