10.54103/ai/15460



The landscape of contemporary visual culture and contemporary artistic practices is currently undergoing profound transformations caused by the application of technologies of machine learning to the vast domain of networked digital images. The impact of such technologies is so profound that it leads us to raise the very question of what we mean by “vision” and “image” in the age of artificial intelligence. This paper will focus on the work of three artists – Trevor Paglen, Hito Steyerl, Grégory Chatonsky – who have recently employed technologies of machine learning in non-standard ways. Rather than using them to train systems of machine vision with their different operations (face and emotion recognition, object and movement detection, etc.) and their different fields of application (surveillance, policing, process control, driverless vehicle guidance, etc.), they have used them in order to produce entirely new images, never seen before, that they present as altered states of the machine itself. 



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On the altered states of machine vision: Trevor Paglen, Hito Steyerl, Grégory Chatonsky
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Antonio Somaini
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On the altered states of machine vision: Trevor Paglen, Hito Steyerl, Grégory Chatonsky
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1
              On the altered
              states of machine
vision. Trevor Paglen, Hito
Steyerl, Grégory
by Antonio Somaini
                          Chatonsky
            Machine learning
                     Digital images
                     Paglen
                     Steyerl
                     Chatonsky




                                      Issue №1 Year 2022
                                      → Altered states
                                      Edited by Giancarlo Grossi
                                      and Andrea Pinotti
On the altered states
of machine vision.
Trevor Paglen, Hito Steyerl,
Grégory Chatonsky
by Antonio Somaini

Abstract                        The landscape of contemporary visual culture
                  and contemporary artistic practices is currently undergo-
                  ing profound transformations caused by the application
                  of technologies of machine learning to the vast domain of
                  networked digital images. The impact of such technologies
                  is so profound that it leads us to raise the very question of
                  what we mean by “vision” and “image” in the age of artifi-
                  cial intelligence. This paper will focus on the work of three
                  artists – Trevor Paglen, Hito Steyerl, Grégory Chatonsky
                 – who have recently employed technologies of machine
                  learning in non-standard ways. Rather than using them
                  to train systems of machine vision with their different op-
                  erations (face and emotion recognition, object and move-
                  ment detection, etc.) and their different fields of application
                  (surveillance, policing, process control, driverless vehicle
                  guidance, etc.), they have used them in order to produce
                  entirely new images, never seen before, that they present
                  as altered states of the machine itself.




Machine learning               Digital images             Paglen         Steyerl         Chatonsky
To quote this essay: A. Somaini, “On the altered states of machine vision. Trevor Paglen, Hito Steyerl,
Grégory Chatonsky”, AN-ICON. Studies in Environmental Images, no. 1 (2022): 91-111

ANTONIO SOMAINI                                    91                                           AN-ICON
                           The landscape of contemporary visual culture
             and contemporary artistic practices is currently undergo-
             ing profound transformations caused by the application
             of technologies of machine learning – one of the areas of
             so-called “artificial intelligence” – to the vast domain of
             networked digital images. Three strictly interrelated phe-
             nomena, in particular, are producing a real tectonic shift in
             the contemporary iconosphere, introducing new ways of
            “seeing” and new “images” – we’ll return later to the mean-
             ing of these quotation marks – that extend and reorganize
             the field of the visible, while redrawing the borders between
             what can and what cannot be seen.
                           These three strictly interrelated phenomena are:
      ჸ      the new technologies of machine vision fuelled by pro-
      cesses of machine learning such as the Generative Adversarial
      Networks (GAN);
      ჸ      the ever-growing presence on the internet of trillions of
      networked digital images that are machine-readable, in the sense
      that they can be seen and analyzed by such technologies of ma-
      chine vision;
      ჸ      the entirely new images that the processes of machine
      learning may generate.
                           Considered from the perspective of the longue
             durée of the history of images and visual media, the ap-
             pearance of these three phenomena raises a whole series
             of aesthetic, epistemological, ontological, historical and
             political questions. Their impact onto contemporary visu-
             al culture is so profound that it leads us to raise the very
             question of what we mean by “vision” and “image” in the
             age of artificial intelligence.
                           What is “seeing” when the human psycho-phys-
             iological process of vision is reduced, in the case of machine
             vision technologies, to entirely automated operations of pat-
             tern recognition and labelling, and when the various appli-
             cations of such operations (face and emotion recognition,
             object and motion detection) may be deployed across an ex-
             tremely vast visual field (all the still and moving images acces-
             sible online) that no human eye could ever attain? In speak-
             ing of “machine vision”, are we using an anthropomorphic
ANTONIO SOMAINI                      92                               AN-ICON
              term that we should discard in favor of a different set of
              technical terms, specifically related to the field of computer
              science and data analysis, that bear no connection with the
              physiological and psychological dynamics of human vision?
              Artists-researchers such as Francis Hunger and scholars
              such as Andreas Broeckmann (with his notion of “optical
              calculus” as “an unthinking, mindless mechanism, a calcu-
              lation based on optically derived input data, abstracted into
              calculable values, which can become part of computational
              procedures and operations”),1 Adrian MacKenzie and Anna
              Munster (with their ideas of a “platform seeing” operating
              onto “image ensembles” through an “invisual perception”),2
              Fabian Offert and Peter Bell (with their idea according to
              which the “perceptual topology” of machines is irreconcil-
              able with human perception)3 have argued for the necessity
              of moving beyond anthropocentric frameworks and terms,
              highlighting the fact that machine vision poses a real chal-
              lenge for the humanities.
                           Can we still use the term “image” for a digital
              file, encoded in some image format,4 that is machine-read-
              able even when it is not visible by human eyes, or that be-
              comes visible on a screen as a pattern of pixels only for
              a small fraction of time, spending the rest of its indefinite
              lifespan circulating across invisible digital networks? Can
              concepts such as that of “iconic difference” [ikonische Dif-
              ferenz],5 which highlights the fundamental perceptual dif-
              ference between an image and its surroundings (its “hors
              champ”) be still applied to machine-readable images?
                           And what is the status of the entirely new im-
              ages produced by processes of machine learning? These

