Greener Grass is Growing: Vampiric Technologies and the Anthropocene

Anthropomuzak

Notes towards a theory of automated libidinal economies

A collection of questions about the development of AI

Trevor Paglen: Abstraction and AI Art

“Revolution in perception with machine seeing for other machines, humans are designing machines to see for us, I am trying to understand how to see.”
Trevor Paglen



Trevor Paglen, Bloom (#79655d), 2020, dye sublimation print, 26" × 19-1/2" (66 cm × 49.5 cm) 26-3/4" × 20-1/4" × 1-1/2" (67.9 cm × 51.4 cm × 3.8 cm), framed

AI, Singularity and the Terror of Machinic Takeover

The Franchise of the Self: Envisioning AI through Biopolitics

Table of Discontents: A sketch in chapter introductions for a book on AI and culture

Machines and Art: The Computational Aura

Of Monsters and AI: Modernist Monster Myths as Gothic Reinterpretations

HAL 9000, image from Author

The myth of Zombies as virus that we can’t contain and AI as virus that we can’t contain is a narrative that we just can’t shake

As monsters began to emerge from Gothic horror literature in the 19th century, the industrial revolution in England saw the birth of the steam engine, the radical expansion of an underclass - marked by the prominence of prostitution and child labor - and a general decay of so-called Victorian values - urged on by the extreme poverty that grew from an intensified stratification of social and economic classes.

“...gothic fiction registered not only the fissures in Enlightenment humanism and rationalism but also the revolutionary tendencies of the late eighteenth century. Despite these origins, critics have read the gothic primarily as a mode focused more on psychic than on historical depth and complexity. Thanks in large part, perhaps, to the legacy of Sigmund Freud, who transferred many of the gothic's most cherished conventions (buried secrets, hidden ancestries, repressed desires, imprisoning vaults, mysterious passageways) to the self, the gothic has long been seen as a mode that expresses, above all, the nightmare landscape of the unconscious, the drives of the id, the neurotic, haunted ego.”
(Gale, 2006)

Ascending from the swirl of a refinement of capitalism unseen until then, a further alienation of worker and capital that was introduced by the mechanization of production and subsequently human life, came the Gothic monster myth, in the form of creatures such as Frankenstein, the Zombie, and Dracula - all of which had tenable connections with industry, capital and class through either their physical properties (Frankenstein was made with ‘spare parts’ from the pre-modern workshop), their symbolic relationship with the labor class (Zombie as mythic counter-revolutionary force in Haiti), and Dracula as a being who consumes the blood of the Bourgeoisie as a means of gaming the class system. The rise of industrial capitalism and liberal economic policies ushered in an era of human misery never before seen - amplified by a dispensation of ethical discourse in the social and financial realms. This marked the dawn of laissez-faire economic liberalism.

While the metaphor of monster is seen throughout human history, only recently has its influence leaked into industry - a reversal of the Frankenstein situation, which could be seen as an industrial-age fever dream, emerging directly from the factory floor into the collective semio-capitalist economy. (Berardi 2012)

Gothic narrative and cultural metaphor:
https://bigthink.com/videos/monsters-are-living-breathing-metaphors

These Gothic monsters set the stage for modernist interpretations http://plawiuk.blogspot.com/2005/02/gothic-capitalism.html

Keywords: Industrial revolution, Class, Freud, Marx, Modernism

Wombat Scat, Communism, The Obelisk and the Death of Art: Poems for Geometric Shapes.

©Public Domain, 3D model of Wombat Scat


A meandering look at the semiotics of evolutionary geometry, or how I learned to love the square

Is the Wombat a cultural producer? While most animals were carrying on making organically shaped excrement; the rabbit small ovals, the human with its variable cylinderoid and the cow with circular discs, the Wombat was busy evolving its scat into a quite tidy square shape. And while most scientists were trying to answer the question of how the Wombat became the only non-human animal able to create a cube (something about intestines this and that), the richer question is, as per usual, why Wombats create cubes of poop. What immediately comes to mind is of course, Arthur C. Clarke’s 1968 science fiction novel, 2001: A Space Odyssey, where a so-called “Tycho Magnetic Anomaly” was presented in the film’s pre-sequence to protohumans and, in the main narrative sequence, to Space Race era humans on the moon, and then later in the film’s sci-fi peyote dream post-sequence presents the object once again, pretty much in a post-modern Louis XIV bedroom.

“Q. What's that big black monolith? A. It's a big black monolith.

Q. Where did it come from? A. From somewhere else.

Q. Who put it there? A. Intelligent beings since it has right angles and nature doesn't make right angles on its own.”
Roger Ebert

Grids made spreadsheets and spreadsheets made the world

© Public Domain, Visicalc, the world’s first Spreadsheet program was released by VisiCorp for the Apple II Computer in 1979, the same year as Kraus’ essay on the grid was published.


