Technophany, A Journal for Philosophy and Technology

This article examines the concept of symbiosis as a premise for elucidating the origin of the human-technology relationship. The starting point is the work of the biologist Lynn Margulis, who introduced the concepts symbiosis and symbiogenesis in the biological
sciences. Her idea is that a long-lasting physical association that as symbiosis may be defined, will eventually by symbiogenesis lead to an evolutionary novelty. From this perspective the human-technology relationship is explained using philosophical ideas of Bernard Stiegler and Helmuth Plessner, who both considered this relationship essential for being human. I explain what is typical about the human life form as it is thought by them. Basically, the difference between the human and other organisms is that in the human, something is moved outside that in animals stayed within. I explicate that this exteriorisation, as it is called by Stiegler, at the same time is an interiorisation. This movement should be considered as a form of endosymbiogenesis by which the long- lasting use of tools was cognitively internalized in mind and body and became eventually a condition for the origin of an organism with a technological culture—the human.

Paul B. Preciado’s theory of the pharmacopornographic regime provides a radical theoretical analysis of the relationship between gender, technology and capitalism. Firstly, I explicate Preciado’s key concepts and argue that their overarching theoretical project illuminates neoliberal capitalism’s capture and commodification of sexual energies and desire. I contend that contemporary toxic heteronormativity in extreme online communities may be explained as reactionary internalisation/ resistance to this process. I conclude by suggesting Preciado’s theoretical insights gesture toward a progressive and emancipatory pathway for rethinking masculinity.

How do we conceive diversification in the milieu of technics? Though associated-milieu is a useful concept, the Indian terrain of techniques impels one to underscore the notion as a milieu of subversion. The paper begins with a reading of the seminal work done by Debiprasad Chattopadhaya analyzing the impediments in writing a history of ancient Indian science and technology. Whether his effort resuscitating a history of materialism succeeds in interpolating a history of techniques in India is a critique this paper explores, taking cues from Yuk Hui’s incisive reading of the perceived absence of traditions of technical thinking in China, or broadly Asia. Present paper builds on Hui’s concept of “cosmotechnics” alongside the notion of “associated milieus” of counter-cultural techniques of Yoga and Tantra. In the second half, the paper brings into view how these Indian thought and practice traditions of Tantra and Yoga as techniques of the cosmic self and the body are woven into counter-cultures eliciting a critical framework for thinking these associated-milieus as exiled and migrated milieus.

In this article, I use the dialogical ideas of Hans-Georg Gadamer to evaluate whether generative AI is ready to join the ontological conversation that he considers humanity to be. Despite the technical advances of generative AI, Gadamer’s philosophical hermeneutics reveals that it cannot function as a proxy human dialogue partner in pursuit of understanding. Even when free from anthropomorphic projections and reimagined as the “other”, generative AI is found to have a weak epistemology, lack of moral awareness, and no emotions. Even so, it evokes a response in some users that places it on the threshold of being. The most promising dialogical role identified for generative AI is as a digital form of Gadamerian “text” currently constrained by copyright and technical design. Generative AI’s shortcomings risk inhibiting hermeneutical understanding through greater access to summarised knowledge. Nonetheless, the new technology is on the brink of joining the ontological conversation of humanity.

The following paper attempts to articulate a distinctly materialist notion of emergence and the formation of patterns by way of re-visiting two texts that have been considered oddities, if not embarrassments, by the subsequent developments of their respective disciplines: Freud’s Project for a Scientific Psychology and Engels’s Dialectic of Nature. Both texts are strikingly similar in their speculative engagement with the natural sciences and in their potential to inform a renewed engagement with the question of the relation between technology and life. In the concept of “path-breaking” [Bahnung] Freud understands perceptions as inscribing themselves in the structure of the very perceiving apparatus through repetition of what one could call a “material trace” (Sybille Krämer). This notion of the “material trace” can be connected to the key thrust of Engels’s “objective dialectics” in that it “concerns a model of structural emergence”(Hartmut Winkler). I want to propose that these texts can potentially enrich our understanding of how mental formations such as memory take shape and how subjectivity is constituted in material processes. That is, once Freud and Engels are read through recent philosophical thinking on technology (Bernard Stiegler, Catherine Malabou) and the concept of recursivity (Yuk Hui). This approach can also supply resources for a Marxist notion of ideology—namely by performing a turn from a critique that is primarily concerned with the question of how we can penetrate false appearances towards a materialist account of how (“false”) appearances, something like “real abstractions” (Alfred Sohn-Rethel), can emerge out of the “flat plane” of matter.