      1 A. Broeckmann, “Optical Calculus”, paper presented November 2020 at the conference
      Images Beyond Control, FAMU, Prague, video, 5:01, https://www.youtube.com/
      watch?v=FnAgBbInMfA.
      2 A. MacKenzie, A. Munster, “Platform Seeing: Image Ensembles and Their Invisualities”,
      Theory, Culture & Society 36, no. 5 (2019): 3-22.
      3 F. Offert, P. Bell, “Perceptual bias and technical metapictures: critical machine vision as a
      humanities challenge”, AI & Society (2020), https://link.springer.com/article/10.1007/s00146-
      020-01058-z.
      4 On the theory of formats, see M. Jancovic, A. Schneider, A. Volmar, eds., Format Matters:
      Standards, Practices, and Politics in Media Cultures (Lüneburg: Meson Press, 2019).
      5 On the notion of “iconic difference”, see G. Boehm, “Ikonische Differenz”, Rheinsprung 11.
      Zeitschrift für Bildkritik 1 (2011): 170-176.

ANTONIO SOMAINI                                 93                                          AN-ICON
             are images that are not produced through some traditional
             form of lens-based analog or digital optical recording, nor
             through traditional computer-generated imagery (CGI) sys-
             tems, but rather through processes belonging to the wide
             realm of artificial intelligence that either transform existing
             images in ways that were impossible until a few years ago,
             or create entirely new images, never seen before. What do
             such images represent, what kind of agency do they have,
             how do they mediate our visual relation to the past, the
             present, and the future? And why have such new images
             generated by processes of machine learning been so of-
             ten considered, both in popular culture and in the work of
             contemporary artists, to be the product of some kind of
             altered state – a “dream”, a “hallucination”, a “vision”, an
            “artificial imagination” – of the machine itself?
                           Before we analyse the way in which this last
             question is raised, in different ways, by popular computer
             programs such as Google’s DeepDream (whose initial name
             echoed Christopher Nolan’s 2010 film “Inception”), and by
             the recent work of contemporary artists and theorists such
             as Trevor Paglen, Hito Steyerl and Grégory Chatonsky, let
             us begin with a quick overview of the current state of ma-
             chine vision technologies, with their operations and appli-
             cations, and of the new images produced by processes of
             machine learning that are increasingly appearing through-
             out contemporary visual culture.

                         The impact of machine learning
                         technologies onto contemporary visual
                         culture

                         First tested in the late 1950s, with image rec-
             ognition machines such as the Perceptron (developed at
             the Cornell Aeronautical Laboratory by Frank Rosenblatt
             in 1957), and then developed during the 1960s and 1970s
             as a way of imitating the human visual system in order
             to endow robots with intelligent behavior, machine vision
             technologies have entered a new phase, in recent years,
             with the development of machine learning processes,
ANTONIO SOMAINI                     94                              AN-ICON
              and with the possibility of using immense image databases
              accessible online as both training sets and fields of appli-
              cation.6 If in the 1960s and 1970s the goal was mainly to
              extract three-dimensional structures from images through
              the localization of edges, the labelling of lines, the detection
              of shapes and the modelling of volumes through feature
              extraction techniques such as the Hough transform (invent-
              ed by Richard Duda and Peter Hart in 1972, on the basis
              of a 1962 patent by Paul Hough), the recent development
              of machine learning techniques and the use of vast image
              training sets organized according to precise taxonomies
             – such as ImageNet, in which 14 millions of images are
              organized according to 21,000 categories derived from
              the WordNet hierarchy (a large lexical database of English
              nouns, verbs, adjectives and adverbs)7 – have allowed a
              rapid increase in the precision of all the operations of ma-
              chine vision.
                            Among such operations, we find pixel counting;
              segmenting, sorting, and thresholding; feature, edge, and
              depth detection; pattern recognition and discrimination;
              object detection, tracking, and measurement; motion cap-
              ture; color analysis; optical character recognition (this last
              operation allowing for the reading of words and texts within
              images, extending the act of machine “seeing” to a form
              of machine “reading”).
                            For a few years now, such operations have
              been applied to the immense field of machine-readable
              images. A field whose dimensions may be imagined only if
              we understand that any networked digital image – wheth-
              er produced through some kind of optical recording, or
              entirely computer-generated, or a mix of the two, as it is
              often the case – may be analysed by machine vision tech-
              nologies based on processes of machine learning such
              as the Generative Adversarial Networks (GAN).8 Starting
              from vast training sets containing images similar to the
      6 For a general overview of computer vision and computer imagery, with its historical
      developments, see S. Arcagni, L’occhio della macchina (Turin: Einaudi, 2018).
      7 “ImageNet”, accessed November 3, 2021, http://www.image-net.org/.
      8 I. Goodfellow et al., “Generative adversarial nets”, Advances in neural information
      processing systems (2014): 2672-2680.

