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Apparatuses were invented to simulate specific thought processes. Only now (following the invention of the computer), and as it were with hindsight, is it becoming clear what kind of thought processes we are dealing with in the case of all apparatuses. That is: thinking expressed in numbers. All apparatuses (not just computers) are calculating machines and in this sense 'artificial intelligences', the camera included, even if their inventors were not able to account for this. In all apparatuses (including the camera), thinking in numbers overrides linear, historical thinking. — Vilém Flusser, 1983. 1 Camera on: computational capitalism. In 1983 the philosopher Vilém Flusser defined the camera as " computational thinking flowing into hardware " , just by considering the laws of optics and mechanics that are necessary to build such a device and translate light forms in physical memory. 2 The camera thinks, any time the shutter shuts. This intuition became trivial as the gears of digital cameras, across the 1990s, started to encage photons in the concrete abstractions of ever faster Turing machines. As the media theorist Jonathan Beller explains, the calculus of the technical image maintains an organic relation with the calculus of the attention economy in the mass media society. 3 A logic evolution can be traced between the cinematic mode of production of Fordism and the computational assembly line of the digital age. The abstractions that are necessary to build technical, optical and cinematic artefacts continue in the ones that are found in the design of technical institutions, including financial ones. Flusser considered machines, though, blind and subhuman automata, while apparatuses (also institutional ones) would be technical games [Spielzeug] that " simulate thought " , as they are able to morph and adapt to a new situation. 4 From the outside, institutions appear to breath and think. The movie Esiod 2015 by Clemens von Wedemeyer is an exploration of financial institutions qua artificial intelligence in the age of cinema qua artificial intelligence. 5 Paraphrasing an old saying by Gilles Deleuze on cinema, we may add: " the computational brain is the screen ". 6
Recently there is much talk about the coming possibilities of " Artificial Intelligence " or " Robotics ". But these technological developments are embedded in a media culture. The recent data surge is at once the precondition for machine learning as is the analysis of this data one of the goals of AI-research. The economic and ideological forms of society shape the media culture of AI. In this essay, we try to sketch these implications for understanding AI as a central topic of recent digital media culture.
In: Ippoliti, E., and Chen, P. (eds.), Methods and Finance, pp. 169-178, Springer
Quantification Machines and Artificial Agents in Global Finance: Historical-Phenomenological Perspectives from Philosophy and Sociology of Technology and Money2017 •
La Deleuziana
The Digital Image of Thought2022 •
This essay conceives a Deleuzian image of thought proper to the digital medium. I call this "the digital image of thought" (or "the digital image" for short). I begin with an introduction to Gilles Deleuze and Félix Guattari's work on the image of thought and the related concept of the plane of immanence. Here, I emphasize that images of thought not only incarnate in entities recognized as either intelligent (e.g., a human mind) or as the products of intelligence (e.g., a work of art), but in material objects and media. The digital image, I argue, does not precede thought, but pertains to the latter category, as it is realized in the medium of digital data. Taking a speculative approach, I consider its implications were it to indeed inform human thought, arguing that in this capacity it would structure thought to meet the technical criteria of digital computability. From there, I present the digital image as a mechanism which would subsume thought under the needs of capital. Insofar as it yields digitizable (and thus monetizable) mental figures, I claim, it would figure minds as ideal environments for the creation of commodifiable concepts. I conclude by suggesting that, although the digital image is not an actually-existing precursor to thought, this possibility is an ideal of digital capitalism. For this reason, Deleuze and Guattari's attempts to conceptualize a radically novel type of thought-one which would defy digital capture-are allied with contemporary political theorizations of the relationship between information technology and the psyche.
NECSUS European Journal of Media Studies
The artificial intelligence of a machine: Moving images in the age of algorithms (with Patricia Pisters)2020 •
Abstract The article introduces the NECSUS Spring 2020 Special Section #Intelligence (https://necsus-ejms.org/portfolio/spring-2020_intelligence/#toggle-id-2), that includes seven essays addressing the impact of Artificial Intelligence on cinema and media from a cultural perspective. More particularly, three levels of pertinence are focused. At a first level, selected papers analyse several representations of non-human intelligence confronted with human one, as provided by film, television series, and video games. At a second level, a set of mutual functioning dynamic between A.I. and the media are identified and scrutinised. Finally, the contributing authors consider how A.I. algorithms lead cinema and media theory to deeply rethink its assumptions about creating and viewing moving images.