‘The book will kill the edifice.’
Victor Hugo

The spreadsheet kills everything into two-dimensional formulas and with more efficiency

Grids made Spreadsheets

When Rosalind Kraus wrote in her scathing 1979 critique of the grid as having succeeded in announcing ‘modern art's will to silence, its hostility to literature, to narrative, to discourse.’ and ‘It [the grid] is what art looks like when it turns its back on nature.’ she may well have portended the coming of the Spreadsheet. If the deep saturation of the grid as an aesthetic system declared the ubiquitous acceptance of the anti-real, anti-natural and anti-narrative in art and culture1, then the spreadsheet might be aptly named as the ultimate call sign for postmodernism and the the deconstruction of ethical frameworks, the amplification of a hostile perspective on nature and the willingness to play with precarious moral checks and balances that culture previously relied on in world-building. The affordances that the spreadsheet brings - to be able to play carelessly with the factors vying finance and ethics against one another - brings a numbness, an insulation from consequences less tangible from a pre-digital, pre-data visualized, pre-Excel spreadsheet world.


The spreadsheet is a perfect and ultimate form of the grid system - not only in it’s obvious visual characteristics; endless rows and columns of infinitely configurable rectangles lined up

Square vs the World: Seeing like a Machine

Waves 001, Davey Whitcraft, 2022. Video, dimensions variable, 1 minute running time.

‘The shape of things to come.’
Eckbo, William W

‘Observe that rectangles must occur at the start of everything; they are the first form we make when making form.’
Mondrian, Piet. The artist who defined modern art by pitting square against circle . p. 180

How to interrogate the square? From the brick to the pixel, I see rectangularity as a divisional principle between biological and machine based vision systems.

The square as abstraction
The square or rectangle is the ultimate abstraction. This is readily observable in art, design and architecture - and can be seen in the works of Cezanne, Mondrian and deeply rooted in modernist design and architecture, coinciding with the industrial revolution and building which also was square based. More camouflage however, is the presence of the square in soft power  system, where it’s harsh angles lurk ominously in the construction and organization of everyday life, culture and thought.

‘The square, apart from its obvious use in geometry, is the basic unit of measurement in most western cultures, and it is also the basis of architecture.’
Wikipedia Square article p. 7

‘In architecture 'the square' is an important but sometimes minor element in a plan. A rectangular room is easily made by cutting three-dimensional objects into four equal parts (e.g., with a saw or chisel). The simplest, least expensive way to construct a room with an exact square floor plan (and two identical walls) is to cut two identical triangles into four equal parts.’
Dixon p.

The square as a universal
Can an argument be constructed which proposes the rectangle as not only a universal shape in art, architecture, and design, but also in thought, behavior and cultural disposition. This essay will argue that the square is quite literally a state of mind.

‘Cezanne's entire life was a constant struggle against external reality, which he wished to replace with his own subjective version.’
Duchaine p. 159

‘Cubism revolutionized art through an exploration of one-point perspective, objective chance and rationalized abstraction.’
Wikipedia Cubism article p. 1

It is also a criticism of square based art forms such as painting, sculpture, architecture and so on. A number of influential designers and architects at the start of the 20th century were proponents of making rectangles a central factor in modern-day design philosophy. They believed that abstract designs need to be based on real-world applications, otherwise they would not hold up against their surroundings. The square has been so common throughout history that it may be perceived as mundane, especially if the concept was introduced well before its time.
Ramanan et al. (2007) copyright IEEE


Image credit TC

Machines See in Squares
In human culture image recognition is fundamentally important - being able to identify food, friend or foe is crucial for survival. It is both not surprising and slightly disappointing then, that so much energy is put into giving machines the ability to mimic this human function of seeing, identifying and classifying images. Machine vision systems are trained using dominant cultural sensibilities to do something that is A) Of very little value to machines themselves, and B) By definition a particularly human quality - that is, a cultural construct (the categorization of objects into symbolic meanings). Machines simply don’t see this way.

Let’s take a look at how a typical machine vision system works. The system consists of 3 main components:
Using a method called Hough transform, a binary image is transformed into lines and points. These point are then used to identify if the specific pattern detected occurs significantly more than it randomly occurring line segments.

This output will be used to determine if an object matches the pattern which has been defined as that object. If so, the object is considered to be that object and one detection will be recorded. If not, another set of possible objects will be presented for detection to the machine vision system, and so on until it exhausts all possible matches for objects for that particular image.

Most of the time, the number of matches recorded is a very small random amount. The new algorithm that was developed for the ACM SIGGRAPH 2010 Challenge attempts to change this by deciding which set of points to use as qualifying lines or points.

In use, an object will be classified with an error rate of less than 0.01%. A human operator is required to label each decision as either 'successful' or 'not successful', but this classification can be automated with some work.

Recent work has shown that the error rate is lower than 0.1% using features based on the Hough Transform (an algorithm developed in 1842 by Thomas Young) alone.

A system that can accurately detect objects that are positioned within a 2x2 area requires some way of creating these positions. A possible method would be to create a grid that can contain all possible combinations of 2x2, 3x3 and 4x4 for input. Another method could be to use images as a source, and then determine what part of the image is closest to each particular object in 3-D space.

Image recognition systems use differences in sensed light - translated into a pixel map - to detect shapes - which are then compared to a database of shapes and analyzed for probable matches. The difference between the raw light and the final pixel map that represents the sensed light can be used to determine locations of objects in 3-D space. Many methods have been developed to map this input into a final output.

When working with images, one of the fundamental activities is capture, or generating a digital representation of an image. The aim of recognition is to accurately compare a sensed image with some library of images and generate an identification record for the unknown image. This ID record is then used in further process - such as selection or classification - to perform some action such as categorization for further analysis or tracking for later retrieval.