ANTONIO SOMAINI                                95                                         AN-ICON
               ones the system has to learn to identify, and feeding such
               training sets into an ensemble of two adversarial neural
               networks that act as a Generator and a Discriminator that
               are in competition against one another, the GAN-based
               machine vision systems have gradually become more and
               more precise in performing their tasks. All the main smart
               phone producers have equipped their devices with cam-
               eras and image processing technologies that turn every
               photo we take into a machine-readable image, and internet
               giants such as Google and Facebook, as well as a host of
               other companies, have developed machine vision and face
               recognition systems capable of analysing the immense
               quantity of fixed and moving images that exist on the in-
               ternet and that continue to be uploaded every day, raising
               all sorts of ethical and political issues and highlighting the
               need for a broader legal framework that for the moment is
               largely missing.9
                             Considered together, such machine vision sys-
               tems are turning the contemporary digital iconosphere and
               the entire array of contemporary screens, with their various
               dimensions and degrees of definition,10 into a vast field for
               data mining and data aggregation. A field in which faces,
               bodies, gestures, expressions, emotions, objects, move-
               ments, places, atmospheres and moods – but also voices
               and sounds, through technologies of machine hearing –
               may be identified, labelled, stored, organized, retrieved,
               and processed as data that can be quickly accessed and
               activated for a wide variety of purposes: from surveillance
               to policing, from marketing to advertising, from the mon-
               itoring of industrial processes to military operations, from
               the functioning of driverless vehicles to that of drones and
               robots, from the study of the inside of the human body
               through the analysis of medical imagining all the way up to

      9 As I complete the final revisions of this essay, on 2 November 2021, Facebook just
      announced its decision to stop using facial-recognition software that could automatically
      recognize people in photos and videos posted on the social network: a massive shift for a
      company that is currently trying to reposition itself, also through the new company name Meta,
      adopted in October 2021.
      10 On the aesthetic, epistemological, historical and political implications of the high and low
      definition of images, see F. Casetti, A. Somaini, eds., La haute et la basse définition des images.
      Photographie, cinéma, art contemporain, culture visuelle (Milan-Udine: Mimesis, 2021).

ANTONIO SOMAINI                                  96                                           AN-ICON
              the study of the surface of the Earth and of climate change
              through the analysis of satellite images. Even disciplines
              that might seem to be distant from the most common cur-
              rent applications of machine vision technologies, such as
              art history and film studies, are beginning to test the pos-
              sibilities introduced by such an automated gaze, capable
              of “seeing” and analyzing, according to different criteria,
              vast quantities of visual reproductions of artworks or vast
              corpuses of films and videos in an extremely short time.11
                             In order to fully understand the impact of ma-
              chine learning onto contemporary visual culture, we need
              to add, to the vast field of machine vision technologies that
              we just described, the new images produced by processes
              of machine learning – often, the same GAN that are used
              to train and apply machine vision systems – that either
              transform pre-existing images in ways that were impossible
              until a few years ago, or create entirely new images, never
              seen before.
                             In the first case, we are referring to processes
              of machine learning capable of transforming existing imag-
              es that can have very different applications: producing 3D
              models of objects from 2D images; changing photographs
              of human faces in order to show how an individual’s ap-
              pearance might change with age (as with the app Face-
              App) or by merging a face with another face (Faceswap);12
              animating in a highly realistic way the old photograph of
              a deceased person (Deep Nostalgia, developed by My-
              Heritage);13 creating street maps from satellite imagery;14
              taking any given video, and “upscaling” it, by increasing
              its frame rate and its definition. An emblematic example

      11 See for example the various experiments being developed at the Google Arts & Culture
      Lab: “Google Arts & Culture”, accessed November 3, 2021, https://artsandculture.google.
      com/, or the way in which the EYE Film Museum in Amsterdam is testing new ways of
      accessing its collections through a program fuelled by machine vision systems: “Jan Bot”,
      accessed December 2, 2021, https://www.jan.bot/.
      12 The website of Faceswap, which announces itself as “the leading free and OpenSource
      multiplatform Deepfakes software”: “Faceswap”, accessed November 3, 2021, https://
      faceswap.dev/.
      13 “Deep Nostalgia”, accessed November 3, 2021, https://www.myheritage.fr/deep-nostalgia.
      14 R. Matheson, “Using artificial intelligence to enrich digital maps. Model tags road features
      based on satellite images, to improve GPS navigation in places with limited map data”, MIT
      News, January 23, 2020, https://news.mit.edu/2020/artificial-intelligence-digital-maps-0123.

