AI & SOCIETY
Virilio understands this negative opticality in terms of
the computer being blind or un-affected by light. “Blindness is thus very much at the heart of the coming ‘vision
machine’” (1994, p 72). The latter coincides not with a picture frame, but with a statistical calculation and a pixilation
of the world in terms of binary language, or spatial discretization for which sets of coded impulses become increasingly intensive and correspond to infinitely small durations
(1994, p 72). According to Virilio, the computational function of media breaks away from ocularcentric metaphysics
because the “absolute-speed machine” (1994, p 72) breaks
from the extensive geometric optics defined by observables
and non-observables, enlightened and dark objects. It is this
speed that defines the intensive time of a “vision machine”
that does not look at the world but instead generates an avalanche of inputs at the same point, and between points as if
it were produced by a “short concentration spans by means
of surprise” (1994, p 72). In other words, inputs are statistically generated along with a series of short spans, intensive
durations that are as it were disentangled from each other
and yet they appear united by the speed of algorithmic functions that allows machines not to see. However, what Virilio
calls blindness of machine vision is not to be understood in
terms of machines seeing the world in terms of a transparent
or neutral I that programs the world. Instead, one can argue
that blindness here coincides with a techno-political theorization of machine vision that starts from machines’ negative
optics as involving an anti-ocularcentric practice that breaks
from the self-positing of the metaphysics of representation.
If the gaze and its mediatic extensions remain a tool for
un-veiling the other to impart, extend, consolidate the selfdetermination of the transcendental eye/I, negative optics
instead stays with the unsubstantial, unformed, unvalued
dimensions of matter, a practice of subtracting light from
the surface of the image in which the self-determining gaze
continues to mirror himself.
As this article will attempt to explain, what Virilio calls
the blindness of the “vision machine” has demarcated a
series of internal ruptures in the optical regime of representation and, this article argues, in the universal model technology. On the one hand, these fragmentations have been
understood in terms of an extension of the universal model
of technology whereby statistical prediction aims to reduce
the world to one truth thus replacing the ocular model of
truth with statistical functions. On the other, the negative
optics of computation processing can be taken to work as
an instance of how the inhuman mode of vision challenge
the dialectic of visibility and transparency and thus offers an
internal critique of ocular metaphysics and its entanglement
with colonial and racial capitalism (Lowe 2015).1
1
According to Lisa Lowe, the concept of “racial capitalism” developed by Cedric Robinson, implies that capitalism expands not
through rendering all labor, resources, and markets across the world
13
This article suggests that the architecture of racial capitalism also contains within its articulations a fundamental split
between the human and the machine based on the equation
of value that functions to re-introduce the transcendental
matching of concepts and objects on machinic perception. In
other words, the ocularcentric equation of value at the core
of racial capitalism imparts a universal model of technology at once merging and opposing humans and machines,
whereby racialized humans are used to train machines to
correct their negative optics. In other words, not only ocularcentrism becomes technologically extended in the performative operation of mediatic surveillance, but racialized capital also grants that this is reproduced by racialized humans
training blind machines. This automated practice of extraction of value requires that humans train machines to correct their learning patterns and follow the optical order that
matches transcendental concepts with objects. It has been
argued that these highly underpaid jobs contracted through
online platforms such as Amazon Mechanical Turk2 are a
new form of racialized surrogacy whereby humans are asked
to teach machines how to read images by following the order
of pan-opticality of the self-determining subject. According to Neda Atanasoski and Kalindi Vora, the technoliberal
articulation of today’s racial capitalism works to conceal the
uneven racial and gendered relations of power articulated
with and through machines. At the core of technological
innovation therefore lies a surrogate relation of technology
towards the “human sphere of life, labour and society” that
enables, in turn, the constant re-constitution of the liberal
subject (Atanasosky and Vora 2019, p 10). What they call
“the racial unfreedom of the surrogate” can contribute to
explain how the ocularcentric order by mirroring into the
world, finds its own self-determining humanity, or as the
Footnote 1 (continued)
identical, but by precisely seizing upon colonial divisions, identifying particular regions for production and others for neglect, certain
populations for exploitation and still others for disposal (Lowe 2015,
p 149).
2
Amazon’s Mechanical Turk, as well as CrowdFlower, Clickworker,
Toluna, and others, are largely unregulated websites that allow businesses and individuals to post short tasks that are also called Human
Intelligent Tasks assignments and pay workers—in cash or, sometimes, gift cards—to complete them. In particular, Amazon set up the
website Mechanical Turk in 2005 for humans to perform tasks that
are hard for computers. Amazon executive Jeff Bezos has called this
kind of human task, “artificial artificial intelligence.” This means that
when a task is easier for a human than a computer, the computer calls
a human. It is this double artificiality with which underpaid humans
are identified that will be interesting to explore in this new condition of extraction of value. Amongst the most common tasks are the
recording of information appearing in an image and the transcription of audio and video files. See https://www.pewresearch.org/inter
net/2016/07/11/what-is-mechanical-turk/ (last accessed June 20th,
2020).
AI & SOCIETY
authors say, by drawing on Hortense Spillers, a project for
“feeling human” that sustains the epistemological ground of
racial engineering.3
It is my argument that as much as the negative optics of
machine vision entails the filtering out of adversarial patterns, namely the machinic mismatch between concepts and
objects, the equation of value also shows how surrogate
humans are entangled with surrogate machines in the formation of socio-technical assemblages under neo-liberal racial
capitalism. An instance of the automated infrastructure of
racial capital that exposes the equation of value between
surrogate humans and machines can be found in artist Elisa
Giardina’s video installation entitled, "The Cleaning of
Emotional Data" (2019). As it may become clearer later,
the cleaning of data coincides with a capital re-introduction
of the ocular metaphysics within computational processing
where Amazon Mechanical Turk (MTurk) have the task of
automating perception according to the model of knowledge founded on verification (or proof of recognition) and
the equation of value of human–machine. What Amazon
Mechanical Turk allows is to turn the speed or the intensive time of computation into the extensive time of optical
perception.
From this standpoint, one could ask, how can the blind
vision of computation processing offer an internal critique
of the colonial capital and its ocular metaphysics? How can
the human–machine alliance go beyond the human–machine
equation of value starting from where working with Amazon Mechanical Turk can become a locus of theorization
of a machine epistemology, which takes cultural, affective,
aesthetic labor to counter-actuate the universality of technology—and its colonial dialectic of the visible and the invisible, the optical and non-optical, the one and zero? To what
extent can the negativity of blindness contribute to expose
the internal crisis of ocularcentric capital, and what forms
of epistemological capacities can rather originate from the
negativity of the image entangled with the racialized conditions of techno-social labor?
