- Visual Arts, Communication, Cultural Studies, Computer Science, Visualization, Digital Humanities, and 39 moreSoftware Studies, Data Science, Media Studies, Social Media, Social Networking & Social Media, Photography, Cinema, Avant-Garde Cinema, Big Data, Data Visualization, Design, Digital Media, New Media Art, Big Data Visualisation, Cultural Theory, Infographics and data visualization, New Media, Digital Culture, Photography Theory, Digital Photography, New Media Art & Emerging Practices, Facial Recognition, Experimental Media Arts, Photographs, Big Data Analytics, Instagram, Visual Culture, Internet Studies, Information Visualization, Big Data / Analytics / Data Mining, Social Sciences, Art History, Contemporary Art, Art Theory, Data Mining, Computational Sociology, Mapping, Digital History, and Image Processing(Software Studies, Data Science, Media Studies, Social Media, Social Networking & Social Media, Photography, Cinema, Avant-Garde Cinema, Big Data, Data Visualization, Design, Digital Media, New Media Art, Big Data Visualisation, Cultural Theory, Infographics and data visualization, New Media, Digital Culture, Photography Theory, Digital Photography, New Media Art & Emerging Practices, Facial Recognition, Experimental Media Arts, Photographs, Big Data Analytics, Instagram, Visual Culture, Internet Studies, Information Visualization, Big Data / Analytics / Data Mining, Social Sciences, Art History, Contemporary Art, Art Theory, Data Mining, Computational Sociology, Mapping, Digital History, and Image Processing)edit
- Lev Manovich is a visual artist, writer, and one of the world's most influential digital culture theorists. Manovich ... moreLev Manovich is a visual artist, writer, and one of the world's most influential digital culture theorists. Manovich played a key part in creating four new research fields: new media studies (1991-), software studies (2001-), cultural analytics (2007-) and AI aesthetics (2018-). He has published 190 articles and 17 books that include AI Aesthetics, Cultural Analytics, Instagram and Contemporary Image, and The Language of New Media described as “the most suggestive and broad-ranging media history since Marshall McLuhan.” His digital art projects were shown in 14 personal and 122 international groups exhibitions in Centre Pompidou, ICA London, ZKM, KIASMA, and other leading venues.
http://manovich.net/
http://lab.culturalanalytics.info/(Lev Manovich is a visual artist, writer, and one of the world's most influential digital culture theorists. Manovich played a key part in creating four new research fields: new media studies (1991-), software studies (2001-), cultural analytics (2007-) and AI aesthetics (2018-). He has published 190 articles and 17 books that include AI Aesthetics, Cultural Analytics, Instagram and Contemporary Image, and The Language of New Media described as “the most suggestive and broad-ranging media history since Marshall McLuhan.” His digital art projects were shown in 14 personal and 122 international groups exhibitions in Centre Pompidou, ICA London, ZKM, KIASMA, and other leading venues.<br />http://manovich.net/<br />http://lab.culturalanalytics.info/)edit - Mieke Baledit
This essay discusses similarities between media and art creation using Generative AI today and a number of conceptually related artistic paradigms in the 20th century. Although generative AI and modernist art appear to be opposites of... more
This essay discusses similarities between media and art creation using Generative AI today and a number of conceptually related artistic paradigms in the 20th century. Although generative AI and modernist art appear to be opposites of each other (one was focused on "making it new," the other is based on training data of already existing art), in reality they are similar. While modernist artists explicitly opposed traditions, in reality they achieved innovation by reinterpreting and incorporating older art forms from other cultures. Similarly, generative AI tools allow the creation of new works because they are trained on massive databases of existing art and media. Therefore, making new art and media with GenAI fits into a long-standing tradition in modern art that involves creating new art from accumulations of existing artifacts. This tradition encompasses modernist collage and photomontage, post-modern bricolage, net art, and the pioneering media work of artists like Nam June Paik. Contemporary AI artists, such as Refik Anadol and Lev Pereulkov, exemplify the practice of using AI models trained on specific datasets to produce novel artworks that engage in a dialogue with historical art while introducing new aesthetic possibilities.
