Machine learning is taking computer intelligence to a whole new level. One of the tasks machine learning is thought to excel at is image classification. Nevertheless, cases in which machine vision fails are not uncommon. On one hand, instances of misclassification by machine learning indicate failure of a quantitative nature. On the other hand, they reveal human stereotypes emerging from a qualitative mindset.

In this project, titled Objects in Limbo, I investigate and dramatize quantitative aspects of machine learning. In highlighting and visualizing the moments when machine vision diverges from human vision, I strive to reveal the differences between human and machine perceptions and to put into perspective the capabilities and limitations of machine learning.

Objects in Limbo is a series of everyday objects that are manually deformed in an incremental and performative manner until they can no longer be correctly recognized by an algorithm. The human-machine dialogue throughout the process generates a poetic layer of meaning involving the epistemology of what we see and what machines see. Ultimately, the project serves as a commentary piece intended to assist the audience in making informed decisions whenever the use of machine learning is in question. See more here.