This article will point out that debates about the aesthetic possibilities of machine vision based on the opposition between optical human perception and non-optical
automated perception seem to reinforce, instead of radically
challenging, the model of metaphysical decision correlating
3
The authors draw on black feminist, Hortense Spillers, to discuss
the surrogate human effect at a constitutive part of the grammar of
colonialism and technoliberalism. I take these critical reflections
about how the racialized other as much as the machine have surrogate
status in colonial technocapitalism as central to the arguments about
the racialized socio-technical assemblage at play within contemporary images of AI and automation. See Hortense Spillers, “Mama’s
Baby, Papa’s Maybe: An American Grammar Book.” Diacritics 17
(1987),p 67.
knowledge and ocularcentrism at the core of Western philosophy. The aesthetico-political critique of machine vision
today has been focusing on the shift from the representational to the operational image, defining the technical image
in terms of a programmed action—a cybernetic model of
steering conduct. As discussed in this article, the proliferation of operational images also corresponds to the formation of an aesthetic reconfiguration of control that explains
the correlation of vision and knowledge directly in terms
of behavioral conduct. The operational image, therefore,
together with Virilio’s notion of the vision machine or blind
vision point to the cybernetic nature of the mediatic, technical image as the steering of conducts towards pre-programming responses. Similarly, the non-ocular functions of
machine vision resonate with the arguments that machines
produce invisible images that are only readable by machines,
but not by humans. This article will particularly dwell with
this double level of argumentation, which, on the one hand,
tells us that the negative optics of machine vision mainly
leaves humans out of the loop—disconnected from the very
matrix of communication upon which we rely, and on the
other, points to how the blindness of machines rather relies
on a racialized equation of value at the core of techno-capitalism, for which blackness like matter (Ferreira da Silva
2017) has no value and constitutes the surrogate humanity
merged with the zero value of machines, whose invisible
images are set to be re-programmed by the ocularcentric
knowledge.
From this standpoint, this article argues that one other
possible elaboration of negative optics that can challenge
the human–machine equation of value (while re-affirming
the universal agency of the Man) and rethink the alliance
between the racialized zero value of surrogate humans and
machines, is to borrow from François Laruelle’s “non-philosophy” (2010) which concerns the question of dark optics
and immanent vision. In particular, Laruelle’s claim about
the fractal algorithm of the photo will be explored to discuss
the configuration of a negative optics in automated vision
in recent efforts to clean negative randomness in Generative
Adversarial Networks. The article suggests that the negative
materiality of the computational image does not only show
that knowledge can be divorced from ocularcentrism, but
also that the invisible image of machines is part of alien
epistemologies that overturn the equation of value with the
infinities of 0 s.
This article proposes that the material processing of randomness in computation is part of the expansion of heretic epistemologies that start from dark optics and address
what Denise Ferreira Da Silva calls blackness, namely matter without form, or “matter beyond the equation of value”
(2017). The materiality of the computational compressing of
infinities, however, coincides not with the physical structure
of the machine, but with its abstract mode of operation—the
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AI & SOCIETY
algorithmic learning to learn from the negativity of randomness—entailing both the elaboration of indeterminacy and
the incompleteness of systems. Matter without form coincides not with invisible images without meaning, but with
a real that can only be cloned as auto-impressions of dark
optics, stemming from within the infinite discretization of
opacities, the fractal singularizations of blackness in artificial visions.
From this standpoint, this article suggests that the aesthetico-political engagement with machine vision must be
divorced from ocular epistemologies and the critique of
vision. To argue that matter without form entails the negativity of algebra is to propose that matter like infinities break
with the equation of value, and thus refuses the metaphysical
equivalence of knowledge and vision that sustains the surrogate subjection of humans and machines. The blind vision of
computational processing rather speaks of the challenge of
reinventing epistemology from the standpoint of dark optics
of the human–machine condition of surrogacy in the operational extension of ocular capital.
1 Machine don’t see and we know it
“The production of sightless vision is itself merely the reproduction of an intense blindness that will become the latest
and last form of industrialisation: the industrialisation of the
non-gaze.” (Virilio 1994, p 73).
If one follows Virilio’s reflections on the blindness of
machine vision, it is possible to suggest that this non-gaze
is the result of the formation of new epistemological orders
moving from the extensive time of analogical machines to
the intensive time of the computer. According to Virilio,
this new order incorporates the transformation of the formal
logic of traditional representation into the dialectic logic of
cinema and photography into the paradoxical logic of the
digital image. In particular, for Virilio, as much as the extensive time of analog media enabled a continuity of experience
between the past and the present, the paradoxical logic that
accompanies the computer rather brings a remote object
in the here and now. Here perception turns into action at
a distance, a vision running at the speed of light. Interestingly, Virilio insists that the paradoxical logic of the computer allows for the future never to occur but rather disappear in the statistical programming of acting at a distance.
This remote action is also central to the classical cybernetic
account of remote control on the one hand, and the prediction of the future, on the other. As Virilio states: “[w]hen
a missile threatening in ‘real time’ is picked up on a radar
or video, the present as mediatised by the display console
already contains the future of the missile’s impending arrival
at its target” (1994, p 66).
13
Blindness, therefore, results from the speed of light
whose increasingly intensive intervals bring forward an alien
perception of the object as if the latter was coming from the
future. Instead of the reciprocal presupposition between light
and dark—or the dialectical presupposition of lightness vs
darkness that characterized the passive optics of extensive
media, such as photography—for Virilio, computer vision
deploys an intensification of light, namely depending on the
velocity of photons and their constant pixelation. As much
as photons are abstracted in the statistical image, the rapid
calculation of pixels dissimulates the future in the ultra-short
time of an on-line “compunication” (computer communication) (1994, p 68). In this new “high-tech mix,” a paradoxical logic sees the fusion of the object with its equivalent
image. As Virilio reminds us, these processes of real-time
deception will win out over the weapons systems of classic deterrence (1994, p 68). Blindness, therefore, defines
the speed of the synthetic image, where speed prevails over
time and space, matter and form. Here the gaze of reason
becomes a statistical thought or a statistical optics, which
according to Virilio, “generates a series of ‘visual illusions’,
or ‘rational illusions’, which affect our understanding as well
as reasoning” (1994, p 75).