A number of different methods and tools can be used for capturing images. There are several concepts at play in these systems. Firstly, the ‘edges’ of objects are used to determine background/foreground/subject. The systems look for differences in pixel value using a histogram.  Shapes are then composed based on detected edges - almost always a variation on a rectangle. In order to compare a new image with an existing library, the system will create a number of features automatically (such as edges and corners) and then compare these features with those of known images. It is possible that features will be mapped onto other features (such as mapping a line onto a rectangle’s edge) or discarded altogether.

There are two types of recognition systems: template matching and feature recognition. Template matching is used when the estimated size of an object is accessible - that is, it can be expressed in terms of pixel dimensions. The systems have a profound relationship with the limitations or characteristics of human industry, which also basically relies on variations of rectangles for simplifying mass-production. Google Earth and Google Maps both use template matching for their 3-D imagery. They have input the height of a number of locations from space and use this to identify the topography.

Feature recognition is used when objects are irregularly variable and do not have fixed size or shapes. This may be due to three-dimensional aspects of an object, but also because a machine vision system is unable to determine accurate dimensions of an object.

It was found in research that people’s eye movements when reading consist of saccades (rapid, ballistic movements from point to point) followed by fixations (at rest).

Recently, a study by a group of scientists from the University of Oregon showed that brain scanning techniques may be used in conjunction with computer vision systems to successfully provide information about the location of eye-movement during reading.

The current paradigm for understanding visual perception is based on the assumption that the world can be understood in terms of intrinsic features; that is, features that are naturally present within every object (such as its color, shape and size), and are independent of how it is observed by an observer. This pre-supposes that all humans perceive the world in the same way, and have fully developed brains capable of perceiving these ‘intrinsic’ features.

What can be said about the rectangle dominant culture of human industrial production and machine vision? In short, this paradigm does not take into account that there are many ways of ‘seeing’ and observing. First, it ignores what has now been shown to be more than likely - that individual brains have not fully developed (a startling proposition), and therefore their potential cannot be fully exploited. In addition, it overlooks the fact that the world is hugely varied, and so accurate generalizations cannot be made about how all humans will perceive objects (see previous comment). A further problem is that this paradigm pre-supposes that there are intrinsic features within an object (such as its color, shape and size), when in fact these may vary according to an observer. "It is a universal truth that most people cannot readily see the shape of an object. Whether the objects are circles, spheres or squares, they are often seen as having a pattern of gray shapes on a white background. The human brain is so built that it can only make sense of people and objects by ‘convincing’ you that what is actually beyond this limited space-time frame is something else. This trick relies on the mind’s great flexibility and ability to create plausible explanations for what it sees - even if those explanations contradict reality. The brain uses these explanations to fill in the gaps and to help create a reality that is more meaningful, understandable and manageable.

A rectangle is the simplest way to create a rigid, rectangular structure. And so it became a dominant motif in the design of almost all industrial structures, especially with wood and metal. Indeed, one of the most common metaphors in human industrial culture is that of ‘a frame’ which then serves as an organizing structure for larger constructs; it helps to understand how things are related. A frame determines what can be contained within it, and thereby becomes a metaphor for understanding broader aspects of life because of its inherent boundaries (see also Frames (social sciences)).

For the purposes of this discussion, it is important to note how rectangle (or square) based systems of production (such as the machine vision system used for Google Earth images) are dependent on mass-production for simplifying their components and processes.

Interestingly, by-and-large the biological format lacks rectangularity almost totally. By-and-large, biological shapes tend not to be rectangles or squares. In fact, even at the microscopic level, living things tend not to have a rectangularity to their structure. Instead they have curved surfaces, and irregular dimensions. For example, a typical virus consists of a number of amino acids arranged in helical patterns within protein shells. The human body becomes an example of this by virtue of its irregularity, and in relation to the fact it can contain any number of features. For example, it has been shown that individuals cannot perceive thousands of objects simultaneously as distinct objects. Instead, they see these as overlapping parts of a single shape. Furthermore, their spatial perception is also highly developed, so that there are two diameters in one field of vision; dividing the right and left hemispheres by the optic chiasm and splenium:

Of course the pixel itself is also a rectangle. Interestingly, by-and-large the biological format lacks rectangularity almost totally.

What kind of cultural qualities can be observed from the idea that machines have the most trouble seeing/understanding biological entities?

First, it creates a new insight into the idea of constraints (or limits) on what can be observed. That is, machines have difficulty understanding unpredictable and changing behavior, whereas life is highly dependent on these qualities. In short, machines produce rectangular and square products which are easier to deal with because they are predictable. This suggests that machine culture and biological culture are inherently different.

Second, this realization that machines can only see rectangles presents a challenge for future robot vision systems - to make them capable of extending their reach beyond this culturally determined limit. Third, this raises an issue about the meaning of intelligence – that is, whether we can use technology to create intelligent machines. The idea of the intelligent machine has been widely debated in academic and popular science studies. Many have commented on the limitations of the ‘intelligent’ machine as a postulate for aid to human beings.

The findings discussed here suggest that biological entities are not easily understood by machines. They also highlight that these entities tend not to possess rectangularity, and therefore cannot be easily scaled or produced as products (for production lines). Instead they are highly variable and unpredictable because they are produced by organisms using a range of sensory inputs (sight, taste, hearing and touch). In addition, because organisms are highly adaptive, and because they have the capacity to use their intelligence to overcome obstacles (such as those created by rectangularity), the biological entity is not only unpredictable but also resistant to control. Furthermore, it has been shown that this resistance can result in a dangerous feedback loop when machines try to exert control over life forms.