ANTONIO SOMAINI                                97                                          AN-ICON
              of this last application, which may alter significantly our
              experience of visual documents of the past, would be the
              videos realized by Denis Shiryaev15 in which, through a
              process of machine learning, a Lumière film such as L’Ar-
              rivée d’un train en gare de La Ciotat (1896) is transformed
              from the original 16 frames per second to 60 frames per
              second, from the original 1.33:1 format to a contemporary
              16:9 format, and from the original, grainy 35mm analog
              film to a 4K digital resolution.16 In other examples of imag-
              es transformed by machine learning, the transformations
              are much more radical, as it happens with the so-called
             “deepfakes”: videos that use neural networks in order to ma-
              nipulate the images and the sounds of pre-existing videos
             – in some cases a single, pre-existing image – producing
              new videos that have a high potential to deceive. Among
              the many examples that can now be found across the in-
              ternet in different domains such as pornography, politics
              and social media, pornographic videos in which faces of
              celebrities are swapped onto the bodies of porn actors,
              a TikTok account with a whole series of odd videos by a
             “Deep Tom Cruise”,17 or speeches by public figures such as
              Barack Obama18 and Queen Elizabeth19 whose content has
              been completely altered in such a way that the movements
              of their mouths perfectly match the new, invented words
              they are uttering. And among the applications of deepfakes
              in the musical realm, the “new” songs by long deceased
              singers, whose style and voice are reproduced in a highly
              realistic way by applications of machine learning such as
              Jukebox, developed by OpenAI20: a “resurrecting” function

      15 “Denis Shiryaev”, Youtube channel, accessed November 3, 2021, https://www.youtube.
      com/c/DenisShiryaev/videos. For an online platform offering video enhancement powered by
      AI, see: “Neural Love”, accessed December 2, 2021, https://neural.love/.
      16 The video of the upscaled version of L’Arrivée d’un train en gare de La Ciotat (1896) can
      be found all across YouTube, in various black-and-white and colored version. For a version in
      color, see: Deoldify videos, “[DeOldified] Arrival on a Train at La Ciotat (The Lumière Brothers,
      1896)”, Youtube video, February 4, 2020, https://www.youtube.com/watch?v=EqbOhqXHL7E.
      17 “Deep Tom Cruise”, TikTok account, accessed November 3, 2021, https://www.tiktok.
      com/@deeptomcruise.
      18 BuzzFeed Video, “You Won’t Believe What Obama Says in This Video!”, Youtube video,
      April 17, 2018, https://www.youtube.com/watch?v=cQ54GDm1eL0.
      19 Channel 4, “Deepfake Queen: 2020 Alternative Christmas Message”, Youtube video,
      December 25, 2020, https://www.youtube.com/watch?v=IvY-Abd2FfM.
      20 “Jukebox”, accessed November 3, 2021, https://openai.com/blog/jukebox/.

ANTONIO SOMAINI                                  98                                           AN-ICON
              that in the animated photographs of deceased persons of
              Deep Nostalgia.
                           In the second case, the use of machine learning
              processes leads to the creation of entirely new images or
              sections of images: modelling patterns of motion (for ex-
              ample, crowd motion) in video, thereby leading computer
              generated imagery, in some cases fuelled by artificial intelli-
              gence, to produce new kinds of “contingent motion”;21 pro-
              ducing highly photorealistic images of objects and environ-
              ments for different kinds of advertising; inventing perfectly
              realistic faces of people that actually do not exist through
              open source softwares such as StyleGan and then make
              them accessible through projects such as Philip Wang’s
              This Person Does Not Exist.22
                           To these widespread applications of machine
              learning we may add the hybrid, unprecedented imagery
              produced by the software DeepDream, created in 2015 by
              the Google engineer and artist Alexander Mordvintsev: a
              program that uses Convolutional Neural Networks in order
              to enhance patterns in pre-existing images, creating a form
              of algorithmic pareidolia, the impression of seeing a figure
              where there is none, which is here generated by a process
              which repeatedly detects in a given image patterns and
              shapes that the machine vision system has been trained
              to see. 23 The result of such a recursive process, in which
              every new image is submitted again to the same kind of
              pattern and shape recognition, are images that recall an
              entire psychedelic iconography that spans through cinema,
              photography, the visual arts and even so-called art brut:
              images that are here presented as a sort of dream – a hal-
              lucinogenic, psychedelic dream – of the machine itself.