The Dark Precursor: Deleuze and Artistic Research
Digital Folds, or Cinema's Automated BrainThe meaning of AI has undergone drastic changes during the last 60 years of AI discourse(s). What we talk about when saying “AI” is not what it meant in 1958, when John McCarthy, Marvin Minsky and their colleagues started using the term. Take game design as an example: When the Unreal game engine introduced "AI" in 1999, they were mainly talking about pathfinding. For Epic Megagames, the producers of Unreal, an AI was just a bot or monster whose pathfinding capabilities had been programmed in a few lines of code to escape an enemy. This is not "intelligence" in the Minskyan understanding of the word (and even less what Alan Turing had in mind when he designed the Turing test). There are also attempts to differentiate between AI, classical AI and "Computational Intelligence" (Al-Jobouri 2017). The latter is labelled CI and is used to describe processes such as player affective modelling, co-evolution, automatically generated procedural environments, etc. Artificial intelligence research has been commonly conceptualised as an attempt to reduce the complexity of human thinking. (cf. Varela 1988: 359-75) The idea was to map the human brain onto a machine for symbol manipulation – the computer. (Minsky 1952; Simon 1996; Hayles 1999) Already in the early days of what we now call “AI research” McCulloch and Pitts commented on human intelligence and proposed in 1943 that the networking of neurons could be used for pattern recognition purposes (McCulloch/Pitts 1943). Trying to implement cerebral processes on digital computers was the method of choice for the pioneers of artificial intelligence research. The “New AI” is no longer concerned with the needs to observe the congruencies or limitations of being compatible with the biological nature of human intelligence: “Old AI crucially depended on the functionalist assumption that intelligent systems, brains or computers, carry out some Turing-equivalent serial symbol processing, and that the symbols processed are a representation of the field of action of that system.” (Pickering 1993, 126) The ecological approach of the New AI has its greatest impact by showing how it is possible “to learn to recognize objects and events without having any formal representation of them stored within the system.” (ibid, 127) The New Artificial Intelligence movement has abandoned the cognitivist perspective and now instead relies on the premise that intelligent behaviour should be analysed using synthetically produced equipment and control architectures (cf. Munakata 2008). Kate Crawford (Microsoft Research) has recently warned against the impact that current AI research might have, in a noteworthy lecture titled: AI and the Rise of Fascism. Crawford analysed the risks and potential of AI research and asked for a critical approach in regard to new forms of data-driven governmentality: “Just as we are reaching a crucial inflection point in the deployment of AI into everyday life, we are seeing the rise of white nationalism and right-wing authoritarianism in Europe, the US and beyond. How do we protect our communities – and particularly already vulnerable and marginalized groups – from the potential uses of these systems for surveillance, harassment, detainment or deportation?” (Crawford 2017) Following Crawford’s critical assessment, this issue of the Digital Culture & Society journal deals with the impact of AI in knowledge areas such as computational technology, social sciences, philosophy, game studies and the humanities in general. Subdisciplines of traditional computer sciences, in particular Artificial Intelligence, Neuroinformatics, Evolutionary Computation, Robotics and Computer Vision once more gain attention. Biological information processing is firmly embedded in commercial applications like the intelligent personal Google Assistant, Facebook’s facial recognition algorithm, Deep Face, Amazon’s device Alexa or Apple’s software feature Siri (a speech interpretation and recognition interface) to mention just a few. In 2016 Google, Facebook, Amazon, IBM and Microsoft founded what they call a Partnership on AI. (Hern 2016) This indicates a move from academic research institutions to company research clusters. We are in this context interested in receiving contributions on the aspects of the history of institutional and private research in AI. We would like to invite articles that observe the history of the notion of “artificial intelligence” and articles that point out how specific academic and commercial fields (e.g. game design, aviation industry, transport industry etc.) interpret and use the notion of AI. Against this background, the special issue Rethinking AI will explore and reflect the hype of neuroinformatics in AI discourses and the potential and limits of critique in the age of computational intelligence. (Johnston 2008; Hayles 2014, 199-210) We are inviting contributions that deal with the history, theory and the aesthetics of contemporary neuroscience and the recent trends of artificial intelligence. (cf. Halpern 2014, 62ff) Digital societies increasingly depend on smart learning environments that are technologically inscribed. We ask for the role and value of open processes in learning environments and we welcome contributions that acknowledge the regime of production as promoted by recent developments in AI. We particularly welcome contributions that are historical and comparative or critically reflective about the biological impact on social processes, individual behaviour and technical infrastructure in a post-digital and post-human environment? What are the social, cultural and ethical issues, when artificial neuronal networks take hold in digital cultures? What is the impact on digital culture and society, when multi-agent systems are equipped with license to act?
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