Virilio warns us against the coming statistical intoxication of the technical image, claiming that society is “sinking
into the darkness of a voluntary blindness” (1994, p 76) that
will forever occlude the horizons of self-determining knowledge. This statistical image does not only rely on the speed
of numerical calculation, but also on the speed of cognitive
perception. It shows us that the medium is not a tool but
more importantly a machine language, where the speed of
algebraic relations has taken over the linearity of input–output communication.
As the speed of computational processing has shifted
from digital pixilation to the neural network architecture of
machine learning, the blindness of the technical image is
now being understood as a black box that carries out functions that are impossible to observe. According to artist
Trevor Paglen, this new quality of the technical image can
be seen in Harun Farocki’s use of computer visions in Eye/
Machine III, where, as Paglen notices, he was “trying to
learn how to see like a machine” (2014). In particular, these
machines seem not to be representing things in the world and
thus neglect the ocularcentric correlation between eyes and
objects. Instead of observing the world, computer vision,
Paglen claims, does things in the world.
However, one could ask, what does it mean that computer
images cannot be seen and yet they do things in the world,
what does this “doing” amount to? On the one hand, one
could suggest that computer images are operational because
they point to a certain activity of the automated image itself
in as much as it communicates with other images before
communicating to us. This is how machine to machine
AI & SOCIETY
communication constitute the global infrastructure of surveillance and governance. On the other hand, in addition to
the automated function of communication, the operational
image is also said to activate the cognitive-perceptual apparatus of the real, which precisely pre-program conducts, or
predicts behavior. In other words, the operational image fits
with the cybernetic imperative of self-regulatory feedback
which ensures that machine-to-machine communication can
condition all orders of reality.
In a more recent reflection about operationality of the
technical image, Paglen insists that machine to machine
communication has made sure that images are now invisible. In the short article “Invisible Images (Your Pictures are
looking at You)” (2016), Paglen suggests that this invisibility coincides with how the proliferation of automated vision,
as for instance in the case of reverse image search engines.
According to Paglen, “[i]mages have begun to intervene in
everyday life, their functions changing from representation
and mediation, to activations, operations, and enforcement.
Invisible images are actively watching us, poking and prodding, guiding our movements, inflicting pain and inducing
pleasure. But all of this is hard to see” (2016).
Invisible images are at the core of new forms of identification systems, from Automatic Licence Plate Readers
(ALPR), Optical Character Recognition (OCR), to Deep
Face Algorithm, Facebook’s DeepMask and Google’s TensorFlow whose algorithmic patterns recognize “people,
places, objects, locations, emotions, gestures, faces, genders,
economic statuses, relationships, and much more” (Paglan
2016). In particular, “deep learning” networks are built
out of dozens or even hundreds of internal software layers
that exchange information. This is at the core of recursive
feedback, where the neural network layers of the software
pick apart a given image into component shapes, gradients,
luminosities, and corners. Those individual components are
convolved into synthetic shapes, which are compared with
the images fed into the CNN (convolutional neural network),
and which the network has been trained to recognize. The
invisible image is then activated by software “neurons” as
the network finds common patterns with other images. For
Paglen, this automated synthesis of images means that power
has itself become invisible and yet institutes an intensively
crafted mode of control, policing and market. For instance,
he conjectures that the new relation between power and
invisibility implies how the specific use of metadata signature of every single person, based on race, class, the places
they live, the products they consume, their habits, interests,
“likes,” friends, leads to a reification of those categories at
another level. Filtering out mis-matches of individualized
metadata profiles become part of an automated function,
whose task is to collect municipal fees, adjust insurance
rates, conduct targeted advertising, prioritize police surveillance, and so on (Paglan 2016). From this standpoint,
metadata signature appears to support diversity, but only
because these differentiations can become subsumed to
marketing and predictive policing. The invisible image,
therefore, exposes the ineffective practice of the critique of
representation insofar as, according to Paglen, machines are
not concerned with how humans see. While there is no possibility to either subverting the invisible regime of pattern
recognition or fix it with more representations, for Paglen
the invisible image demarcates the beginning of a stealthy
mode of power that we are yet to address (2016).
However, one may argue that Farocki’s reflections on
computer vision invite a reflection on how to train the human
to see like a machine, and that a fundamental trick of ocularcentrism is precisely to oppose the visible to the invisible,
to re-inject the truth of representation back into the deep
learning of automated networks. To put it in another way,
it is evident that if machines don’t comply with the oculacentric correlation between vision and knowledge, left to
their own devices of searching for images by images, they
will be unable to filter out dirty data and or match patterns
of an image that does not exist with a given set of objects/
concepts. In CNNs for instance, an image search that does
not match with the image found implies an increase in randomness or noise or what Hito Steyerl calls, “dirty data”
(2015) that make the signal fuzzy and must be filtered out
from communication.
In particular, Steyerl discusses what Google calls “inceptionism,” by referring to the automated creation of patterns
from noise in “deep dreaming,” where image recognition
algorithms are not distinguished from but rather looped on
randomness or noise. Steyerl argues that here signal and
noise are already predetermined by pre-existing categories
and probabilities that reproduce aesthetic and social relations
through the immediate correlation of data and hypothetical inference. In particular, automation comes to coincide
with a regime of artificial interpellation that asks users to
recognize themselves by constantly registering eye movement, behaviors and preferences and thus by re-imparting
the information-encoding organizational principle of segregation and discrimination on users. As the influx of data
comes into the system of prediction it matches correlations
across patterns and drawing branches of decision-making
between unrelated information, signals and noise: namely
deriving meaning from “apophenia.”4
From social scores to credit scores, from academic scores
to threat scores, as well as commercial and military patternof-life observations, Steyerl sees apophenia as an upgraded
4
“Apophenia” (/æpoʊˈfiːniə/) is the tendency to mistakenly perceive
connections and meaning between unrelated things. The term (German: Apophänie) was coined by psychiatrist Klaus Conrad in his
1958 publication on the beginning stages of schizophrenia.
