Daniel Chávez Heras

Cinema and Machine Vision unfolds the aesthetic, epistemic, and ideological dimensions of machine-seeing films and television using computers. With its critical-technical approach, this book presents to the reader key new problems that arise as AI becomes integral to visual culture. The book theorises machine vision through a selection of aesthetics, film theory, and applied machine learning research, dispelling widely held assumptions about computer systems designed to watch and make images on our behalf. At its heart, Cinema and Machine Vision is an invitation for film and media scholars to critically engage with AI at a technical level, a prompt for scientists and engineers working with images and cultural data to critically reflect on where their assumptions about vision come from, and a joint recognition of the fruitful problems of working together to understand the algorithmic governance of the visual.

In this article we introduce the concept of implied optical perspective in deep learning computer vision systems. Taking the BBC's experimental television programme “Made by Machine: When AI met the Archive” as a case study, we trace a conceptual and material link between the system used to automatically “watch” the television archive and a specific type of photographic practice. From a computational aesthetics perspective, we show how deep learning machine vision relies on photography, its technical regimes and epistemic advantages, and we propose a novel way to identify the latent camera through which the BBC archive was seen by machine.