      21 J. Schonig, “Contingent Motion: Rethinking the ‘Wind in the Trees’ in Early Cinema and
      CGI”, Discourse: Journal for Theoretical Studies in Media and Culture 40, no. 1 (2018): 30-61.
      22 “This Person does not exist”, accessed November 3, 2021, https://thispersondoesnotexist.com/.
      23 The program is now open source: see “Deep Dream Generator”, accessed November
      3, 2021, https://deepdreamgenerator.com/. See also: “Alexander Mordvintsev”, accessed
      November 3, 2021, https://znah.net/.

ANTONIO SOMAINI                                 99                                          AN-ICON
                                                              Fig. 1




                                                              Fig. 2




                                                                Fig. 3
                              The original image (Fig. 1) has been
                                 modified by applying ten (Fig. 2)
                             and then fifty (Fig. 3) iterations of the
                        software DeepDream, the network having
                                   been trained to perceive dogs.




ANTONIO SOMAINI   100                                     AN-ICON
                                Exploring the “altered states”
                                of machine vision through Generative
                                Adversarial Networks

                           The idea that lies at the basis of the DeepDream
              software – the idea that machine learning technologies, and
              in particular the Convolutional Neural Networks (CNN) and
              Generative Adversarial Networks (GAN), may be used in order
              to explore and reveal the altered states of machine vision – can
              also be found in the recent works of artists (often active also
              as theorists) such as Trevor Paglen, Hito Steyerl, and Grégory
              Chatonsky.24 By insisting on the “creative”, image-producing
              potential of Generative Adversarial neural networks – rather
              than on their standard application for the training of machine
              vision systems – the three of them explore a vast field of
              images that they consider to be “hallucinations”, “visions of
              the future”, or the product of an “artificial imagination”, char-
              acterized by a new form of realism, a “disrealism”.
                           Trevor Paglen’s entire work as an artist and the-
              orist, since 2016, has been dedicated to the attempt of un-
              derstanding and visualizing the principles that lie at the basis
              of machine vision technology. Through texts (sometimes
              written in collaboration with the researcher Kate Crawford),25
              exhibitions, performances, and works made of still and mov-
              ing images, Paglen has tried to highlight not only the social
              and political biases that are inherent in the way machine vi-
              sion systems are structured, but also the way in which such
              systems diverge profoundly from human vision.26
                            In an article published in December 2016 in The
              New Inquiry with the title “Invisible Images (Your Pictures
              Are Looking at You)”,27 Paglen discusses the new challeng-
              es that arise in a context in which “sight” itself has be-
              come machine-operated and separated by human eyes,

      24 Among the first artists who started working with GANs, one should remember Helena Sarin
      and Mike Tyka. See “Helena Sarin”, AI Artist, accessed November 3, 2021 https://aiartists.org/
      helena-sarin, and “Mike Tyka”, accessed November 3, 2021, https://miketyka.com/.
      25 K. Crawford, Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence
      (New Haven: Yale University Press, 2021).
      26 See: “Trevor Paglen”, accessed November 3, 2021, https://paglen.studio/.
      27 T. Paglen, “Invisible Images (Your Pictures Are Looking at You)”, The New Inquiry, December
      8, 2016: https://thenewinquiry.com/invisible-images-your-pictures-are-looking-at-you/.

ANTONIO SOMAINI                                101                                          AN-ICON
               participating in a vast field of image operations. Arguing
               that what we are currently experiencing is part of a vast
               transition from human-seeable to machine-readable imag-
               es – a new condition in which “the overwhelming majority
               of images are now made by machines for other machines,
               with humans rarely in the loop” – Paglen writes that

if we want to understand the invisible world of machine-machine vi-
sual culture, we need to unlearn to see like humans. We need to learn
how to see a parallel universe composed of activations, keypoints,
eigenfaces, feature transforms, classifiers, training sets, and the like.28
                            We need to unlearn to see like humans. But how
               can we not see like humans, how can we step out of our
               human point of view? Accomplishing this apparently impos-
               sible task – a task which echoes the recurrent philosophical
               problem of how to step out of one’s own socio-historical
               position, of one’s own cognitive and emotional framework
              – has been the main goal of Trevor Paglen’s artistic practice
               during the last few years, as we can see in a body of works
               that was initially produced in 2017 through various col-
               laborations with computer vision and artificial intelligence
               researchers as an artist-in-residence at Stanford Univer-
               sity, and was first exhibited at the Metro Pictures Gallery
               in New York in an exhibition entitled A Study of Invisible
               Images (September 8 – October 21, 2017) 29, before being
               presented at various other galleries and museums such as
               the Osservatorio of the Fondazione Prada in Milan, or the
               Carnegie Museum of Art in Philadelphia, in an exhibition
               entitled Opposing Geometries (2020).
                            The works in the exhibition at Metro Pictures
               present a possible response to the challenge of how to pen-
               etrate within systems of machine vision that tend to expel
               the human gaze from their processes. Among them, we
               find the attempt to master the machine learning techniques

       28 Ibid.
       29 A series of images of the works presented in the exhibition can be found at the following
       address: “Trevor Paglen. A study of Invisible Images”, Metro Pictures, accessed January 19,
       2020, http://origin.www.metropictures.com/exhibitions/trevor-paglen4.