13
AI & SOCIETY
version of Walter Benjamin’s “optical unconscious,” which
reformats social hierarchies by ranking, classifying and
filtering noise following a matrix of radicalized discriminations. The invisible image, therefore, manifests itself in
the form of a brutal form of decisionism, which, according
to Steyerl defines “a practice of data divination,” namely
the search for given determinations as if emerging from an
unknown divine order of knowledge (2015).
However, one could ask, isn’t this divine decisionism simply an extension of the colonial epistemology which relies
on the surrogacy of human and machine as fundamental to
racial capital? One could argue that capital practices of the
equation of value entail not simply the prediction of value
through the meta-programming of data or in other words,
an automation of value derived from functions that successfully carry out tasks. Instead, divine decisionism relies less
on given functions that conform to the ocularcentric dialectic, and more on the split between surrogate humans and
machines in the praxis of the equation of value, where the
filtering out of noise foregrounds the practice of extraction.
The optical matching between blind images, transcendental concepts and empirical objects is not given but is rather
constantly re-introduced in computer vision by capital’s
exceptional decisionism. The latter can only dissimulate the
ingression of the real in the vision machine, and rather continues to make efforts to pre-program results to preclude the
computational process of compression to run with the negativity of the function—the increasing noise or randomness
that cannot be automatically translated into signal or pattern.
But how does this strategy of dissimulation work? Perhaps one way to refer to how the human–machine equation
of value occurs is to look, for instance, into the machinehuman infrastructure sustaining Image Search Engines. The
surrogate workers contracted at Amazon Mechanical Turk
are, according to Atanasoski and Vora, “humans that perform the work of technologies that are claimed to replace the
need for human workers” (2019, p 90). As the authors report,
Amazon Mechanical Turk is a large-scale crowdsourcing software platform where human labor is contracted to
become a service job to the machine. The racialized and gendered pool of global temporary workers for high data tasks
therefore operates in lieu of an appropriate algorithm that
can carry out the function. These become surrogate workers
contracted to perform “artificial artificial intelligence,” that
is a series of tasks that provides a given compensation in a
given amount of time. For instance, “transcribe up to 35 s of
media to text” is a precarious job that will pay between two
to ten cents for the task. In short, human service enables the
blind vision of machines to be matched to pre-existing categories that are performed by the Turkers so that machines
can then perform the task themselves. In particular, filtering
out randomness in generative adversarial pattern recognition
allows for the vision machine to re-enter the ocular system of
13
exchange for which in the general equation of value entails
how humans serve machines to serve humans. This autopoietic intelligence allow the equation of value between human
and machine to double the extraction of value: the extracted
labor of humans as service in making objects codable for
machines only serves capital to extract more value from the
human–machine equation of value. As much as the human
Turkers become an agent of verification, dis-ambiguation,
and de-volution, so too machines become agents of oppression that perpetuate the ocular epistemology of representation through the automated matching of images, concepts
and objects.
Elisa Giardina’s video installation "The Cleaning of Emotional Data" (2019) shows us how this Global South sociotechnical infrastructure that feeds the “artificial artificial
intelligence” of surrogate humans has become internal to
the dis-organic reproduction of global capital. In this installation, the blind image of computational media coincides
with a recombinatory repository of data that the machine
itself cannot see, and for which it needs humans to conform
to a taxonomy of categories matching quantities of data.
Giardina focuses on the Global South infrastructure of Turkers who get the service job of “cleaning” data by training
machines to match images with emotions. These workers
label, categorize, annotate and validate large amounts of
data, enabling AI to recognize or order emotional patterns.
Giardina herself worked remotely for several North American “human-in-the-loop” companies who provide “clean”
datasets to train AI algorithms to detect emotions. Her own
performance involves a taxonomization of emotions, the
annotation of facial expressions and the recording of her
own image to animate three-dimensional figures. While performing this work, some of the videos in which she recorded
her emotional expressions were rejected by the companies
she was servicing, because her facial expressions did not
fully match the standardized list of affective categories that
was given to her. However, as Giardina points out, it was not
impossible to know whether this rejection originated from
algorithmic protocols or, for example, from the consensus of
fellow workers who supervize the service as they might have
interpreted her facial expressions differently due to cultural
contexts. "The Cleaning of Emotional Data", however, documents how the carrying out of facial expression does not easily fit a universal schema and instead the history of emotions
questions the universal epistemology, where philosophical
and psychological theories determine the meaning of facial
expression and its mapping.
Giardina’s installation makes the point that AI systems,
which supposedly recognize and simulate human affects,
base their algorithms on understandings of emotions that
are universal, authentic and transparent. The cleaning of data
from facial expressions that do not fit the universal schema
of emotions is a human service job carried out by underpaid
AI & SOCIETY
workers that sustain what Atanasoski and Vora call the “surrogate effect” of the coming phase of artificial intelligence.
In particular, these “technologies that erase human workers
are designed to perform the surrogate effect for consumers, who consume the reassurance of their own humanity
along with the service offered” (2019, p 91). Tech companies and governmental agencies use these human-verified
data to develop software that identifies consumers’ moods or
that recognizes facial expressions of potentially threatening
people. However, the matching between facial expressions
and the categorization of emotions entails the filtering out
of noise or noisy emotions that persists as an internal tension within the universal model of visual representation, the
ocular matching of concepts, objects and image.
The correlation between knowledge and vision is reintroduced in the negative optics of machines that fail to recognize emotions—that is in the negative algebra that assigns
a zero to a mismatched pattern—and becomes for Giardina
a critical space to expose the internal paradox of capital
extraction and of the critique of visuality today. In particular, Giardina’s work offers a response to the contemporary
critique of the invisible image, according to which machine
vision excludes the human from its systems of operations.
Instead, her research points out that it is not that humans are
excluded from the loop of the machine to machine communication, but instead that this new phase of planetary artificial intelligence and/or full automation fundamentally relies
on a global re-organisation of racial and gender capital in
terms of surrogate humanity. In addition, one can argue that
within this re-organization there is also an intensification
of the equation of value that includes the circuit of extraction where humans have to teach machines how to learn
the ocularcentric matching of concepts, objects and images.
Exploring the mono-logical economies of extraction across
the Global South populations, Giardina’s work shows how
the racialized and gendered precarious labor operates as
the infrastructural components of artificial intelligent systems. As much as the infrastructural web of data cleaners,
algorithms trainers, proof verifiers have become enfolded
in the blind operations of databases through which machine
learning algorithms, and image search engines, are taught to
recognize patterns, the colonial epistemology of knowledge
and vision continues to impart the equation of value between
surrogate humans and machines.