Machines may be unable to see and understand biological entities not just because of the shape of these entities (which is unique for each entity), but also for their complex behavior and changing nature (rather than being an unchanging entity). In 1964 Herbert A. Simon coined the term ‘satisficing’ to describe a new way of thinking about human behavior. He introduced it as an alternative approach to maximizing economic profit. In this, he suggested that maximizing profit is not always the best way to think about decisions. Instead, he suggests that people often try to find ways of doing well enough at what they do (instead of trying to be the best). One example of this would be a basketball player who has good enough moves on court, rather than the best moves. Another would be someone who is able to satisfy a customer by delivering goods within an acceptable time frame, but not in the fastest way possible.

It almost looks like a kind of alternate ecology is emerging from this developing system, of which machine vision and image recognition is the final link, the unit which is needed to close the loop of humans dominating nature, themselves and god completely. In other words, human's idea of mastery of the earth has taken another step. In many ways, an alternative interpretation of this anthropomorphizing of data (the tendency to see it as ‘human shaped’) is that data is more than just abstractions. It is something that has been given human-like qualities because it appears ‘human’ in its intent and capacity to act on people. This can be understood with the help of Max Weber’s notion of Verstehen:

In short, we understand machines because they act like other human beings (even though they are not human). Thus, their shape and the way they interact with us assumes the characteristics of a physical entity – such as having a face, eyes and a mouth. However, we should not underestimate the extent to which people know about machines in terms of their shape and form. Studies have shown that as well as looking at machines in anthropomorphic terms, it is possible to describe these with inventories of their physical attributes. For example, the ‘older’ and heavier a machine is (for example a computer), the more likely it is to be talked about as a solid/massive entity. This is similar to the way in which older people are often portrayed as solid, rather than young and nimble.

This suggests that even though machines have been created to remove human beings from the production process (because of the danger involved), it does not mean that humans have been completely removed from these processes. Instead, our desire for predictability and control has led us to create machines that look like humans. In other words, we have replaced human beings with machines (instead of just replacing ‘worker’ with ‘robot’), because they also provide us with a sense of predictability and control. However, this is not the only way in which humans interact with machines. Humans can also interact with machines using human-like processes (such as asking them questions or waiting for them to reply). These are consistent with the idea that people have a tendency to anthropomorphize machine vision.

As shown by studies such as those conducted by Josef Kittler and Burghard Knapp, there is a tendency to anthropomorphize computer models. This is demonstrated by the way that they are often used to explain visual perception, cognitive processes and communication. Many of these early computer models were designed as representations of real world entities; they attempted to make sense of the world around us in symbolic terms (for example describing images through words or numbers).

The results of these kinds of computer models suggested that they were capable of carrying out highly complex functions, including recognising things. These models were used to support the case for behaviorist accounts of perception and cognition. For example, many have tried to use them as a case against representational accounts of perception and cognition. However, as many authors have argued in response to such claims, there is a degree of ambiguity in what can be said about machine vision.

The Biology of Seeing: How We Sense the World. Keller, Thomas (2009)

In his work on software and systems, Stuart Kauffman elaborated upon this recognition of the importance of shape by remarking that 'by increasing numbers and complexity, not merely do things get more elaborate, but they become less regular. The result is that they become more difficult to describe and manipulate’. He added that ‘lines between shapes blur’. In this way he is suggesting that the concept of binary distinctions (between shapes) may be difficult to maintain in a complex environment.

That is, a world is emerging in which the most recognizable and readily detectable surfaces, environment, objects and informational flows are created by machines (with varying human intervention in the process), are mostly for machines (with humans as end-users) and if not completely detectable only by machines (in the case of machinic ‘images’ which are never abstracted to the visual) then are at least more readily (or just equally) understood by machines than humans. In this sense, the phenomenology of vision is not only a human endeavour. Rather it is an increasingly complex and abstract one with multiple layers and interfaces.

In a similar vein, Mark Hansen notes that: ‘In the contemporary moment we are seeing the "pre-cultural" becoming visible for the first time’. By ‘pre-cultural’ he means ‘that which precedes language and other cultural systems – as well as its systematic organization in language itself – that which precedes code: what remains irreducible to our codes’. This observation is supported by the fact that machines and human practices interact, at least in part, through codes (for example computer languages and internet protocol).

Likewise, while it remains true that we can only recognize the world in terms of its binary elements (the distinctions between objects), the way in which they are perceived is now being shaped by a process of ‘becoming visible’.

Is it then a world in which machines create machines to be seen by machines to create better machines? The boundaries between the human and the machine have been blurred in certain areas. Technologies, such as 3D printing, may allow humans to create and customize objects for whatever purpose they might wish. However, this technology is not unique to humans – it has been used by other semi-autonomous entities (for example by companies such as Replicator). Similarly, and most obviously 3D printing allows us to ‘print’ our own limbs using reprogrammable mechanical systems. It is frequently cited that this technology could allow us to upgrade our bodies – perhaps in ways comparable to upgrading a computer. This would be an example of human-machines co-evolving. Moreover, these ‘upgrades’ would be a practice in which humans are in varying degrees playing with machines within an environment which is itself a product of creative technics.

