i-could-have-written-that* is a practice based project about text-based machine learning, to question its readerly nature and investigate how these techniques can be used as writing procedures.

Text-based machine learning (also called 'text mining') is a subgroup of natural language processing and machine learning that is specifically built to work with text. It applies statistical models to large bulks of text to perform all sorts of automated classification, text generation, question answering and search suggestions. Due to the large amount of text available on the internet, text-based machine learning has become an important tool for search engines, the advertisement industry, social media timelines and academic research. However, the promises of machine learning practices can be very confusing. Various companies use rhetorics to present their products like "the power to know", "the absolute truth", "with an accuracy that rivals and surpasses humans", which creates the misleading expectation that these techniques reveal an one-sided absolute and objective truth out of textual information. Beside these kind of statements, machine learning is surrounded by fuzzy sci-fi AI scenario's, the economical system of the cloud, invisibility, complexity, tech-oppertunism and ready-to-use API's.

The project is built with ie. nltk, Pattern, python, cgi, jinja, git.

i-could-have-written-that is a project by Manetta Berends. Subscribe to the mailinglist to receive updates and info about future workshops:

* origin