Research Interests:
Research Interests:
Even though Instagram has been the subject of numerous studies, none of them have systematically investigated its potential as a narrative medium. This article argues that Instagram's narrative capabilities are comparable to those of... more
Even though Instagram has been the subject of numerous studies, none of them have systematically investigated its potential as a narrative medium. This article argues that Instagram's narrative capabilities are comparable to those of literature and film. To support our claims, we analyze a number of prominent female Instagram creators and demonstrate how they employ the platform's diverse features, functionalities, and interface to create multi-year biographical narratives. Furthermore, we discuss the applicability of theories developed in literary and film studies in analyzing Instagram's narrative capabilities. By employing Bakhtin's influential chronotope concept, we examine in depth how these narratives make specific use of space and time. Additionally, we compare time construction in film and Instagram narratives using the cinema studies' theory of narrative time in movies.
https://firstmonday.org/ojs/index.php/fm/article/view/12497
https://firstmonday.org/ojs/index.php/fm/article/view/12497
Research Interests: Cultural Studies, Communication, Media Studies, Media and Cultural Studies, Digital Humanities, and 14 moreArt Theory, Film Studies, Internet Studies, Narratology, Digital Culture, Literary Theory, Social Media, Media Theory, HCI, Cybercultures, Digital Theory and Culture, Cinema Studies, Digital Aesthetics, and Cultural analytics
While we do not expect that an artificial system sees and “thinks” like a human being, it must have nevertheless a sense of how humans perceive and react. This touches on what I define in Chapter 8 as the problem of “AI alignment” in the... more
While we do not expect that an artificial system sees and “thinks” like a human being, it must have nevertheless a sense of how humans perceive and react. This touches on what I define in Chapter 8 as the problem of “AI alignment” in the context of aesthetics. This also requires an understanding of the limits and biases natural to human perception and cognition. Artificial systems can analyze data beyond human capabilities, but it remains crucial for them to be able to take into account the typical modalities of human perception. For example, when an AI system trained in object recognition fails to discern details in an image, we might conclude that either the system lacks sophistication or the image is excessively blurred or noisy. However, in the realm of human aesthetic experience, moments of confusion are not necessarily flaws to be fixed. Often, an object captivates our interest precisely because it poses a perceptual or cognitive challenge, such as ambiguity or indeterminacy. Artistic techniques like defamiliarization (ostranenie) thrive on uncertainty and interpretative instability, sparking the viewer’s curiosity and attention. Unlike in AI, where ambiguity might be a problem to solve, in aesthetics, ambiguity is a feature. An artwork’s aesthetic richness often lies in its resistance to a singular interpretation, remaining open to multiple readings.
Research Interests: Artificial Intelligence, Aesthetics, Art History, Media Studies, New Media, and 15 moreArt Theory, Machine Learning, Visual Culture, Psychology of art, Mass Communication, Philosophy of Art, Media Theory, Visual Arts, Aesthetics and Theory of Arts, Mass Communication and New Media, Communication Studies, Philosophy of Artificial Intelligence, Image theory, Aesthetics and Visual Culture, and Experimental Aesthetics(Art Theory, Machine Learning, Visual Culture, Psychology of art, Mass Communication, Philosophy of Art, Media Theory, Visual Arts, Aesthetics and Theory of Arts, Mass Communication and New Media, Communication Studies, Philosophy of Artificial Intelligence, Image theory, Aesthetics and Visual Culture, and Experimental Aesthetics)
(Art Theory, Machine Learning, Visual Culture, Psychology of art, Mass Communication, Philosophy of Art, Media Theory, Visual Arts, Aesthetics and Theory of Arts, Mass Communication and New Media, Communication Studies, Philosophy of Artificial Intelligence, Image theory, Aesthetics and Visual Culture, and Experimental Aesthetics)
Lev Manovich. MEMORY, DRAW. Images and texts presented in a solo exhibition | Seoul, South Korea. 12/20/2023 - 02/16/2024. The exhibition presents a selection of my digital images from 2023 and also a few of my early drawings... more
Lev Manovich. MEMORY, DRAW.
Images and texts presented in a solo exhibition | Seoul, South Korea.
12/20/2023 - 02/16/2024.
The exhibition presents a selection of my digital images from 2023 and also a few of my early drawings (1978-1985). The title of the exhibition refers to the use of generative AI as a "memory machine." AI models extract information from existing cultural material during the training process, constructing a new historical archive. And when we probe inside this archive, asking AI tools to generate scenes from a specific historical period or place, or to create something unique, the results are frequently idealized, generic imagery, or bland. For me, using generative AI is a constant battle against this fundamental limitation of this medium. The introductory texts in this catalog some of the techniques I employ to push AI to generate more subtle, delicate, and ambitious images that I prefer.
The title of the exhibition also refers to a number of my works on paper from the early 1980s, which are displayed as digital prints in this exhibition. These drawings were created following my emigration from the Soviet Union in 1981 (seethe "Closed Word" section of the catalog).
Finally, the exhibition title alludes to "Memory, Speak," the title of a 1951 memoir by Vladimir Nabokov, a famousRussian emigrant writer.