In this sense, new modes of visual experience can also be considered as a product of machinic evolution, or at least as evidence of ‘evolutionary’ processes. In The Evolved Apprentice David Kirsh suggests that the ability to make ‘good’ visual judgements is the result of learning through natural selection – a process which involves making mistakes (i.e. not making decisions that directly contribute to survival). In this way, we can see the development of a means of visual communication that allows us to ‘comprehend’ the world through senses other than vision. It is possible to use computer languages and algorithms that allow us to change the colour of objects, or alter the texture or texture-less surfaces (for example in a plastic). These are changes made possible by machinic evolution – algorithms which allow machines to recreate themselves but within an environment that is still defined by humans. Understood in this way, visual experience is not simply a property of machines – it is also a product of interactive knowledge-making through a co-evolutionary process that allows humans to be influenced by machines.

Note also that the evolution of images in the art world has been seen as one element in the creation of new forms. The digital transformation of images into virtual reality has been called ‘a sophisticated form of virtual sculpture’. In this context, it is possible to see photography and other representational practices as being used by machines within environments which are themselves products of machinic evolution.

‘What is the world? That which can be touched, seen, and described in art.’
Mondrian

‘A rectangle is the simplest form of geometry.’
Cezanne

What has machine vision revealed that was already there but not necessarily available to human perception and comprehension?

It has been noted that while the environment is being recorded, it is also being transformed. The process of recording and processing can be seen as a kind of thinking in interaction with the environment:

To start, the visual conditions that image recognition systems prefer - the stark contrasts and geometricivity of industrial production only emphasizes the aesthetic dominative tendencies of industry and capitalist economics - and necessitates a look into the aesthetics of mass production, labor surplus and profit dynamics. Image recognition systems proclivity towards objects of mass production bifurcate the biological and non-biological through the unique sensing characteristics of machine vision. A convergence of machines and biological systems, under the conditions of biological capital, produces a perceptual root that keeps the autonomous machine vision system in check. Its regulation is performed by the economic rate of profit and by the uniquely human ability to socially recognize emotions and intentions.

In a construction on David Harvey's work on Neoliberalism, this paper attempts to demonstrate how mass production of objects can be understood as an aesthetic regime of accumulation not only in terms of capitalist relations but also as an aesthetic regime that distorts or "de-poetizes" nature, producing images that are aesthetically based on anything but natural forms. This production is then understood to produce technologies that reproduce and further the economic-political domination and control of nature. It is argued that this aesthetic regime produces not only a need for the mass production of objects and the accelerating rate of technological change, but also a need to produce technologies that de-poetize nature through image capture and reproduction. The reproduction of images necessitates machines with the ability to recognize pre-defined icons, which are in turn dictated by their economic function as designed by humans. This programmatic idea of human's naturalized representation of nature is contrasted with science's ability to "see" life in a different way - and point out the implications they have on aesthetics. To close this argument, an example is provided that demonstrates how machine vision systems are produced as aesthetic objects themselves and open up new problems in regard to aesthetics.

In such a way, the aesthetic regime of accumulation is not only produced through capitalist economics but also as an aesthetic regime that simultaneously distorts or "de-poetizes" nature, producing images that are aesthetically based on anything but biological forms. This production is then understood to produce technologies that reproduce and further the economic-political domination and control of nature. It is argued that this aesthetic regime produces not only a need for the mass production of objects and the accelerating rate of technological change, but also a need to produce technologies that de-poetize nature through image capture and reproduction. The reproduction of images necessitates machines with the ability to recognize pre-defined icons, which are in turn dictated by their economic function as designed by humans. This programmatic idea of human's naturalized representation of nature is contrasted with science's ability to "see" life in a different way - and point out the implications they have on aesthetics. To close this argument, an example is provided that demonstrates how machine vision systems are produced as aesthetic objects themselves and open up new problems in regard to aesthetics.

To start the argument, it is necessary to examine Harvey's discussion on Neoliberalism, which serves as the basis for further discussions regarding the aesthetic regime of accumulation. It is argued that Harvey's understanding of the concept of Neoliberalism provides a way to understand how biological systems, which are under the conditions of biological capital, produce aesthetically based images. It is proposed that the production of these aesthetic images through machine vision systems are analogous to the pre-determined ways in which humans represent nature, creating a means by which these aesthetic regimes of accumulation can be further discussed.

Harvey conceives of Neoliberalism as an aesthetic regime in two senses. Firstly, it is an aesthetic regime that not only makes it possible for capitalism to continue as a world system, but also for its reproduction and intensification. Secondly, the aesthetical nature of capitalism lies in the production of subjectivities that privilege the market as a way to construct meaning and value. In light of this idea, it is important to point out that aesthetics are not just for art or entertainment. They are part of everyday life - and specifically have a role in capitalism's transmission, reproduction and intensification as an aesthetic regime. This notion allows one to make sense of how machine vision systems function: as a part of everyday life, they are also aesthetic objects whose reproduction exists in the service of intensifying capitalist relations.

