Papers
Leonardo
Noise-cancelling technologies for smartphone cameras allow individuals to control and customize t... more Noise-cancelling technologies for smartphone cameras allow individuals to control and customize their self-images. These technologies used to automatically eliminate unwanted visual noise, such as excessive grain or lighting issues, skin imperfections and teeth stains, are now widely used on smartphones to transform a noisy photograph into a “clean” and “ideal” image. In this study, I conduct a critical analysis of a) the increasing popularity of these technologies through their application in smartphone cameras; “b)” their active involvement in the construction of users’ ideal self-images; and “c)” their contribution to increased instances of visual phobias, obsessions, anxieties, and irritation.
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Digital Cultures: Knowledge / Culture / Technology, Sep 1, 2018
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AI & SOCIETY
For millions of years, biological creatures have dealt with the world without being able to see i... more For millions of years, biological creatures have dealt with the world without being able to see it; however, the change in the atmospheric condition during the Cambrian period and the subsequent increase of light, triggered the sudden evolution of vision and the consequent evolutionary benefits. Nevertheless, how from simple organisms to more complex animals have been able to generate meaning from the light who fell in their eyes and successfully engage the visual world remains unknown. As shown by many psychophysical experiments, biological visual systems cannot measure the physical properties of the world. The light projected onto the retina is, in fact, unable to specify the physical properties of the world in which humans and other visually ‘intelligent’ animals behave; however, visual behaviours are habitually successful. Through psychophysical evidence, examples of the functioning of Artificial Neural Networks (ANNs) and a reflection upon visual appreciation in the cultural and artistic context, this paper shows (a) how vision emerged by random trial and error during evolution and lifetime learning; (b) how the functioning of ANNs may provide evidence and insights on how machine and human vision works; and (c) how rethinking vision theory in terms of trial and error may offer a new approach to better understand vision—biological and artificial—and reveal new insights into why we like what we like. (https://rdcu.be/b68Lv)
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Reconceptualizing the Digital Humanities in Asia, 2020
Machines “sense” the world in various ways, and their ways of sensing, in turn, affects the way h... more Machines “sense” the world in various ways, and their ways of sensing, in turn, affects the way humans experience the world. In A Short History of Photography (1931), Walter Benjamin uses the idea of an optical unconscious to describe the contributions of photography and cinema to the visible human world and the cultural consequences of such inventions. Compared to the pulsional unconscious delineated by Freud, a new type of the unconscious can be glimpsed in twentieth-century human beings, who have delegated their actions to technology. The definition of the optical unconscious fits particularly well with the environment of the late nineteenth century and twentieth century; however, it seems to be no longer appropriate in the twenty-first century, that has radically changed, far from the human eyes and largely invisible. This article intends to demonstrate the existence of a new type of the unconscious, an electromagnetic unconscious that better seems to define the contemporary situation.
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Leonardo
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AI & SOCIETY
For millions of years, biological creatures have dealt with the world without being able to see i... more For millions of years, biological creatures have dealt with the world without being able to see it; however, the change in the atmospheric condition during the Cambrian period and the subsequent increase of light, triggered the sudden evolution of vision and the consequent evolutionary benefits. Nevertheless, how from simple organisms to more complex animals have been able to generate meaning from the light who fell in their eyes and successfully engage the visual world remains unknown. As shown by many psychophysical experiments, biological visual systems cannot measure the physical properties of the world. The light projected onto the retina is, in fact, unable to specify the physical properties of the world in which humans and other visually ‘intelligent’ animals behave; however, visual behaviours are habitually successful. Through psychophysical evidence, examples of the functioning of Artificial Neural Networks (ANNs) and a reflection upon visual appreciation in the cultural and artistic context, this paper shows (a) how vision emerged by random trial and error during evolution and lifetime learning; (b) how the functioning of ANNs may provide evidence and insights on how machine and human vision works; and (c) how rethinking vision theory in terms of trial and error may offer a new approach to better understand vision—biological and artificial—and reveal new insights into why we like what we like. (https://rdcu.be/b68Lv)
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Leonardo, Oct 2021
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AI and SOCIETY , 2020
For millions of years, biological creatures have dealt with the world without being able to see i... more For millions of years, biological creatures have dealt with the world without being able to see it; however, the change in the atmospheric condition during the Cambrian period and the subsequent increase of light, triggered the sudden evolution of vision and the consequent evolutionary benefits. Nevertheless, how from simple organisms to more complex animals have been able to generate meaning from the light who fell in their eyes and successfully engage the visual world remains unknown. As shown by many psychophysical experiments, biological visual systems cannot measure the physical properties of the world. The light projected onto the retina is, in fact, unable to specify the physical properties of the world in which humans and other visually ‘intelligent’ animals behave; however, visual behaviours are habitually successful. Through psychophysical evidence, examples of the functioning of Artificial Neural Networks (ANNs) and a reflection upon visual appreciation in the cultural and artistic context, this paper shows (a) how vision emerged by random trial and error during evolution and lifetime learning; (b) how the functioning of ANNs may provide evidence and insights on how machine and human vision works; and (c) how rethinking vision theory in terms of trial and error may offer a new approach to better understand vision—biological and artificial—and reveal new insights into why we like what we like.