Images and texts presented in a solo exhibition | Seoul, South Korea.
12/20/2023 - 02/16/2024.
The exhibition presents a selection of my digital images from 2023 and also a few of my early drawings (1978-1985). The title of the exhibition refers to the use of generative AI as a "memory machine." AI models extract information from existing cultural material during the training process, constructing a new historical archive. And when we probe inside this archive, asking AI tools to generate scenes from a specific historical period or place, or to create something unique, the results are frequently idealized, generic imagery, or bland. For me, using generative AI is a constant battle against this fundamental limitation of this medium. The introductory texts in this catalog some of the techniques I employ to push AI to generate more subtle, delicate, and ambitious images that I prefer.
The title of the exhibition also refers to a number of my works on paper from the early 1980s, which are displayed as digital prints in this exhibition. These drawings were created following my emigration from the Soviet Union in 1981 (seethe "Closed Word" section of the catalog).
Finally, the exhibition title alludes to "Memory, Speak," the title of a 1951 memoir by Vladimir Nabokov, a famousRussian emigrant writer.
Research Interests: Artificial Intelligence, Aesthetics, Art History, Digital Humanities, Art Theory, and 15 moreDigital Media, Contemporary Art, Visual Culture, Digital Culture, Memory Studies, Media Theory, Russian & Soviet Art, Soviet Visual Culture, Visual Arts, Digital Theory and Culture, Communication Studies, Lev Manovich, Philosophy of Artificial Intelligence, Media theory and Research, and Curating contemporary art(Digital Media, Contemporary Art, Visual Culture, Digital Culture, Memory Studies, Media Theory, Russian & Soviet Art, Soviet Visual Culture, Visual Arts, Digital Theory and Culture, Communication Studies, Lev Manovich, Philosophy of Artificial Intelligence, Media theory and Research, and Curating contemporary art)
(Digital Media, Contemporary Art, Visual Culture, Digital Culture, Memory Studies, Media Theory, Russian & Soviet Art, Soviet Visual Culture, Visual Arts, Digital Theory and Culture, Communication Studies, Lev Manovich, Philosophy of Artificial Intelligence, Media theory and Research, and Curating contemporary art)
The key difference between me, a human, and generative AI: I am limited, but AI is unlimited. Yes, of course: it has significant limits now, in practice. But it advances fast, and what it can already do today is beyond what we could have... more
The key difference between me, a human, and generative AI: I am limited, but AI is unlimited. Yes, of course: it has significant limits now, in practice. But it advances fast, and what it can already do today is beyond what we could have imagined a year ago. Instead of dwelling on what AI can’t do at this particular moment, it is safer to assume that it “can” will only multiply.
Because of how human skills, learning and memory works, I have limitations. I don’t have knowledge of the immense “museum without walls" distributed over the web and museum databases. But AI can. And it will only get better. I can’t simply sit down and start writing summaries of numerous topics in the history of culture. AI can. “I can’t… but AI can.” (Endless other examples can be added.) So why make art now? And what art will still be meaningful to make?
Because of how human skills, learning and memory works, I have limitations. I don’t have knowledge of the immense “museum without walls" distributed over the web and museum databases. But AI can. And it will only get better. I can’t simply sit down and start writing summaries of numerous topics in the history of culture. AI can. “I can’t… but AI can.” (Endless other examples can be added.) So why make art now? And what art will still be meaningful to make?
Research Interests: History of Science and Technology, Artificial Intelligence, Aesthetics, Art History, Media Studies, and 14 moreNew Media, Digital Humanities, Internet Studies, Digital Media, Contemporary Art, Visual Culture, Software Studies, Media Theory, Visual Arts, Cybercultures, Mass media, Visual Cultures, Digital Aesthetics, and Digital Art History
The AI Brain in the Cultural Archive (Notes on Refik Anondol Unsupervised AI art project for MoMA, and connections between generative AI and artistic methods of the 19th and 20th century.) Shorter version of this text published in MoMA... more
The AI Brain in the Cultural Archive
(Notes on Refik Anondol Unsupervised AI art project for MoMA, and connections between generative AI and artistic methods of the 19th and 20th century.)
Shorter version of this text published in MoMA magazine, July 21, 2023. Another version was included in Chapter 5 of "Artificial Aesthetics" book.
(Notes on Refik Anondol Unsupervised AI art project for MoMA, and connections between generative AI and artistic methods of the 19th and 20th century.)
Shorter version of this text published in MoMA magazine, July 21, 2023. Another version was included in Chapter 5 of "Artificial Aesthetics" book.