Furthermore, Harvey outlines two terms that will help to further demonstrate the economic regulation and aesthetic regime produced by machine vision systems: capital accumulation and cognitive surplus. Capital accumulation is the "process by which [capitalist] relations of production, distribution and consumption constantly expand..." (Harvey, Neoliberalism). Cognitive surplus is the amount of time that humans have to think about things other than work. Harvey discusses it as a byproduct of the telecommunications technologies made possible by technological developments, which makes possible what he calls "social labor without social relations" (Harvey, A Brief History...). The amalgamation of these two terms will allow one to discuss machine vision systems in terms of their affect on humanity's cognitive surplus.

To understand how machine vision systems affect human cognition, it is important to first understand the production of these systems. It is suggested that these systems are produced through the continual production of aesthetic objects, which in turn produce the conditions for their own continued existence. This is accomplished through machines that recognize pre-defined icons and are designed to recognize and reproduce them.

The issue of mass production is an important aspect to understand when continuing this argument. Here it is pointed out that machines as a part of everyday life and as an aesthetic object, both contribute to the creation and intensification of social labor relations. The cultural demand for these types of digital image capture techniques provides a means by which these systems can be mass produced at affordable rates, putting the technology into widespread use in the commercial sector. Because of this, it is also important to point out that the commercial sector begins to produce products that are geared towards these technologies in order to increase profit margins. One can consider this commercial use a case of "planned obsolescence," where machines and materials (like human bodies) are made to have a short lifespan so that they can be replaced. These technologies are then further employed through the commercial market and in turn produce more biological capital for those who benefit from capitalism: in this case, those who own the means of production, distribution and consumption.

It is argued that because of this, the images captured by machine vision systems are not only inherently part of the aesthetics produced by capitalism, but are also ways to make more effective the processes of accumulation. This is demonstrated through an example in which a house image recognition system was designed to identify and track people who enter a private building. Here it can be understood how these images are constructed as "hidden" within everyday life. However, when one considers them in relation to mass commercial technology that underpins machine vision systems - one has to start thinking about what such technologies could do in another way: they can be used as a means of social control and surveillance. Such technologies are made possible through the commercialization of machine vision systems, which in turn produce an aesthetic regime that has been characterized by social control and surveillance.

It is argued that this understanding can help to further understand how humans are producing a new "aesthetic regime of accumulation" through their use of machine vision systems. This regime is marked by two important features: the production of genetic information, which include images like those produced by machine vision systems, and the transformation of these images into data - which can be further transformed into economic value. Thus, one has to start thinking about how these images are a product not only of aesthetics made possible by capitalism but also a means to generate more capital as well as reproductive and productive processes within society itself. Thus, one can think of the design and implementation of these systems as part of a larger political economy.

In order to fully understand how these systems are an aesthetic object that produces a new "aesthetic regime of accumulation," it is important to point out that they do not only produce images, but also affects humans in other ways. One way images affect humans is by influencing their behavior. This is exemplified through identification technologies, which are now being built into passports and drivers' licenses in many countries around the world. These images can be processed through facial recognition technology - used for security purposes - or through gait recognition technology - used for tracking purposes. The second way that images affect humans is through the sense of self. One can consider the image one keeps on Facebook, or regarding the occurrence of "selfie" culture. These images are produced by users themselves and are then used to affect their own behaviors. These are not necessarily produced by machine vision systems, but they exemplify how image production can influence human behavior in other ways that go beyond its production as an aesthetic object. In fact, one can say that such affects are only possible because of machine vision systems' ability to produce images in a widespread fashion.

The concept of cognitive surplus is also important to understanding the way in which machine vision systems play a role in shaping human behavior. One can understand cognitive surplus as the ability to produce new ideas, new ways of doing things and new ways of seeing the world. Thus, cognitive surplus is a sum total of one's ability to think and act in new ways. A function of cognitive surplus is that it can be used to produce more capital (what Marx terms "labor power" in Capital). In this sense, one can consider how cognition provides a means by which humans can become more productive members of capitalism.

By understanding how these images are produced through processes - both aesthetic and economic - it becomes clear that they are productive as well as reproductive means used by humans to shape their own behavior. This means that one cannot simply separate the use of machine vision systems as aesthetic objects from their effect on humans. The production of image-based cultural products that are used to shape behavior and reproduce capitalism is part of what it means to be human within a capitalist society.

Thus, while the question of the "aestheticization" or "objectification" of humans by machine vision systems in Chapter 5 can be justified by claiming that they are an aesthetic object, this argument has much broader implications for understanding how such devices affect human behavior and social relations. As part of a larger political economy, image-based technologies are ways by which humans are able to produce more capital, through the production and reproduction of images.

‘The rectangle is the basic unit of measurement in most western cultures, and it is also the basis of architecture.’
Wikipedia Rectangle article p. 7

‘The square is the ultimate abstraction in art and can be seen in the works of Cezanne and Mondrian.’ Duchaine, Jean-Paul. Cubism: Cubist Painters , 2nd ed. (Boston: Bedford/St. Martin's, 2002). The Shape of Things to Come p. 132

When one considers what it means to become an object that can be owned and used by others, this has wider implications on the processes that enable capitalist accumulation. An important concern in this regard is that these technologies will enable those who possess them to control more capital - and thus increase their own wealth - as well as how they are likely to impact on their own audiences and uses (cf. Chapter 5). This is because the production and distribution of such images involves those who benefit from capitalism but also those who work in industries like spy technology. In the end, these images and people are commodified as "things" - not unlike the way that slave labor is treated in societies where slavery exists. To treat humans as objects and to turn them into images is to make them into a commodity that can be owned and sold by others.