ANTONIO SOMAINI                                102                                         AN-ICON
              that are commonly used for machine vision applications, in
              order to hack them and lead them to produce entirely new
              images, never seen before, that may be considered as a
              form of hallucination of machine vision.
                           This is what happens in a series of still images
              entitled Adversarially Evolved Hallucinations, which Paglen
              developed through a non-standard application, in three
              steps, of Generative Adversarial Networks.30
                           The first step consisted in establishing new,
              original training sets. Instead of using the usual corpuses
              of images that are used to train machine vision systems
              in recognizing faces, objects, places and even emotions –
              corpuses that are often derived by pre-existing and easily
              available image databases such as the already mentioned
              ImageNet – Paglen established new training sets composed
              by images derived from literature, psychoanalysis, political
              economy, military history, and poetry. Among the various
              taxonomies he used in order to compose his training sets
              we find “monsters that have been historically interpreted
              as allegories of capitalism”, such as vampires, zombies,
              etc.; “omens and portents”, such as comets, eclipses, etc.;
             “figures and places that appear in Sigmund Freud’s The In-
              terpretation of Dreams”, a corpus which includes various
              symbols from Freudian psychoanalysis; “Eye-Machines”, a
              series of images clearly inspired by Harun Farocki’s videoin-
              stallations Eye-Machine I, II, III (2001-2003) and containing
              images of surveillance cameras or of spaces under surveil-
              lance; “American Predators”, a corpus containing various
              predatory animals, plants, and humans indigenous to the
              United States, mixed with military hardware like predator
              drones and stealth bombers.
                           The second step consisted in feeding these un-
              usual training sets into the two neural networks of the GAN
              system: the Discriminator and the Generator. These two
              networks begin interacting with one another in an adver-
              sarial, competitive way, in such a way that the Discriminator,

      30 Information on the Adversarially Evolved Hallucinations can be found in Trevor
      Paglen’s website: T. Paglen, “Hallucinations”, accessed November 3, 2021, https://paglen.
      studio/2020/04/09/hallucinations/.

ANTONIO SOMAINI                               103                                        AN-ICON
             after having received the initial training set, has to evaluate
             the images that it receives from the Generator, deciding
             whether they resemble or not to those of the training set. As
             the process unfolds through reiterated exchanges between
             the two neural networks, the Discriminator becomes more
             and more precise and effective in evaluating the images
             that are submitted to it.
                          The third step consists in the artist intervening
             in the process and choosing to extract, at a given mo-
             ment, one of the images produced by the Generator: an
             image that emerges from the sequence of the adversarial
             exchanges, and that is the result of one of the countless
             attempts by the Generator to test the precision of the Dis-
             criminator, trying to fool it. In the case of the series of the
             Adversarially Evolved Hallucinations, all the images selected
             by Paglen seem to bear some kind of resemblance to the
             ones contained in the original training sets – even though
             we cannot really assess the degree of this resemblance,
             because the training sets are not accessible to us – while
             displaying at the same time different forms of deviations
             and aberrations that recall a sort of psychedelic imaginary.
                          Among Paglen’s Adversarially Evolved Hal-
             lucinations, we find images with titles such as Vampire
             (Corpus: Monsters of Capitalism), Comet (Corpus: Omens
             and Portents), The Great Hall (Corpus: The Interpreta-
             tion of Dreams), Venus Flytrap (Corpus: American Pred-
             ators), A Prison Without Gards (Corpus: Eye-Machines).
             In the case of Vampire (Corpus: Monsters of Capitalism),




ANTONIO SOMAINI                     104                             AN-ICON
                                                    Fig. 4. Trevor Paglen, Vampire
                                                 (Corpus: Monsters of Capitalism),
                                                 dye sublimation metal print, 2017

                           the Discriminator was trained on thousands
             of images of zombies, vampires, Frankensteins, and other
             ghosts that have been at some point – be it in essays, liter-
             ary texts, or films – associated with capitalism. Paglen then
             set the Generator and Discriminator running until they had
             synthesized a series of images that corresponded at least
             in part to a specific class within the given corpus. From all
             of the even slightly acceptable options that the GAN gen-
             erated, Paglen then selected the one we see in the series
             exhibited at Metro Pictures as the “finished work”.
                          There may be multiple reasons behind Trevor
             Paglen’s decision to call these images “hallucinations”. To
             begin with, such images recall a type of imagery that we
             might consider to be “psychedelic” or “surrealist”: in some
             of them, we definitely see echoes of Max Ernst, or Salvador
             Dalì. A second reason may lie in the attempt to emphasize
             the fact that the result of this non-standard application of
             the processes of machine learning – a process which un-
             folds within a closed machine-to-machine space, the in-
             visible space of the back-and-forth between the Generator
             and the Discriminator from which human eyes are excluded
            – produces images that, just like human hallucinations, have
             no footing in exterior reality, or may merge in unpredictable
ANTONIO SOMAINI                    105                                  AN-ICON
             way with shapes and forms stemming from the perception
             of the outer world. Finally, the term “adversarially evolved
             hallucinations” may underline the fact that these images
             are the result of a machine learning process gone astray: a
             process which has been hacked and led to drift away from
             its original, standard applications.
                           The reference to the term “hallucinations”, though,
             should not be misleading. What Trevor Paglen tries to show
             us with his Adversarially Evolved Hallucinations has really
             nothing to do with a disruption of the orderly functioning of
             human consciousness. What they highlight, rather, is the
             radical otherness of machine vision, if compared with human
             vision. A radical otherness based on operations that have
             nothing to do with human, embodied vision, which we may
             just try to grasp through the impossible attempt of “unlearn-
             ing to see like humans”.
                           We find a different application of images pro-
             duced by machine learning in This is the Future, an instal-
             lation by Hito Steyerl which was presented at the Venice
             Biennale in 2019, and which was conceived as an expan-
             sion of the exhibition Power Plants at the Serpentine Gal-
             lery in London the previous year. In the Venice installation,
             Hito Steyerl arranged onto different platforms a series of
             nine videos in which one could see images resembling to
             some kind of “vegetal” time-lapse imagery: flowers quickly
             blossoming and spreading out, plants and bamboo shoots
             growing in height and width.