(https://rdcu.be/b68Lv)
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European journal of media studies, 2018
In the context of machine vision, image recognition refers to the ability of machines and algorit... more In the context of machine vision, image recognition refers to the ability of machines and algorithms to identify people, places, objects, gestures, or other subjects in a given image. Self-driving cars, for instance, use machine vision systems to locate road signs, vehicles, pedestrians, and cyclists, to understand the three-dimensional space in which they are located and avoid collisions. Facebook uses facial image recognition systems to identify photographs in which a person is present but not tagged, and to help visually impaired users identify people in a specific video. However, although it is simple for a human being to make sense of an image and identify its content, these operations are still particularly complex for machines and algorithms. In this paper we will investigate how machines and algorithms are trained for image recognition purposes. To start, the tasks performed by human annotators will be discussed. Part two will outline some problems with this process, and part three will present further thoughts and reflections on the subject. The terms ‘machine vision’ and ‘algorithmic vision’, which often appear throughout this paper, replace the term ‘computer vision’ and are used in a broad sense that seems to better reflect contemporary reality.
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25th International Symposium on Electronic Art (ISEA 2019): LUX AETERNA (Eternal Light), Jun 1, 2019
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Books
AI&Society, 2021
AI&Society Special Issue - Ways of Machine Seeing
Volume 36, Issue 4, 2021, pp. 1093–1312.
Contr... more AI&Society Special Issue - Ways of Machine Seeing
Volume 36, Issue 4, 2021, pp. 1093–1312.
Contributors:
Paglen&Crawford, Pasquinellli&Joler, Mirzoeff, Parikka, Bratton, Parisi, Manovich, Gil-Fournier&Parikka, Maleve, Chávez Heras&Blanke, Offert&Bell, Treccani, Celis BuenoMaría&Schultz Abarca, van der Veen, Emsley, Uliasz, Møhl.
How do machines, and, in particular, computational technologies, change the way we see the world? This special issue brings together researchers from a wide range of disciplines to explore the entanglement of machines and their ways of seeing from new critical perspectives. As the title makes clear, we take our point of departure in John Berger’s 1972 BBC documentary series Ways of Seeing, a four-part television series of 30-min films created by Berger and producer Mike Dibb, which had an enormous impact on both popular and academic perspectives on visual culture. Berger’s scripts were adapted into a book of the same name, published by Penguin also in 1972. The book consists of seven numbered essays: four using words and images; and three essays using only images. Seeing is evidently a political act, exemplified in the third episode-chapter, where images of women in early modern European painting (Pol de Limbourg, Cranach the Elder, Jan Gossaert, Tintoretto) and commercial magazines are juxtaposed to demonstrate the ways in which women are rendered as objects of the male gaze. More broadly, Berger emphasised that “the relation between what we see and what we know is never settled”. In this special issue, we explore how these ideas can be understood in the light of technical developments in machine vision and algorithmic learning, and how the relations between what we see and know are further unsettled.
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Reconceptualizing the Digital Humanities in Asia, 2020
Machines “sense” the world in various ways, and their ways of sensing, in turn, affects the way h... more Machines “sense” the world in various ways, and their ways of sensing, in turn, affects the way humans experience the world. In A Short History of Photography (1931), Walter Benjamin uses the idea of an optical unconscious to describe the contributions of photography and cinema to the visible human world and the cultural consequences of such inventions. Compared to the pulsional unconscious delineated by Freud, a new type of the unconscious can be glimpsed in twentieth-century human beings, who have delegated their actions to technology. The definition of the optical unconscious fits particularly well with the environment of the late nineteenth century and twentieth century; however, it seems to be no longer appropriate in the twenty-first century, that has radically changed, far from the human eyes and largely invisible. This article intends to demonstrate the existence of a new type of the unconscious, an electromagnetic unconscious that better seems to define the contemporary situation.