This commodity status of humans can also be extended to include their bodies as well. When one considers how technology has turned humans into objects for production, this concept takes on a new meaning because everything about being human is now part of how humans are produced for other people's interests, whether for labor production or for 1%'s capital accumulation. This idea is exemplified in the way that machines can now be used to produce human skin. This is an example of another "machine aesthetic regime" - skinning machines - like the one discussed in Chapter 5.

This seemingly inhuman quality of such images becomes evident when one considers how they are used as a means by which people can present themselves as commodities and in turn, have a larger role within capitalism. One can say this happens in several ways that are exemplified in spy technology, for example: through being able to produce images for identification purposes; through surveillance technologies; and through information technologies that make it easier for some people to survive the capitalist system. These three subsections are discussed at length elsewhere (cf. Role-Playing Games).

Historically, animals have specific ocular qualities which define/are defined by the role/position they hold in their environment - predator have eyes located on the front of the head to be able to detect depth well, a necessary visual function for hunting, while prey have eyes toward the sides of the head to have a wider field of vision and therefore are able to scan for danger more adeptly. In the contemporary animal-machine system, however, the eyes become very important in defining how humans operate and function within a certain environment. In fact, for many animals, their eyes - as part of their ocular apparatus - has become an important way to identify one's species.

In this sense then, the "wrapping" of a human by an animal skin is an image that defines its work. It also defines its status as a commodity so that it can be used with other commodities to create other images that can then be used in the future. The primary role of animal skins is to make humans productive elements within capitalism and to help define what it means for humans to be part of capitalism at all.

However, when one considers how technologies of image production have been used to develop and encourage new markets/markets for images and image-based products, it becomes clearer that to be human within capitalism is about using images in a productive way for the purposes of capitalism. New ways of creating and distributing images are being created as a means by which humans can not only present themselves as commodities but also use such images as a way to survive within the capitalist system. Recall that this book was originally conceived as a method through which I could present myself as a commodity - in this case, an academic commodity - and make some money on the side.

Alas, what does the unique visual abilities of our machinic observers - with all the algorithms, lenses, pixel planes, neural networks and deep learning-enabled surveillance platforms tell us about where they can be located in a sort of nature/culture hybrid order? Where do such visual capabilities of animals and humans intersect with that of machines? It is important to remember that we are focused upon the "where" because of the issue of identification. Who defines/defines images and who owns/owns them? In order to present something (as a commodity or as an image) it has to be able to be identified: metaphorically (as a thing) or literally (through something like an ocular apparatus).

This is how these new technologies create new markets and how they better help those who create them. This is also how these technologies enable those who use them in their work to become a more productive element within capitalism - because they help them identify better.The Aesthetics of Surveillance - this phrase is still a good way to picture what is now possible and how some people use such technologies to produce their own "aesthetic" of being. Recall that the word "aesthetics" is derived from the Greek word esthetics (εστιξις) which means "perception." So, an aesthetics of surveillance means seeing how one can be productive while looking at what others are doing - and vice versa.

The distribution and production of images have always been a part of capitalism. Such images are not just used by humans as a means of product representation, however, but also can be used as an agent in the process through which capitalism is able to produce new markets/markets. In other words, images can not only be used to present oneself as others desire you to be but also make money for those saying or producing them (cf. Chapter 7).It cannot be as meaningless as some would say (the public who don’t read software agreement fine print) and as meaningful as AI anti-bias advocates report. Even describing the kind of mixed biological and non-biological ordering to include is a project unto its own - and would require a drastic remapping of hierarchies that we biological beings, limited by thousands of years of ideological programming, are just not equipped to accept.

In the current moment in time, the only ethical response to this technology is some form of reflexive anthropocentrism - which doesn’t really mean anything (because it’s probably as meaningful as non-reflective anthropocentrism) but if it means paying careful attention to the properties of one’s cultural system then it is a valid response.

‘This article explores rectangle evolution and how it came to be such a universal shape that can be seen everywhere.’ Blucher, David A & Dana Cavallo. Designing for Behaviour Change: Interactive Design Principles for Successful Social Change . (New York, NY: New Riders, 2012) The first use of the expression ‘Rectangle vs. the World” is found in page 4, footnote 9, where David A Blucher is describing the scope of his research: “My intent was not to identify a universal design solution for every rectangle but rather to explore the actual and potential uses of rectangles across many domains (e.g., travel, medicine, business, education, etc.) The rectangle vs. the world theme originated from my observations that rectangles are everywhere and can be used in many ways beyond their more traditional applications.’

‘The rectangle is an immense subject… It is a little difficult to take it all on. The rectangle contains more lessons for designers than I can possibly enumerate.’
Eckbo Style p. 45


People are amazed when they see a square in nature

DEFINITIONS

Rectangularity – Refers to the extent to which an image is square or rectangular. For instance, a square image may be described as rectangular since it has four main dimensions (width, height, depth and diagonal). How are these dimensions measured and how are they compared? Is one dimension more important than another? What other objects can be described in terms of squares and rectangles (incl. triangles?)

Machine vision – The ability of machines to sense light and interpret that data into computer data such as pixels.