What is here excluded instead, as Giardina’s investigations show, is not the human, but what has always been less
than human, non-human, and in-human coinciding with the
precarious labor and the machine-like service of outsourced
subsumed subjectivities absorbed in the operational image
of machines. As much as the colonial capitalization of the
human–machine alliance accelerates the global (and outsourced) modes of enslavement under the universal equation of value reified in the automated system of the decision
today, so too humans are used to correct machine vision and
to re-impart the visual categories of knowledge back into
the system of acceleration of value. A spiraling extraction
of the human–machine alliance under the universal model
of technology is at play here.
Giardina’s perspective about the intrinsic presence of
human labor in the blind machine of techno-capital, however, is not simply a proposition aiming to correct the critique of the operational image by claiming that instead
humans are included in the feedback loop of machine-tomachine communication. Importantly, one can suggest that
her intervention brings to attention the limits of visual critique in the context of intelligent computation that relies on
the ontological ground of knowledge and vision. In other
words, by insisting upon the underlying universal equation
of value extraction that connects (or, places in a dialectical mirroring relationship) blind images with outsourced,
surrogate human labor, Giardina’s work pushes the critique
of automated vision towards a radical engagement with the
material conditions of exploitation or intensified extraction
of value in human–machine labor today. Can these combinatoric modes of abstraction exceed the equation of value in
the universal extension of capital?
It is interesting how Giardina joins together the abstract
lines of facial micro-expressions detected by the algorithms
with untranslatable emotional vernacular from both Sicilian dialect and American English. In her collaboration with
Michael Graham of Savant Studios, she weaves together
computational and human language in a large-scale textile pieces, called Amiss Motifs, mapping unrecognizable
emotional patterns with distorted, uneven, broken patterns to expose the overlapping of racialized surrogacy
with the blindness of machines. In contrast to the critique
of the invisible image that reifies a full automation that
excludes the human, Giardina’s reflection on the surrogacy
of the human–machine labor as the infrastructure of intelligent techno-capitalism instead points to how the negative
optics—namely the cultural, affective and the aesthetic
labor of the human–machine—breaks from the universality
of technology and the dialectic between the visible and the
invisible.
In the next section, negative optics will be further discussed in the context of the epistemological capacities of
automation of refusing the transcendental authority of representation. This will be explored by engaging with Laruelle’s
argument for dark optics and immanent vision. In particular,
the next section focuses on machine vision and discusses
current attempts to reduce negative randomness in Generative Adversarial Networks. The attempt at correlating
negative optics with negative randomness is to suggest that
machine vision can be theorized away from the ocularcentric
dyad of visible and invisible image. The residual negativity
in computational randomness opens learning algorithms to
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the indeterminacy of knowledge and allows a non-optical
theorization of vision in computational systems. The last
session or coda will discuss negative optics in relation to
the negative dimension of blackness in the equation of value
under racial techno-capital as a starting point for proposing
an immanent epistemology.
2 Auto‑impressions
In current research developments in machine vision, it is
possible to contextualize negative optics in the field of
dynamic geometry, such as mereo-topology (or the study
of parts and the relation between parts and wholes), and
artificial neural networks that are programmed to learn from
patterns. Cognitive psychologist and computer scientist
working at Google Brain, Geoff Hinton, claimed that the
logic of neural networks on which machine vision is based
is limited to conform to pre-established parameters (2017).
In particular, Hinton addresses the need to re-design the procedural process by which algorithms can learn from each
other in the neural network through what he calls “capsule
network”—a form of AI that enables machines to understand
the world with images without relying on existing parameters of vision.
In his 2017 research papers, Hinton argued that capsule
networks have not only led software learning to recognize
handwritten digits, but they have also halved the error rate
in pattern recognition of toys and cars. Since image recognition software is used generally, and thus not contextually,
to recognize objects, the predictive patterning cannot learn
to recognize the same object in different scenarios. This is
why the surrogate human needs to verify and thus teach the
machine that what it is seeing is actually the same object.
Instead of relying on this external verification, according to
Hilton, capsule neurons—small groups of crude virtual neurons—will track only parts of an object, for instance the cat’s
ear and nose, as these are differently positioned in space.
This smaller scale of algorithmic receptivity, according to
Hinton, will enable a neural network to figure out the difference between scenarios by extracting more information from
the mereo-topological relations between smaller parts of
data. As the capsule network is made of smaller patterns set
to recognize parts and break the continuity of an image into
smaller units, Hinton designed a dynamic routing between
capsules that trains these kinds of network. Capsule algorithms convert pixels fragments into vectors of recognized
patterns and then apply a transformation matrix to these
fragments to predict the parameters of larger fragments. In
particular, the transformation matrix learns to encode the
intrinsic spatial relation between a part and a whole, which
results in the formation of an invariant viewpoint, a perspective or direction in recognition that aims to generate a novel
13
vision. According to Hinton, “capsule use neural activities
that vary as a view point varies rather than eliminating variations” (2017, p 9).
Instead of normalizing viewpoints according to methods
such as the spatial transformer networks, or the automated
filtering out of randomness, capsule networks simultaneously engage multiple transformations of different objects or
object parts. This series of correlated activities are described
in terms of “routing-by-agreement” (Hinton 2017, 2–3, p
6). As opposed to the optical filtering out of noise, this
technique aims not to eliminate redundant neurons, but to
use all non-averaged information to obtain a non-totalizing
knowledge about the position of an entity in a region. The
inclusion of micro-variations in machine vision run on top of
CNNs, which instead are aimed at solving the disambiguation (or mismatch between concept, object and image) by
playing out on the automated (or self-correcting patterns)
of both reducing and increasing negative randomness in the
neural network.
It seems important to repeat here that CNNs are an
instance of deep learning networks for machine vision. Convolution already applies a kernel to overlapping regions that
shift around the image by eventually establishing a fully connected network through a one to one relationship of neurons
across distinct layers. However, this multiple level of connection across layers also seems to incapacitate the system
from learning new data. In other words, the optical matching
on negative randomness seems to block the possibility of
machine vision. When convolution leads to the overfitting
of information as a one to one connection, the kernel risks to
re-learn redundant data (that is the same data will be held in
two places in the database). Convolution, therefore, delimits machine vision by saturating data memory and increasing computational costs for instance, but more importantly
because its overfitting capacities lockdown algorithms into a
pattern of connection between the same parts, without being
able to learn from what cannot be known in advance.