Book review of Cybernetics and the Origin of Information by Raymond Ruyer, Translated by Amélie Berger-Soraruff, Andrew Iliadis, Daniel W. Smith, and Ashley Woodward.

Review for:

Laura Tripaldi, Parallel Minds: Discovering the Intelligence of Materials. Falmouth, UK: Urbanomic, 2022. 192 pp. $18.95 (Paperback ISBN: 9781913029937)

The implementation of generative models in deep learning, particularly those of Text-to-Image Synthesis (T2I), are essentially an exaptation of the cognitive processes of the transcendental imagination Kant outlined in his notoriously opaque schematism chapter of CPR. While such engineering feats mirror the liberating force of photography’s invention, they have also proven to be a significant engine for reproducing antediluvian ideologies of art pivoting on claims about what has been stolen by the machine. This paper argues that T2I presents an opportunity to instead reconsider what our models of the procedures of the imagination actually are or could be, and wagers that the interdisciplinary conceptual frameworks supporting machine learning enable us to recuperate from an “incommensurable” synthetic intelligence the necessary resources for revising our understanding of what creativity is and does, with pattern recognition providing the tools for a renewed elaboration of techné to pull a heist upon the transcendental itself.

This essay analyses the interplay of indeterminacy and determinacy in the experience of images generated through text-to-image (T2I) models. Through an interdisciplinary approach, it uncovers three layers of indeterminacy: the computational indeterminacy inherent in text-to-image model processes, the indeterminacy of imagination in Husserl’s concept of protean phantasy, and finally the visual indeterminacy that figures in meaning making in all images. Generated images pass through these stages of indeterminacy, transforming indeterminate phantasy into determined visual objects, resulting in a conflict of consciousness between potential and actual. A distinction emerges between artificial phantasy, characterized by quasi-experience, and artificial imagination, grounded in images both as training data and perceptual image objects. As mediators between indeterminacy and determination, T2I images appear as technical media that mediate multiple forms of indeterminacy, showing the circulation between phantasy and imagination, between continuous and discrete. The generated image marks the limit of the unlimited indeterminate imagination.

With the understanding that art and technology continue to experience a (rapidly escalating) historical rapprochement, but also with the understanding that our comprehension of art and technology has tended to be constrained by scientific rigour and calculative thinking by one side, or have tended to change to the extreme from the lyrical: the objective of this article is to provide a reflective look for artists, humanists, scientists and engineers to consider these developments from the broader perspective it deserves, while maintaining a focus on what should be the emerging core of this topic which is the relationship between art, technology and science: the state of the art in mechatronics and computing today is such that we can now begin to speak comfortably of the machine as artist, and we can begin to hope, too, that an aesthetic sensibility on the part of the machine might help generate an intelligent more friendly and responsive machine agency overall. The principle of the inhuman emphasises that the questions of ontology are not questions of being as subject, of being as consciousness, of being as Dasein, of being as body, of being as language, of being as human or of being as power, but of being as being. Finally, the ontological principle hypotheses that all beings are ontologically on an equal footing or that all are to the extent that they make a difference. However, until now not much has been said about “algorithmic entities”. From the above, it is clear that there are still many unanswered questions, for example: How to raise the question of techno-diversity when intellectuals yearn for a general artificial intelligence? We must go back to history to orient ourselves in our current situation with a sense of distance. Will it be possible to find strategies to free ourselves from this apocalyptic end of technological singularity and reopen the question of the creative future in machines in relation to humans?

The introduction of automated algorithmic processes (e.g. machine learning) in creative disciplines such as architecture and urban design has expanded the design space available for creativity and speculation. Contrary to previous algorithmic processes, machine learning models must be trained before they are deployed. The two processes (training and deployment) are separate and, crucially for this paper, the outcome of the training process is not a spatial object directly implementable but rather code. This marks a novelty in the history of the spatial design techniques which has been characterised by design instruments with stable properties determining the bounds of their implementation. Machine Learning models, on the other hand, are design instruments resulting from the training they undertake. In short, training a machine learning model has become an act of design.

