Today’s rapid advances in algorithmic processes are creating and generating predictions through common applications, including speech recognition, natural language (text) generation, search engine prediction, social media personalization, and product recommendations. These algorithmic processes rapidly sort through streams of computational calculations and personal digital footprints to predict, make decisions, translate, and attempt to mimic human cognitive function as closely as possible. This is known as machine learning.

The project Recipe for Disaster was developed by exploring automation in technology, specifically through the use of machine learning and recurrent neural networks. These algorithmic models feed on large amounts of data as a source to continuously adapt and learn from and, in return, predict and produce their own data. Using a recurrent neural network (a subset of machine learning) and a data-set of over 800 internet-sourced food recipes, Recipe for Disaster is a video, photographic, and installation based exploration of five results of a computer’s own version of a recipe. The recipes are translated into how-to styled videos modeled after popular social media tropes and photographs of each resulting food dish. The food photographs appear as imagery commonly found in consumer culture, but as the disjointed results of the generated recipes. The videos, photographs, and installation are all displayed through variations of screens and screen-like components, deploying a bridge between the viewer and notions of digital media consumption.

Recipe for Disaster functions as a critique on the loss of human agency through the use of algorithmic models, while simultaneously recognizing food consumption as an intrinsic element of being human. In discussing how machine learning or predictive models have become more deeply integrated into the systems we use on a day-to-day basis, this project mimics information and media shared through and created by those systems. It is a response to the hidden complexities of systems and structures that question the effectiveness of predictions made by machines and how they might be affecting information and media literacy, visual semiotics, culture, and overall human behavior and development.



Document Type


Level of Degree


First Committee Member

Patrick Manning

Second Committee Member

Meggan Gould


computational thinking, art, conceptual art, recipes, food, algorithms, machine learning, recurrent neural networks, automation, technology, photography, artificial intelligence, generative art, data set, cookbook