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ADS2: Demonic Shores – Imaginaries of Indeterminacy in the Age of Logistics

Helena Francis

Helena holds a BSc (Hons) in Architecture from the University of Bath. She works at Grimshaw Architects in London.

At the RCA, Helena’s research has explored the spatial opportunities presented at the intersection of organic and machine bodies. Her interests lie in the fields of digital technologies and environmental and social politics, with a particular focus on machine learning, intersectional feminism and the politics of non-human knowledge production. Her dissertation Feminism, Data Technologies and Spatial Practice: Towards a manifesto for 2020 discussed methods for reclaiming and counter-purposing data technologies.

In ADS8: Data Matter: Digital Networks, Data Centres and Post-Human Institutions, Helena’s first year project Cultivating Feminism[s]: A cyborg future for the data archive explored advances in biotechnology to store archival data in the DNA of living plants.The theory of cyborg is realised as a tool of power against binary-oppositions, imagining a future in which constructed identities are fragmentary, fluid, and in-between. The project has been featured in the Het Nieuwe Instituut x TU Delft 'Repositioning Architecture in the Digital' Conference, the CAADRIA 2021 Exhibition, Marina Otero 'Cartesian Enclosures' in New Geographies and Cosa Mentale 'Dixit' magazine.

In ADS2: Demonic Shores – Imaginaries of Indeterminacy in the Age of Logistics, Helena's thesis project Algorithmic by Nature further developed her work with plants and machines.

The project firstly deconstructs the system of machine vision. Questioning how space is reconfigured by more-than-human acts of algorithmic architectures. Secondly, there is an identification of non-human and indigenous conceptions of space that act as forms of resistance. How can algorithms be trained by organic and esoteric systems, to intervene in technological process of extractivism, control, and governance?

The design is a non-human vision system to develop alternative ways of seeing and knowing space. Inspired by animism, photosynthesis and self-computing architectures, the posthuman intervention is a counterpurposing of the machine vision system to broadcast the lifeworld of the world’s most polluted forest. In its most basic terms, a system to record and livestream the photosynthetic rate of the plants.

The presentation explores this non-human vision system through a series of performative representations. The research, a site-specific infrastructure strategy, plant to machine experiments, and the generative perceptual overlay of the data collected from the plants.

What can humans learn from non-human vision? Instead of extractivist practices, exploiting and mining everything that can be deemed a resource. Plants are in constant interaction with the environment. Seeing, thinking and being is not an internal, “self” privatised process, but a sensing, a constant openness and interaction with its environment, full of conversations, discussions and agreements with it. The nature of plants is in our past, Marder reminds us, adding that they do not discriminate against those to whom their breath is given.

Visualisation of photosynthetic process
Visualisation of photosynthetic process
Plant-machine experimentation
Plant-machine experimentation
Plant-machine experimentation
Plant-machine experimentation with visualisation of photosynthetic process
Infrastructure strategy

We are in the midst of a data revolution, but this revolution not only articulates the proliferation of data as the driving force of our lives but the role it has in catalysing a dramatic and wide reaching change in the way bodies are organised and made visible. The project spatialises the invisible realm of algorithmic architectures. The data sphere is commonly seen to be non-physical and therefore distinct from the field of the spatial practitioner; however, the design proposal recognises data as an architecture that forms, organises and shapes relations through more than human performative acts.

Algorithmic or machine vision is concerned with the automatic extraction, analysis and understanding of information from every visual input imaginable. Data is extracted from our environment using the full range of the electromagnetic spectrum.

Machine vision systems are formed of a nexus of sensors from pieces of code to networks of satellites feeding corporate and government actors, also known as the informatics of domination. The project shows an awareness of the reductive nature of the system and, crucially, its ability to reify certain regimes of power and knowledge production by operating through an aesthetics and ideology of objectivity and distance.

Digital ecologies of machine vision and algorithmic intelligence mediate preexisting notions of social imbalances. Assemblages of data are not inert productions but rather lively intraacting ontologies that are in relation and connection with all bodies beyond them. Data are both materially and discursively produced from the multiplicity of forces that include human and more-than-human ontologies.

It is through the concept of a lifeworld that algorithmic architectures reconfigure relations and space. Every organism gives birth to a lifeworld through its spatial stimuli. Therefore, a­ life­world, contains­ all­ things­ that­ can ­be­ recognised­ and­ detected ­by ­a system.­ The co-individuation of the epistemic and the ontological binds certain features of the world to knowledge apparatus in a partial and limited way, otherwise known as a situated knowledge.­ What matters in a system is not the communication between receivers, but the senders and receivers themselves that individuate­ a specific­ mesh, lens and filter­ through which the continuity of the world passes. This lifeworld or knowledge is broadcast back to us through our technologies through what can be described as the Cave architecture. 