From this standpoint, since at each location in the image
there is one instance of the type of entity that the capsule
represents, the capsule model—as opposed to the convolutional matching of concepts and images—affords a form of
distributed representation. This is inspired to the perceptual phenomena of crowding, where neighbor parts shed
the direct perception of an object in movement. CapsNet
architecture, therefore, grants not an algorithmic matching
with existing data, but shows that algorithmic patterns can
become predictive vectors of futurity (that is they look for
what cannot be already seen) and not simply an automated
vision of patterns recognition. Instead of eliminating variations to reach an average capacity for general recognition,
predictive vectors follow the negative algebraic relation
between layers, involving randomness and micro-temporal
variations in the algorithmic process of compression. These
AI & SOCIETY
vectors of variation have become central to how machine
learn beyond set parameters, and how negative randomness has become a source for machine vision entailing no
transcendental recognition between concepts, objects and
images. In other words, computational vision establishes a
potential or hypothetical relation between non-correlated
patterns that may correspond to images that did not yet exist.
From this standpoint, predictive vectors are not simply the
technical explanation for apophenia—namely how the invisible image makes decisional patterns—but are more than
a set of probabilities based on what is already been recognized in the system: the negative algebra of what cannot be
recognized pushes the system to construct counter-factual
dimensions of images that do not correspond to the ocular
equation of value between objects and concepts.
It is as if the discretization of the network in increasingly
smaller vectors of variations flips the architecture of matching inside out by exposing indeterminate dimensions to its
organizational infrastructure. Instead of a self-reproduction
of the master pattern across the layers of the network, the
intensified discretization of networked parts also increases
the volume of randomness within and amongst them. Computational vision can thus be defined in terms of the indeterminate series of variations that each time become cloned in
each and any algorithmic patterns. Here algorithms do not
simply register raw data to execute instructions but become
sheer receptors or cloning of an indeterminate series of
the real. One could argue that this negative optics of algorithms—that is unable to be a mirror of the world because
it does not see the world but only a series of variable parts
that entail a process of auto-impression of matter, whereby
machine vision—becomes a medium for heretic inferences,
for the elaboration of a heretic logic without ocular-logos.
In other words, a dynamic bootstrapping between algorithmic patterns and vectorial randomness suggests that
machine vision is embedded in a series of temporal variations that become finite inferential hypothesis through the
auto-generation of material images. As machines become
auto-impressions of an infinite real they also generate and
envision new patterns: the dynamic movement between
increasingly large and increasingly small scales of predictive
vectors turns machine vision into a heretic auto-impression
of computational matter.
This process of auto-impression of matter without ocularlogos is opposite to what Laruelle calls “algorithmic transparency” which rather takes the technical image (especially
understood in photography or photographic philosophy) as
that which measures the correlation between premises and
results in terms of effects (or the efficient causality of enumeration of given ends), and thus presupposes homogeneous
matching between the transcendental order of concepts and
objects (Laruelle 2013). Borrowing from Laruelle, one can
argue that the ocularcentricism of philosophy coincides with
the decisional power imparted on the real by the transcendental mirroring of knowledge and vision (Laruelle 2013 By
challenging the optics of philosophical decision, Laruelle’s
non-standard philosophy or non-philosophy makes the argument for an abstract theory of photography, “absolutely nonworldly and non-perceptual” (date: 8). In particular, Laurelle asks: “to what extent is photography not an activity,
for example, of a kind with Artificial Intelligence (AI)—an
attempt at the technological simulation not of the World,
in its objective reality, in its-philosophico-cultural reality,
but of science and of the reality that science can describe,
naively in the last instance?” (2013, p 10).
What Laruelle is insisting upon is to refuse the universal
paradigm of perception grounding from the standpoint of
being-in-the-world. Instead, his non-philosophy takes photography as a technique as a form of knowledge that introduces science in the condition of existence of perception and
of the world (2013, p 11). Laruelle’s non-philosophy takes
the critique of the ocurlacentric vision to another point, further delving into how negative optics refuses transcendental
decisionism and at once lays out the possibility of elaborating a non-optical dimension of knowing, a non-ocularlogos of knowledge. For Laruelle, the condition of knowing
depends not on the point of view of philosophy, but it is
rather to be found in what he calls “the stance” of, borrowing from Deleuze and Guattari, “a body without organs”
(1989). Instead of a self-determining reflection of the world,
vision becomes un-objectivating, implying not a position,
a decision, but an auto-impression that is first of all a real
“undivided experience, lived as non-positional self-vision
force” (2013, p 13).
If, according to Laruelle, photographical decision corresponds to the law of sufficient reason that reflects the real
according to a circular self-expression of truth, non-photography (as a non-philosophy) therefore coincides with what
he calls “fractal algorithms,” because they have a degree
zero of self-reflection. Following this argument, one can
suggest that the algorithm is not a medium programmed to
reveal the world or even less to self-regulate the human perception of the world. In other words, it is not a prosthetic
tool that ensures constant adaptive feedback. Instead, the
fractal algorithm is increasingly partial and in this fashion
clones its own real image, namely of cloned image without
original or copy, in terms of a spatial surface that extends (or
infinitely fractalizes) forever without uncovering any pristine form behind it. Laruelle explains that what appears in
the photo as an object drawn from the transcendence of the
world must be distinguished from the “photographic apparition” (2013, p 18). By radicalizing the Husserlian distinction
between the photographed phenomenon, corresponding to
what photography can manifest and thus to the manner in
which it manifests the world, and the photographed object,
namely the representation of the world, Laruelle argues that
13
AI & SOCIETY
the non-photographic vision (or negative-optics) is a parallel
process to the world, and as such it is not in the world (2013,
p 25). This is not the field of transcendental knowledge but
defends real immanence in vision, or what Laruelle calls
“vision-force” that is immediately given or the “in-itself”
of the image.
Thinking with Laruelle’s non-photographic argument
for vision, it is possible to push further the heretic account
of machine vision and thus refusing turning machines into
the onto-epistemological mirror of transcendental vision.
