Algorithmic Images. Artificial Intelligence and Visual Culture

During the last few years, a series of deep learning algorithms and models (one of the areas of so-called “artificial intelligence”) have been profoundly transforming the way images are captured, generated, modified and seen. In this course, we will examine in particular three major phenomena, studying their manifestations in the fields of photography, cinema, contemporary art and visual culture at large:

a. machine vision systems, which make it possible to detect and recognize objects, places, bodies and faces among the billions of images accessible online, without these images necessarily having to be displayed on a screen, and therefore without them being visible to human eyes;

b. the first algorithms of “generative AI”, such as DeepDream and the Generative Adversarial Networks (GAN), and their use by artists such as Refik Anadol, Grégory Chatonsky, Pierre Huyghe, Trevor Paglen, and Hito Steyerl ;

c. the new text-to-image models (such as Stable Diffusion, DALL-E 2 and Midjourney) used to generate still images through textual prompts, but also the text-to-video models that use prompts to generate films and videos.

During the course, we will analyze the aesthetic, epistemological and political implications of these deep learning technologies which are revolutionizing the vast realm of digital images, impacting also the fields of design and architecture. Students will also be invited to test some of these models, and to discuss the results of their tests with the rest of the class.

The course aims at introducing the students to the transformations that artificial intelligence is producing in the fields of visual culture, photography, film, and contemporary art.

In presence

Lectures, class discussions, readings, testing

The students will be asked to write a paper on a topic of their choice that will have to be previously accepted by the teacher