Research Interests:
Geoffrey Hinton, the ‘Godfather of AI,’ recently predicted that so-called general artificial intelligence could emerge within 20 years. This term refers to a hypothetical future AI that can perform any cognitive task that a human can.... more
Geoffrey Hinton, the ‘Godfather of AI,’ recently predicted that so-called general artificial intelligence could emerge within 20 years. This term refers to a hypothetical future AI that can perform any cognitive task that a human can. This statement begs the question: how long will it take before we see the emergence of general artistic intelligence? And what should such hypothetical general artistic AI be capable of?
Research Interests: Semiotics, Artificial Intelligence, Visual Studies, Art History, Media Studies, and 15 moreNew Media, Art Theory, Contemporary Art, Visual Culture, Digital Culture, Media Art, Visual Communication, Media Theory, Art Criticism, New Media Art, Visual Arts, New Media Studies, Mass Communication and New Media, Digital Art History, and Cultural analytics
I describe a number of characteristics of AI visual generative media in its current forms that I believe are particularly significant or novel. Some of my arguments also apply to generative media in general, but most focus on visual... more
I describe a number of characteristics of AI visual generative media in its current forms that I believe are particularly significant or novel. Some of my arguments also apply to generative media in general, but most focus on visual media. The analysis reflects my own experience of using a few popular AI image tools, such as Midjourney and Stable Diffusion, almost every day from the middle of 2022 until now.
Research Interests: Artificial Intelligence, Aesthetics, Communication, Art History, Media Studies, and 15 moreNew Media, Art Theory, Media History, Contemporary Art, Visual Culture, Visual Semiotics, Modern Art, Digital Culture, Media Theory, Modernism, Digital Arts, New Media Art, Visual Arts, Art Theory and Criticism, and Digital Art History
This publication contains all articles I wrote between 1991 and 2007 (65 articles, 288,000 words). We reformatted all articles in the same way and assembled them into a single text document. This is a PDF version of this file. You can... more
This publication contains all articles I wrote between 1991 and 2007 (65 articles, 288,000 words). We reformatted all articles in the same way and assembled them into a single text document. This is a PDF version of this file. You can download another version of this PDF where TOC is interactive - you can click on any title and you will be taken to this text in the file:
http://manovich.net/index.php/projects/lev-manovich-all-articles-1991-2007
You can also download text file version and HTML versions at the same URL.
The text version uses minimal Markdown markup. It can be opened in any text editor, and you can also easily convert it in any other format such as .docx or ebook. If you use a text editor that understands markup, the text file will be nicely formatted. Examples of such editors include Ulysses, Typora, Draft, IA Writer, Obsidian.
(The second part of this publication with all 2008-2022 articles will come out in a few months).
http://manovich.net/index.php/projects/lev-manovich-all-articles-1991-2007
You can also download text file version and HTML versions at the same URL.
The text version uses minimal Markdown markup. It can be opened in any text editor, and you can also easily convert it in any other format such as .docx or ebook. If you use a text editor that understands markup, the text file will be nicely formatted. Examples of such editors include Ulysses, Typora, Draft, IA Writer, Obsidian.
(The second part of this publication with all 2008-2022 articles will come out in a few months).
Research Interests:
Suppose human creativity could be potentially replicated by mechanical processes. In that case, we would face a crossroads: either we could give up using the concept of creativity altogether, or if we hold to our common understanding of... more
Suppose human creativity could be potentially replicated by mechanical processes. In that case, we would face a crossroads: either we could give up using the concept of creativity altogether, or if we hold to our common understanding of what creativity is, we could agree to apply this concept to non-human phenomena as well, as world champion Lee Sedol did when judging the performance of AlphaGo. However, the idea that artificial creativity discloses the mechanic nature of human creativity should also be met with a bit of critical detachment, particularly if we consider the specific case of the arts. In fact, artificial reproductions of human artifacts do not follow the same processes with which humans actually produced those artifacts. Nobody thinks that Mondrian followed procedures similar to the algorithm used in 1966 that generated pseudo-Mondrian, even though the public appreciated the artificial images more than the original ones....
Research Interests: Artificial Intelligence, Aesthetics, Art History, New Media, Art Theory, and 11 moreCreativity, Contemporary Art, New Media Arts, Media Arts, Software Studies, Media Theory, Digital Arts, Computer Art and Technocultures, Aesthetics and Theory of Arts, Creativity Research, and Communication and media Studies
Dialog between Margarita Kuleva and Lev Manovich on fashion, beauty and new technologies
Research Interests:
The current discussions about the adoption of AI (artificial intelligence) in visual arts, design, architecture, cinema, music and other arts often rely on widely accepted ideas about art and creativity. These ideas include the following:... more
The current discussions about the adoption of AI (artificial intelligence) in visual arts, design, architecture, cinema, music and other arts often rely on widely accepted ideas about art and creativity. These ideas include the following: “Art is the most creative human domain.” “Art and creativity can’t be measured.” “Artists does not follow rules.” It is also commonly assumed that “computers can only follow rules,” and therefore “computers struggle to generate something novel and original.” Taken together, these ideas lead to a new assumption: “generation of original art is a great test of AI progress.”