Beside spatial representation traditionally comprising of drawings, physical or CAD models, Machine Learning introduces an additional representational space: the vast, abstract, stochastic, multi-dimensional space of data, and their statistical correlations. This latter domain – broadly referred to as latent space –  has received little attention by architects both in terms of conceptualising its technical organisation and speculating on its impact on design. However, the statistical operations structuring data in latent space offer glimpses of new types of spatial representations that challenge the existing creative processes in architectural and urban design. Such spatial representation can include non-human actors, give agency to a range of concerns that are normally excluded from urban design, expand the scales and temporalities amenable to design manipulation, and offer an abstract representation of spatial features based on statistical correlations rather than spatial proximity. The combined effect of these novelties that can elicit new types of organisation, both formally and programmatically. In order to foreground their potential, the paper will discuss the impact of ml models in conjunction with larger historical and theoretical questions underpinning spatial design. In so doing, the aim is not to abdicate a specificity of urban design and uncritically absorb computational technologies; rather, the creative process in design will provide a filter through which critically evaluate machine learning techniques.

The paper tasks to conceptualise the potential of latent space design by framing it through the figure of the paradigm. Paradigms are defined by Thomas Kuhn as special members of a set which they both give rise to and make intelligible. Their ability to relate parts to parts not only resonates with the technical operations of ml models, but they also provide a conceptual space for designers to speculate different spatial organisation aided by algorithmic processes.  Paradigms are not only helpful to conceptualise the use of ml models in urban design, they also suggest an approach to design that privileges perception over structure and curation over process. The creative process that emerges is one in ml models are speculative technical elements that can foreground relations between diverse datasets and engender an urbanism of relations rather than objects.  

The application of such algorithmic models to design will be supported by the research developed by students part of Research Cluster 14 part of the Master in Urban Design at The Bartlett School of Architecture in London. 

Abstract

My paper approaches the theme of computational creativity by looking at uncertainty as an epistemic and aesthetic tool that must be examined to address the challenges brought to critical practice by planetary computation. It positions uncertainty as central to how the encounter of the human practitioner with non-human machines is conceptualized, and as a resource for building speculative-pragmatic paths of resistance against algorithmic capture. It proposes ways to cultivate uncertainty and use it as a design material to produce new types of knowledge that question machines’ pre-emptying manoeuvres and resist their capture of potential. The argument proposed is that uncertainty affords the production of new imaginaries of the human-machine encounter that can resist the foreclosure of futures (what will be) and are sustained instead by the uncertainty of potential (what might be) (Munster 2013). Dwelling in a space of potential – Deleuze’s virtual, or what I call a space of ‘maybes’, requires of the practitioner a repositioning of their epistemic perspective and reflecting on the following questions: how can material knowledge be made by engaging with modes of un-knowing and not-knowing in machine interaction? How can these modes of un-knowing and not-knowing be fostered as a critical and political onto-epistemological project of reinventing critical practice for the algorithmic age? (Horl et al. 2021; Hansen 2021, 2015, Pasquinelli and Joler 2020). The paper argues that the machinic unknown should be engaged with - not through the conventional paradigm that pitches human vs machine creativity and attempts to rank and score them through similarities, but rather through a (paradoxical) deepening of the unknowability at the core of the machine (Parisi) and machine’s own incommensurability (Fazi 2020). It then proposes the Chinese notion of wu wei (active non action) (Jullien 2011, 2004, 2000, 1995; Allen 2015, 2011) as a stratagem to experiment with to craft speculative-pragmatic interventions, and to augment the ‘power of maybes’ as a space of anti-production, and resisting reduction (Ito 2019).

By ingesting a vast corpus of source material, generative deep learning models are capable of encoding multi-modal data into a shared embedding space, producing synthetic outputs which cannot be decomposed into their constituent parts. These models call into question the relation of conceptualisation and production in creative practices spanning musical composition to visual art. Moreover, artificial intelligence as a research program poses deeper questions regarding the very nature of aesthetic categories and their constitution. In this essay I will consider the intelligibility of the art object through the lens of a particular family of machine learning models, known as ‘latent diffusion’, extending an aesthetic theory to complement the image of thought the models (re)present to us. This will lead to a discussion on the semantics of computational states, probing the inferential and referential capacities of said models. Throughout I will endorse a topological view of computation, which will inform the neural turn in computer science, characterised as a shift from the notion of a stored program to that of a cognitive model. Lastly, I will look at the instability of these models by analysing their limitations in terms of compositionality and grounding.