guest edited by Patricia Pisters (University of Amsterdam) and Ruggero Eugeni (Università Cattolica del Sacro Cuore Milano)
The advent of new algorithms of machine learning and AI is producing a profound revolution in societies: indeed, the ‘algorithmic turn’ involves cultural, cognitive, emotional, and practical layers of everyday life; from this point of view, AI directly concern cinema and media at almost three levels.
On the first level, media have represented and represent within their own fictional worlds the different forms assumed by AI. Consider for instance works such as Steven Spielberg’s A.I. (2001), Spike Jonze’s Her (2013), Alex Garland’s Ex Machina (2015), Denis Villeneuve’s Blade Runner 2049 (2017), and various episodes of the television series Black Mirror (Charlie Brooker, 2011-present). These representations are sometimes at the origin of real technological innovations: for example, the Convolutional Neural Networks, used in visual recognition algorithms by Yann LeCun, were inspired by the character HAL 9000 in 2001: A Space Odyssey (Stanley Kubrick, 1968).
On the second level, the functioning of media and post-media is increasingly linked to AI algorithms: machine vision intervenes in the processing of audio and visual data, from capture to editing; digital assistants and home automation interfaces use speech and visual recognition algorithms; AI automates the distribution of audiovisual products on SVOD and TVOD platforms such as Netflix or Amazon Prime, and modify the criteria for visibility and recognition of audiovisual products; they regularly assist human subjects in creating images and videos, and recently some Generative Adversarial Network algorithms started creating artistic images to be used in art installations and successfully inserted in the art market. If film scripts written by AI is for the moment mainly a forecast, trailers produced by specialised software and editing programs assisted by AI such as Magisto are already a reality.
On the third level, the algorithmic turn and the advent of the new AI poses a series of philosophical problems which in turn push to reformulate some critical problems in the field of film and media theory: what does it mean to ‘create’ a text, and what relationship exists between the creative processes and the ‘automatisms’ of a medium? What does ‘understanding’ mean, and in particular what relationship must be considered between ‘vision’, ‘understanding’, and ‘emotional intelligence’ – emotions being the latest holy grail of AI. Should we consider the possibility of ‘non-biological’ or even ‘non-human’ forms of visual or aural understanding and feeling? Also, what relationship exists between human intelligence and ‘the intelligence of a machine’, quoting Jean Epstein? How are AI models connected to those of cognitive neuroscience in accounting for the experience of the spectator? Particularly, how to reconcile an embodied conception of the moving image viewer with the generally disembodied algorithms of vision and AI? And ultimately, what does AI (and mediated forms of AI) tell us about being human in a post-human society?
Contributions may concern one or more of these three levels. In particular, they can focus on:
# Ontological, ethical, and ideological problems implicated by AI as cultural forms in the field of film and media.
# Representations of AI in the history of television, cinema, videogames, in their relations with the actual scenarios of technological research.
# Uses of AI in machine and computer vision; in the reconstructions of three-dimensional environments; in virtual, extended, augmented, immersive, and mixed reality.
# Uses of AI in data capture, analysis, and visualisation, in fields such as medicine, defence, transport, surveillance (for example with face recognition algorithms).
# Voice User Interfaces, digital assistants, recognition devices in media and home automation appliances.
# AI applied to consumer suggestions in post-advertising and SVOD platforms.
# AI art products and the market.
# Uses of AI in the creative practices of prosumers, from shooting to automatic editing, including image correction and cases of ‘deep fakes’.
# Models of learning in connection with visual, cognitive, and emotional recognition/understanding implicated by AI, and their relationship with contemporary models of the spectator’s experience provided by cognitive and neurocognitive approaches.
# Transformations of textual analysis practices following the use of algorithms for the automated analysis of audio and video.
We look forward to receiving abstracts of 300 words, 3-5 bibliographic references, and a short biography of 100 words by 1 July 2019 to firstname.lastname@example.org. On the basis of selected abstracts, writers will be invited to submit full manuscripts (5,000-6,000 words, revised abstract, 4-5 keywords) by 1 February 2020, which will subsequently be peer reviewed.