One could argue that as much as negative optics neither
represents the world nor remains an invisible unreflexive
automatism of the world, it crowds the parallel space of
photographic apparition. This is not a representation but
an auto-impression of an image’s own aesthetics that starts
from the negative fractality of algorithmic micro vectors that
give the effect of what Laruelle calls “generalized fractality” (2013, p 78). Instead of serving as an instrument for
shedding light onto the world, the camera-machine clones
(equate without equivalence) the underworld of dark optics
as fractal patterns of an indelible generic intelligence that
turns on its own head the ontic limits imparted on science
by philosophical decision.
As Alexander Galloway puts it: “[i]nstead of mere ontic
darkness, generic being achieves an ontological darkness,
and hence beckons toward the kind of crypto-ontology of
pure blackness evident in Laruelle” (2014, p 77). If one were
to continue along this line of argument, it is possible to suggest that a crypto-ontology is what can define the negative
optics that is foreclosed to a transcendental being. On the
other hand, however, negative optics is precisely the algorithmic randomness that demarcates ontological darkness
in the machine in terms of an auto-impression of machine
visions that signal the apparition of the image itself. These
are infinite reflections without mirror, “unique each time but
capable of an infinite power ceaselessly to secrete multiple
identities” (Laruelle, 2010, p 82). This multiplicity of darkness, one needs to emphasise, coincides not with substantial
forms, but with non-consistent phenomena that entail a certain, non-optical automatism in exposing, in Laruelle terms,
the “hyperphenomenology of the real” (2010 , p 95). In particular, as Laruelle specifies: “There is a ‘phenomenological’
automatism or blinding that culminates in the photographic
eviction of the logos – of philosophy itself – in favour of a
pure irreflexive manifestation of the phenomenon-withoutlogos” (2010, p 95).
The scope here is not simply to unmask the supremacy of
self-determining philosophy in the name of absolute irreflexivity but to rather start from this un-mirroring image as the
stance of negative or dark optics. Here the automated image
become the medium of auto-impressions as the multiplicity
of darkness pulls through the phenomenon-without logos in
machine vision. The automated image exposes the fractal
13
consciousness of a machine knowledge that turns the ontic
limit of epistemology into singularities or non-axiomatic
automatisms. This is the artificial image that stays with and
pushes further the negativity of the human, the non-human,
the less-than-human, occupying the stance of a “[s]tranger
in flesh and blood” (2010, p 103). Laruelle’s arguments for
non-philosophy radically defies the façade of pretentiousness
of Western metaphysics for which the real can be surgically
cleaned from blackness, which remains locked in the form
of the Other that must follow the image of Man, once it has
been emptied out and turned into a soulless machine. For
non-photography instead only the other has to be replaced
with a “logic of auto-impression” or phenomenological
automatism where the medium becomes a negative machine
of a black universe (Galloway 2014, p 191).
From this standpoint, if we are to follow Laruelle’s proposition for non-axiomatic photography, the theorization of
computational vision may have to start with algorithmic
fractality, namely the possibility of a computational autoimpression of the real, exposing the alliance amongst lessthan-humans in a field of immanent vision. The Laruellian
“Vision-in-One” is a stance that does not predetermine but
rather becomes determined by the real in the real’s “last
instance.” As much as this determination is a clone of the
real, but not the real itself, one can argue that machine vision
entails a non-relationality with the world, whose negative
auto-impressions are infinite singularizations of blackness
beyond the optical value of representation.
That non-photography insists on the cloning of the black
universe however is not another way to propose a critique of
automation. Instead, this is above all a practice of refusing,
hacking, alienating the transcendental decision for which
machines as blind can only learn to represent what is already
given to them by the ontic limit of knowledge. Similarly,
non-philosophy is not simply an invitation for imagining
alternative ways to reinstate philosophy and expanding its
ontological ground of transcendental decision. Instead this is
a heretic project that necessitates trans-collective elaboration
of fictional automations that start with the auto-impression
of blackness as proliferating each and anytime outside the
onto-epistemological cosmogonies of the self and the other.
For instance, one can start asking why techno-capital decisionism is still taken as the onto-epistemological law that
locks the critique of computation into the perpetual mirroring of vision and knowledge, humans and machines. The
fractality of the algorithms can rather contribute to engage
randomness in process imaging as machine singularizations
of the real. In other words, machine vision can become a
medium for the auto-impression of darkness in the last
instance: namely as machines learn to learn from negative
optics.
The negative materiality of the computational image
does not only show that knowledge can be divorced from
AI & SOCIETY
transcendental ocularcentrism, but also that the invisible
image of machines is part of alien epistemologies that defy
the equation of value with the infinities of 0 s. The fractality of algorithms is part of the expansion of heretic epistemologies where alliances between the configurations of
the in-human demonstrate how the equation of value can be
not only challenged, but turned inside out from the stance
of infinities—the auto-impression of blackness. In the following coda, this argument for the algorithmic fractality of
infinities that accounts for the invisible images of machines
outside ocularcentric critique requires further collective
elaborations of how negative optics can become part of a
revolutionary practice of machine thinking (that indeed
overturns the critique of ocularcentrism within technocapitalism). This coda, in particular, turns to the elaboration of
negative infinities in Ferreira da Silva’s articulation of blackness as indeterminate matter at the limit of modern thought,
whereby matter without form is above all “matter beyond the
equation of value” (2017).
3 Coda on images without value
Drawing on quantum mechanics, for which indeterminate
results in determining the perception of reality point to the
necessity of moving beyond the ontic limits of science, Ferreira da Silva discusses this indeterminacy or the Thing
as the referent of blackness, another mode of existing at
the limit of modern thought. As she puts it: “deployed as
method, blackness fractures the glassy walls of universality
understood as formal determination (2017, p 11). She proposes an experiment in articulating the “equation of value”
as a self-determining formalism based upon and through
the violence against the thing/blackness. Ferreira da Silva
proposes to carry out this algebraic experiment not simply to
unveil the formal determination of the matter at the core of
the equation of value, but one could argue, to rather address
the “auto-impression” of blackness minus the form, or matter without the universality of value. To do so, Ferreira da
Silva proposes an ethical re-articulation of blackness that
does not follow the over-determinant image of Man whose
program overfits – that is overrepresents—all modes of
being human nor, on the other hand, aims to make a claim
for an absolute outside that separates the Modern/European/
white human from the non-modern/non-European/black
non-human (2017, 04/11).