                                                                        Fig. 5a




ANTONIO SOMAINI                      106                              AN-ICON
                                                                                               Fig. 5b




                                                                                                Fig. 5c
                                                             Figg. 5a, 5b and 5c. Hito Steyerl, This if
                                                            the Future: A 100% Accurate Prediction,
                                                              stills from the single channel HD video,
                                                                                color, sound, 16’, 2019
                             What interested Steyerl in the use of neural
              networks in this installation was the predictive nature of
              machine learning, and the status of “visions of the future”
              of its imagery: the fact that neural predictive algorithms op-
              erate through statistical models and predictions based on
              immense, “big data” databases, and are therefore related
              to the vast spectrum of predictive systems (be they finan-
              cial, political, meteorological, environmental, etc.) that are
              present in contemporary “control societies”, while at the
              same time being part of the longue durée of the history of
              prediction systems elaborated by human cultures.
                             The main video presented in Hito Steyerl’s instal-
              lation, entitled This is the Future: A 100% Accurate Prediction,
              consists of images produced through a collaboration with
              the programmer Damien Henry, author of a series of videos
              entitled A Train Window31 in which a machine learning algo-
              rithm has been trained to predict the next frame of a video
              by analyzing samples from the previous image, in such a
              way that, as in a perfect feedback loop, each output image

      31 The videos, programmed with Tensorflow, are available at: D. Henry, “A train window”,
      Magenta, October 3rd, 2018, https://magenta.tensorflow.org/nfp_p2p. I thank both Hito
      Steyerl and Damien Henry for useful information on the coding used in This is the Future.

ANTONIO SOMAINI                               107                                           AN-ICON
              becomes the input for the next step in the calculation. In this
              way, after intentionally choosing or producing only the first
              image, all the following ones are generated by the algorithm,
              without any human intervention. In Hito Steyerl’s work, this
              idea of a video entirely made up of images “predicted” by
              neural networks and located, as the video says, “0.04 sec-
              onds in the future”, is presented as a new kind of documen-
              tary imagery: an imagery that can at the same time predict
              and document the future, as paradoxical as this may seem.
              The video begins with white text on a black background that
              reads: “These are documentary images of the future. Not
              about what it will bring, but about what it is made of”. The
              next five sections of the video – Heja’s Garden, The Future:
              A History, Bambusa Futuris, Power Plants and Heja’s Pre-
              diction – lead us to a psychedelic landscape of images that
              morph sample images stemming from categories such as
             “sea”, “fish”, “flower”, “rose” or “orchid”: each of them is
              produced by a neural network that, as the electronic voice
              accompanying the video tells us, “can see one fraction of a
              second into the future”.
                           Second Earth (2019) by Grégory Chatonsky
              takes another route into the iconosphere produced by
              GAN-driven machine learning. What interests him is the
              idea of an “artificial imagination”, capable of visualizing,
              through the means of artificial intelligence, “the halluci-
              nation of a senseless machine, a monument dedicated to
              the memory of the extinct human species”.32 Himself in
              charge of the coding which lies at the base of the various
              elements and the various media mobilized in his work, Cha-
              tonsky works in particular on what he calls the “chaînage”,
              the “sequencing” of different artificial intelligence sys-
              tems that, taken all together, produce a whole cascade
              of new forms: among them, neural networks capable of
              generating new texts (read by synthetic voices) starting
              from some given text databases, or capable of generat-
              ing images from given texts, and texts from given images,

      32 See the presentation of Second Earth: G. Chatonsky, “Second Earth / Terre Seconde”,
      accessed November 29, 2021, http://chatonsky.net/earth/.