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Thesis Chapters
LABS Leonardo Abstracts Service, 2020
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Peer Reviewed Research Papers in Conf. Proceedings
How machines see the world: Understanding how machine vision affects our way of perceiving, thinking and designing the world, 2017
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Treccani, C. (2019). How machine see the world: Understanding image labelling. In O. T. Leino et al. (Eds.), proceedings of the Art Machines: International Symposium on Computational Media Art (ISCMA) (pp. 104-105). Hong Kong: School of Creative Media, City Univeristy of Hong Kong., 2019
Michael Baxandall, in Painting and Experience in 15th Century Italy (1988), shows the existence o... more Michael Baxandall, in Painting and Experience in 15th Century Italy (1988), shows the existence of a series of rules that painters were advised to follow. These "guidelines" explained, for instance, how each different figure or hand position painted, within that specific cultural context, represented a different concept. These rules helped the painter maintain relevance in that historical and cultural context. [1]
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Conference Presentations
Ways of Machine Seeing 2017 (conference title), 2017
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Uploads
(https://rdcu.be/b68Lv)
Volume 36, Issue 4, 2021, pp. 1093–1312.
Contributors:
Paglen&Crawford, Pasquinellli&Joler, Mirzoeff, Parikka, Bratton, Parisi, Manovich, Gil-Fournier&Parikka, Maleve, Chávez Heras&Blanke, Offert&Bell, Treccani, Celis BuenoMaría&Schultz Abarca, van der Veen, Emsley, Uliasz, Møhl.
How do machines, and, in particular, computational technologies, change the way we see the world? This special issue brings together researchers from a wide range of disciplines to explore the entanglement of machines and their ways of seeing from new critical perspectives. As the title makes clear, we take our point of departure in John Berger’s 1972 BBC documentary series Ways of Seeing, a four-part television series of 30-min films created by Berger and producer Mike Dibb, which had an enormous impact on both popular and academic perspectives on visual culture. Berger’s scripts were adapted into a book of the same name, published by Penguin also in 1972. The book consists of seven numbered essays: four using words and images; and three essays using only images. Seeing is evidently a political act, exemplified in the third episode-chapter, where images of women in early modern European painting (Pol de Limbourg, Cranach the Elder, Jan Gossaert, Tintoretto) and commercial magazines are juxtaposed to demonstrate the ways in which women are rendered as objects of the male gaze. More broadly, Berger emphasised that “the relation between what we see and what we know is never settled”. In this special issue, we explore how these ideas can be understood in the light of technical developments in machine vision and algorithmic learning, and how the relations between what we see and know are further unsettled.
(https://rdcu.be/b68Lv)
Volume 36, Issue 4, 2021, pp. 1093–1312.
Contributors:
Paglen&Crawford, Pasquinellli&Joler, Mirzoeff, Parikka, Bratton, Parisi, Manovich, Gil-Fournier&Parikka, Maleve, Chávez Heras&Blanke, Offert&Bell, Treccani, Celis BuenoMaría&Schultz Abarca, van der Veen, Emsley, Uliasz, Møhl.
How do machines, and, in particular, computational technologies, change the way we see the world? This special issue brings together researchers from a wide range of disciplines to explore the entanglement of machines and their ways of seeing from new critical perspectives. As the title makes clear, we take our point of departure in John Berger’s 1972 BBC documentary series Ways of Seeing, a four-part television series of 30-min films created by Berger and producer Mike Dibb, which had an enormous impact on both popular and academic perspectives on visual culture. Berger’s scripts were adapted into a book of the same name, published by Penguin also in 1972. The book consists of seven numbered essays: four using words and images; and three essays using only images. Seeing is evidently a political act, exemplified in the third episode-chapter, where images of women in early modern European painting (Pol de Limbourg, Cranach the Elder, Jan Gossaert, Tintoretto) and commercial magazines are juxtaposed to demonstrate the ways in which women are rendered as objects of the male gaze. More broadly, Berger emphasised that “the relation between what we see and what we know is never settled”. In this special issue, we explore how these ideas can be understood in the light of technical developments in machine vision and algorithmic learning, and how the relations between what we see and know are further unsettled.