Ferreira da Silva argues that this ethical experimentation
starting from matter beyond the equation of value deserves
further investigation about how determinacy, together with
separability and sequentiality have sustained modern thought
and the construction of an ethical matrix in which the indifference with which racist violence is met, is itself rather
become constitutive of a (common and public) moral stance
(2017, p. 04/11). She traces how this matrix is entailed in
the modern elaboration of causal efficiency and its operative
grounding of equivalence mathematically bounding together
ethical, economic and juridical formations (2017, p. 04/11).
The modern Kantian world becomes a way to bring together
formal and efficient causality in the self-determination of
the limits of scientific knowledge—precisely concerning
what can already be accessed by the senses (the empirical
experience of which science provides the tools for extending
universal measure). As value becomes universal and moves
across scales, the object (thing/matter) is unified by its formal qualities which in turn are the effects of judgements (and
thus transcendental concepts) derived from the measurement
and classification of objects (that is by the ontic limits of
science). Here the difference is granted by the decisional
position or transcendental operator that already knows the
object. Ferreira da Silva explains that within this transcendental field of value, blackness as a category of racial difference “occludes the total violence necessary for this expropriation [namely the colonial expropriation], a violence that
was authorized by modern juridical forms – namely, colonial
domination (conquest, displacement, and settlement) and
property (enslavement) (2017, p 08/11). However, Ferreira
da Silva’s invocation of the Thing in quantum mechanics, as
much as it claims for an autonomy of matter beyond form or
universal equivalence of value, also offers a way to unsettle the universal matrix of ethics. The Thing challenges the
ontic limit of knowledge imposed on and through science
by transcendental philosophy, which saw the extension
of the formal into efficient cause driving modern colonial
epistemology.
In particular, Ferreira da Silva’s ethical experiment offers
us a proof of the “equation of value” for which blackness
as nothing—that is zero value or infinity—has the creative
capacity to unsettle and hack Modern Western onto-epistemology of vision and knowledge. According to Ferreira da
Silva, zero is to be taken not as a contradiction, as established by the dialectic of presence and absence, but itself as
a signal of the autonomy (or in Laruelle’s term unilaterality
of) negation and of the negative. Instead of being invisible,
blackness, as matter without form, brings forward the nullification of the ocularcentric field of vision. But how to
demonstrate that such a nullification is not simply another
manifestation of the contradiction that rather confirms the
norms of the transcendental? This is a question that must
accompany the theorization of the negative optics of computational vision because it must bear the challenge against
the ontic limits of knowledge that continue to be inscribed
within the performance of science of information, whereby
the potentiality of machine epistemology is constantly resubsumed by transcendental decisionism and representational metaphysics. For Ferreira da Silva, it is a question
of re-articulating the mathematical mapping of the matrix
13
AI & SOCIETY
of ethics in terms of a radical engagement with zero as a
value in itself, rather than self-positing a critique that reveals
contradictions in the transcendental equation of value. In as
much as the result of Ferreira da Silva’s experiment with
the equation of value points to the real dimension of the
undeterminable (namely value without form, namely neither
life nor not-life), it also has zero value because it exists itself
without form. In equating blackness with zero or infinity,
Ferreira da Silva proposes a radical praxis as a refusal of
dialectics founded on the decisional principle of philosophy,
whose complains against (and rectification of) the invisibility of the automated image, are there only to re-produce its
ocular-logos of recognition (2017). From this standpoint, it
is possible to conclude that the dissolution of determinacy
and its transcendental decisionism requires not a skeptic critique against computational vision but above all a creative
elaboration of the non-ontic science of vision machine that
rather enables the possibility of overturning dialectics and
its critique. The argument for negative optics, therefore, does
not only concern the human–machine relationship but more
importantly how this latter, under technocapitalism, can
rather be turned inside out to defy the onto-epistemological
violence of ocular vision through and with the negation/negativity of infinities, with blackness as matter without form,
auto-impressions of the real without the given, auto-imaging minus the self-positing subject. From this standpoint, a
praxis of refusal stays with the trouble of dark optics, plunging within multi-layering opacities, fractalizing blackness in
artificial visions.
From this standpoint and against the universal model of
technology (the techno-logical universalism of ontic knowledge), it seems crucial to continue to elaborate how machine
vision is equivalent not only to the perceptual gaze but also
to the operational image that has sustained, since modernity,
the colonial operative epistemology and its matrix of ethics.
Ferreira da Silva’s reversed equation of value is a praxis of
refusal that does not only reveal the violence of self-determination but also invites us to stay with the negativity of
algebraic relations for which nothing is a signal of infinity
running against the metaphysical equivalence of knowledge
and vision and the capital equation of humans and machines.
If we were to invert the equation of value in computational
processing one may need to start from the indeterminacy
of compression, the negative algorithm of what cannot be
known in advance, the negative randomness that condemns
the computation to remain incomplete. Since negative optics
implies no image-form, so too no universal formalism can
grant the outcome of the algorithmic compression of randomness. It is, therefore, possible to argue that negative
optics is an index of immanence—namely no transcendental value can explain the auto-impression of images in the
algorithmic discretization of increasingly smaller patterns
of recognition. Here the networked relation between image,
13
concept and object is turned upside down as much as the
auto-impression of images exceeds the operative correction
of images that capital imparts on the human–machine alliance. Instead of the ocular representation of the world, the
increasing discretization of the network increases the volume
of randomness as much as the ANN affords the ingression
of indeterminate variations within algorithmic compression.
In other words, the machine vision also coincides with what
cannot be explained, programmed, represented beforehand
by concepts programmed in the machine. This is why algorithmic operations are not simply prescribed perceptions
but are auto-impressions of the human–machine condition
each and any time multiplying the field of auto-apparition
of blackness without form in the last instance.
To conclude, it is central to this argument that as much as
the negative optics of computational processing speaks of
the challenge of reinventing epistemology outside the knowledge-vision correlation, it also requires us to unsettle the
colonial roots of modern epistemology for a space of thought
for asymmetric auto-impressions of the human–machine
alliance. This is a collective and transversal effort in thinking with machines that starts from acknowledging the inhuman condition, the negativity of value, zero value in the
human–machine alliance. This is also to say that if the modern universality of technology continues to become reified in
machine visions, so too the argument for practices (all forms
of practices) starting from the zero value of auto-impression
must continue to unpack the heretic versioning of negativity
in the technopolitical reconstruction of epistemologies.
Compliance with ethical standards
Conflict of interest Nothing to declare.
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