ANTONIO SOMAINI                              108                                       AN-ICON
             with a new kind of AI-powered ekphrasis. The metamor-
             phical universe that we see in the videos of Second Earth,




                                                                              Fig. 6a




                                                                              Fig. 6b




                                                                              Fig. 6c




                                                                               Fig. 6d
                                                    Figg. 6a, 6b, 6c and 6d. Grégory
                                            Chatonsky, Second Earth, stills from one
                                               of the videos in the installation, 2019


             a work that Chatonsky presents as an “evolving installation”,
             evokes the idea of a form-generating power that used to be
             rooted in nature and which is now taken over by machines
             which are incorporating and re-elaborating the trillions of
ANTONIO SOMAINI                    109                                      AN-ICON
               images that humans have uploaded on the internet as a
               sort of hypertrophic memory. The text accompanying the
               work reads: “Accumulate data. Outsourcing our memories.
               Feed software with this data so that it produces similar data.
               Produce realism without reality, become possible. Disap-
               pear. Coming back in our absence, like somebody else”.33
                             As products of a “realism without reality”, what
               Chatonsky calls a “disrealism”,34 the images produced
               through Generative Adversarial Networks in Second Earth do
               have a hallucinatory, oneiric, “surrealistic” quality, that bears
               a strange kind of “family resemblance” to the ones that we
               find in Paglen’s Adversarially Evolved Hallucinations, Steyerl’s
               This is the Future, and in the work of other artists who have
               recently explored the GAN-generated imagery, such as Pierre
               Huyghe in his installation Uumwelt (2018). In Chatonsky’s
               Second Earth, these images do refer to some kind of “outer
               reality”, but the status of this outer reality is highly unclear.
                             On the one hand, they bear traces of the imag-
               es contained in the training sets that have been employed
               in order to activate the GANs: in the cases of the stills from
               one of the videos in the installation that are here reproduced,
               such training sets referred probably to categories such as
              “birds”, “faces”, “eyes”, etc., and the images contained in
               the training sets – be they actual photographs, or still from
               videos – do in most cases refer to some profilmic reality.
                             On the other, extracted as they are from the
              “latent space” of a process of machine learning in motion
               from the pole of absolute noise to the pole of a perfect re-
               semblance to the images of the training set, the images of
               Second Earth refer to another kind of reality, one that does
               not exist yet. We are here in the domain of “anticipation”,
               rather than “prediction”,35 in the perspective of an explora-

       33 G. Chatonsky, Second Earth, http://chatonsky.net/earth/.
       34 Grégory Chatonsky has begun to use and theorize this term in recent lectures held at
       the Jeu de Paume and at Campus Condorcet in Paris, in the framework of the lecture cycle
       L’esthétique à l’heure du pixel (September 2021 – May 2022) and the seminar L’image à
       l’épreuve des machines. Reconfigurations du visible (25-26 October 2021). See for example
      “Le disréalisme (le pixel perdu de l’espace latent)”, Jeu de Paume, accessed november 29,
       2021, https://jeudepaume.org/evenement/seminaire-esthetique-pixel-1/.
       35 Unpublished conversation with Grégory Chatonsky, 2019, whom I thank for the useful
       information on the different software used in Second Earth.

ANTONIO SOMAINI                               110                                         AN-ICON
             tion of a non-human “artificial imagination”, rather than in
             the denunciation of the pervasive presence of systems of
             control and surveillance, as it was the case in the work of
             Hito Steyerl. At the basis of Chatonsky’s “Second Earth”,
             we find the observation that

the machine was becoming capable of automatically producing
a phenomenal quantity of realistic images from the accumulation
of data on the Web. This realism is similar to the world we know,
but it is not an identical reproduction. Species metamorphose into
each other, stones mutate into plants and the ocean shores into
unseen organisms. The result: this “second” Earth, a reinvention of
our world, produced by a machine that wonders about the nature
of its production.
                            Over fifty years ago, in his seminal Understand-
             ing Media. The Extensions of Man (1964), Marshall McLuhan
             formulated the idea that art could become, in some deci-
             sive historical moments, a form of “advance knowledge of
             how to cope with the psychic and social consequences of
             the next technology”, and added that new art forms might
             become in these moments “social navigation charts”, help-
             ing us find some orientation across a sensorium entirely
             transformed by new media and new technologies. Today,
             while we witness the first signs of what promises to be a
             massive impact of artificial intelligence onto all areas of our
             psychic, social, and cultural life, the works of artists such
             as Trevor Paglen, Hito Steyerl and Grégory Chatonsky do
             appear like “navigation charts”: their exploration of the al-
             tered states of machine vision through the appropriation
             and the détournement of technologies such as the Gen-
             erative Adversarial Networks help us better understand
             the aesthetic, epistemological and political implications of
             the transformations that such technologies are producing
             within contemporary visual culture. Taken together, they
             highlight the fact that what is at stake is the very status
             of what we mean by “image” and by “vision” in the age of
             artificial intelligence.

ANTONIO SOMAINI                       111                             AN-ICON
AN-ICON has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme. Grant agreement No. 834033 AN-ICON.
The project is hosted by the Department of Philosophy “Piero Martinetti” – Department of Excellence at the State University of Milan.



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