A­ contrast­ space­ or ­blind­spot, also exists, which—from the point of view of the life world—has no existence whatsoever. In the context of the existing deployment of machine vision systems, this paralyses generation of different forms of realities beyond extractivist and humancentric subjectivity. In the lifeworld of this gaze, other ways of knowing, of seeing, of being, do not exist. 

To intervene in this system the project embarks on a re-writing of the history of algorithmic architectures. Some see algorithms as a recent, technological innovation implementing abstract mathematics. On the contrary, algorithms are among the most ancient and material practices, predating many human tools and modern machines. 

Algorithmic architectures are self-computing spaces capable of complex bottom-up structures. The research locates these at a cellular scale and a spiritual scale. Molecules in biological systems are considered self-computing actors. Similarly cultural rituals are identified as expressions of algorithmic thought from the ancient Vedic ritual of Agnicayana, the Zarija astrological device, and the Yoruba Ifa divination system.

There are algorithms embedded in culture. There are algorithms embedded in nature. Data and algorithms are quite simply an ongoing process of the world trying to become intelligible to itself. A search engine

Embracing the idea that the machine world acts as an extension of the organic world, capable of healing or poisoning depending on its usage and users. The project questions how can algorithms be trained by organic and esoteric systems, to intervene in technological process of extractivism, control, and governance? 

Plant knowing is a sensual growth. A systemic response to light. Plant vision is photosynthesis. Vision without representation. A process of data. An algorithm. A sensing more than a seeing. 

According to indigenous epistemologies of animism, plants are sentient beings, they impart wisdom, have agency and consciousness. There are persons in these other beings, they see and they think. 

Yet, they have been marginalised as sentient beings in Western discourse, owing to an incapacity for humans to recognise elements of ourselves in the form of a vegetal being. They seem too slow, or too quiet. 

Inspired by animism, photosynthesis and self-computing architectures, the design proposes a counterpurposing of the machine vision system to broadcast the lifeworld of the world’s most polluted forest.

It is not that organisms and technologies are only now merging as part of some new cyborg innovation, but that the design is the result of a co-evolution with its ancient automated landscapes, forever entangled with systems of value. 

The design lies in the world’s most polluted forest in northern Siberia. The growth and photosynthetic rate of the forest is slowing due to rampant air pollution blocking sunlight, both from neighbouring ore mines and the influx of the world’s pollution travelling north.

The automated forest organism is an apparatus for vision. It is an autonomous self-computing space, a bottom-up structure that is controlled from within and responds to its ever-changing diversity. A sensor system collects and translates data. This proposal is an extension of the forest’s own ecosystem. Through a glitch in the search engine, the automated forest broadcasts an algorithmic representation of its vision.

Plant vision understood as photosynthesis has 2 stages. The first is a non-representational process of data to fuel the organism’s metabolic activities. So that is plant vision for plants. The sensors act as an extension of this. The second is that vision’s ability to influence other beings beyond the plant, as the photosynthetic process does. This lies in the perceptual overlay that reenters the logistical human-machine vision system. 

The design of the algorithmic representation of forest vision is informed by a series of three experiment types between sensor and plant.

These experiments recorded light data, in different colours, intensities, directionality and duration, and then the corresponding electrical response signals, and movement data or growth of each plant. The experiments were conducted with specific plants from the Siberian ecology to inform an understanding of each plant in time and space. 

Light dependent reactions allow plants to optimise their use of space. They can tell the time of day and time of year by sensing and using various wavelengths of red light. They use blue wavelengths to inform their directionality, growth and movement.The plants generate electrical signals in response to these light processes. The electrical signalling serves as the most universal system for communication, by using network of mycorrhizal fungi, plants collectively manage absorbed energy. The forest is a community where this vision is distributed, creating a sustaining ecology that extends to other actors, through their breathe.

The experiment output data creates the visual representation of the photosynthetic process. A unity space is used as a design tool where the representation exists as a point cloud data set from a lidar scan of the forest. A series of scripts are then applied to it using the data from the experiments so that it can fluctuate with light quality and pollution levels. 

The light colour and intensity are determined by scale and position of the plant within the forest. The movement direction of the points is based on the sun's position in the sky relative to the specific plant. 

The sound is a translation of the electrical signals representing the quality of photosynthetic rate. Using granular synthesis and open source code, the sound is created using an industrial metal patch because of the neighbouring mines. It is elongated or shortened based on the real time of the specific plant. For example, lichen which lives for 5000 years would operate very differently to a perennial that lives only 1. 

This way of knowing and perceiving space reenters the logistical machine vision system through the internet. Just as the light dependent reactions of plants go on to influence the network of other actors within the forest, the forest also extends to us, to influence us through our technologies.