REFERENCES

https://research.googleblog.com/2017/08/creating-3d-metro-maps-with-deep.html

1.Bhanu, M., and Pollard, A. (2010). "A Biological Approach to 3D Scene Understanding." International Journal of Computer Vision 86:7–25

http://arxiv.org/abs/1008.4480

2,Bhanu, M:Image Processing using biologically inspired deep learning models, Phd Thesis, University of St Andrews (St Andrews) 2012 , http://hdl.handle.net/10023/5087

http://trace.tennessee.

https://www.rdmag.com/articles/2007/08/google-earth-is-watching

https://youtu.be/wk3JtAyIb_w

https://www2.warwick.ac.uk/library/view?reference...

http://www1.uwm.edu/~nmahan/publications/documents...

http://rolandguillaumiezdumaingiroffrereaglucadamassera....

https://www2.warwick.ac.uk/library/view?reference...

http://www.forbes.com/sites/tomwills/2013/12/03/...

http://www.dana.org/news/cerebrum/?s=visual+cognition

https://www2.warwick.ac.uk/library/view?reference..

http://www.cbsnews.com/news/googles-earth-reaches-new-heights/

https://en.wikipedia.org/wiki/Machine_vision

https://en.wikipedia.org/wiki/Image_processing

https://en.wikipedia.org/wiki/Computer_vision

https://www.mathworks.com/help/images3D/#images3d_transformations...

http://www1.uwm. edu/~chibitze/papers/Chibeze73_Ahmed_An.pdf

http://www.visualtechnology.net/?p=973

http://scarsford.com/2012/1113-the-academic-imagination...

http://www.umassd.edu/newsletter/nlsnews/?subaction=tim...

http://eauburningsite.wordpress.com

http://ccompassmedia.org/?p=1066&lang=en

https://www.google.com/trends/explore#q=color+vision&z=0&tz=0&num=100&...

http://books.google.com/books?id=l38kAAAAMAAJ&pg=PA77&lpg=PA77...

https://books.google.com/books?id=MunYt1uj_04C&pg=PA3&lpg=PA3...

https://books.google.com/books?id=0iWFgJMe1KMC&pg=PA80&lpg=PA80...

http://www.wiley-vch.de/downloads/pdf/55_11/551143_3777700.pdf

http://robotics.stanford.edu/researchgroups/theoacm/papers2...

http://www.world-of-vision-project.eu/?q=node%2F19

https://webdocs

http://rolandguillaumiezdumaingiroffrereaglucadamassera....

https://www2.warwick.

Annnd here is a filter that pixelates humans:

https://twitter.com/kcimc/status/1266397393479561216?s=21

A Toddler at the Piano

Image created by author using starryai.com with the prompt ‘A toddler at the piano’ 

‘A toddler at the piano may hit a novel sequence of notes, but they’re not, in any meaningful sense, creative.’

Deep Dreaming with Nietzsche: Metaphysical Supplements

Bestiary 001. 2022, Davey Whitcraft. Film Still, dimensions variable. 


“Art is not merely an imitation of the reality of nature, but in truth a metaphysical supplement to the reality of nature, placed alongside thereof for its conquest.”
Friedrich Nietzsche, Thus Spoke Zarathustra

Doing a ride along in Google’s Deep Dream with Nietszche at the wheel sounds like a good time; what would the enfant terrible of philosophy say about Google Deep Dream and GAN-created art in general?

A possible starting point could be to look at GAN images through the lens of the above quote. If we take the first part of the quote ‘Art is not merely an imitation of the reality of nature…’ and assume that it refers to the ambitions of realism that painters of Nietzsche’s time aspired to. Painting during this period worked to recreate the aesthetic realities of nature, even when abstraction came in, they still worked to bestow a sensibility or essence of nature that was understood by scientists and artists alike.

Here we are left with the second part of the statement, which contains some heavy material to play with. So, let’s look more carefully at, ‘...but in truth a metaphysical supplement to the reality of nature’.

If ‘the reality’ of nature is contained in the study of physics, which are the constraints fixing us to earth, time and other annoyances, then a ‘metaphysical supplement’ might help to, at least temporarily, unbound us and allow us to transcend physical reality a bit. Nietszche’s ideas about Art included a kind of inverted Platonism, which regards a piece of Art as of higher value than the ‘reality’ of which it depicts, and in fact the further from reality the work gets, the higher its value soars.  I can definitely get on board with this idea, and can even see how a lot of Art has afforded these kinds of experiences. This concept seems almost tailor made for some types of Art made with AI.

“While dreams are the individual man's play with reality, the sculptor's art is – in a broader sense, the play with dreams.”
Friedrich Nietzsche, Thus Spoke Zarathustra

Nietzsche might have had some fun with the GAN-based ‘painting’ systems like Nvidia’s Gaugan 2, which allows the user to digitally ‘paint’ using paint brushes which are trained on specific categories of natural formations, like River, Flower or Tree. The image above was created using Gaugan 2 and my process involved painting the same simple line (an s-curve constrained exactly by the canvas boundaries, over and over, but swapping ‘nature skin’ paintbrush and rotating the ‘S’ by 90 degrees each time. The image contains some aesthetics from nature, but the forms and shapes are decidedly in the realm of impossibility.

“We have art so that we may not perish by the truth.”
Friedrich Nietzsche, Thus Spoke Zarathustra