Where do these popular popular ideas about art and its relationship to creativity come from? Historically, they are quite recent. For thousands of years human creators in all human cultures made artifacts that today we put in museums and worship as great art. But their creators did not have modern concepts of art, artist, and creativity.
Th goal of this text is to briefly discuss the historical origins of currently popular ideas about art and creativity, and suggest that these ideas limit our vision of cultural AI. There are a few dominant popular understandings of “art” today. Logically, they contradict each other. Despite this, they may perfectly co-exist in a single publication or conversation. Sometimes one idea dominates and others do not appear. But very often, all three are assumed to be valid in the same time. Because these ideas contradict each other, holding them together can lead to feelings of confusion and unease - and also big fears about “creative AI.”
Where do these popular popular ideas about art and its relationship to creativity come from? Historically, they are quite recent. For thousands of years human creators in all human cultures made artifacts that today we put in museums and worship as great art. But their creators did not have modern concepts of art, artist, and creativity.
Th goal of this text is to briefly discuss the historical origins of currently popular ideas about art and creativity, and suggest that these ideas limit our vision of cultural AI. There are a few dominant popular understandings of “art” today. Logically, they contradict each other. Despite this, they may perfectly co-exist in a single publication or conversation. Sometimes one idea dominates and others do not appear. But very often, all three are assumed to be valid in the same time. Because these ideas contradict each other, holding them together can lead to feelings of confusion and unease - and also big fears about “creative AI.”
Research Interests:
Our study for the first time looks at product design evolution over a long period using machine learning methods. It examines 23,492 sneaker images and metadata obtained from StockX.com, a global reselling site. These sneaker designs span... more
Our study for the first time looks at product design evolution over a long period using machine learning methods. It examines 23,492 sneaker images and metadata obtained from StockX.com, a global reselling site. These sneaker designs span 22 years, from 1999 to 2020. We propose a new tool for understanding historical design changes that we call "design index." As used in this paper, design index combines many sneakers' characteristics into a single number. Such indexes can be computed for any brand or types of sneakers, thus allowing us to compare changes in their models design over time. This method can be also used to study historical changes in other cultural fields such as literature, cinema, visual art, architecture, social media photography, etc.
Our study shows how well-known brands have maintained their distinct identities in the "design space" of possible color color combinations and shapes. The model also predicts which sneakers will likely command a high premium in the reselling market, indicating potential investment and design strategies to use in the future.
Our study shows how well-known brands have maintained their distinct identities in the "design space" of possible color color combinations and shapes. The model also predicts which sneakers will likely command a high premium in the reselling market, indicating potential investment and design strategies to use in the future.
Research Interests:
Imagine this scenario: you find out that an artwork you admire a lot and that you think was made by a human is actually the product of an artificial intelligence. Would your aesthetic judgment change? Would you look, lis- ten or read the... more
Imagine this scenario: you find out that an artwork you admire a lot and that you think was made by a human is actually the product of an artificial intelligence. Would your aesthetic judgment change? Would you look, lis- ten or read the work with different eyes? If so, why? (And if not, why not?)....
Research Interests: Computer Graphics, Artificial Intelligence, Humanities Computing (Digital Humanities), Aesthetics, Art History, and 11 moreMedia Studies, Digital Humanities, Media History, Psychology of art, Philosophy of Art, Media Theory, Metaverse, Digital Theory and Culture, New Media Studies, Philosophy of Artificial Intelligence, and Cultural analytics
What would be the equivalent of the Turing test for an AI system capable of creating new songs, games, music, visual art, design, architecture, films? This looks like a simple question with an easy answer. If a system can automatically... more
What would be the equivalent of the Turing test for an AI system capable of creating new songs, games, music, visual art, design, architecture, films? This looks like a simple question with an easy answer. If a system can automatically create new works in each media or genre and we cannot tell the difference between those works and those created by humans, it passes the Turing test... If we think further, we quickly realize that this is more complex. To even begin to answer it, we may need to consider ideas from several fields such as philosophical aesthetics, experimental psychology of the arts, histories of the arts, media theory, and software studies. Discussions about a Turing test for artistic creativity have not used perspectives from the last two fields much, and yet in my view they are very important for thinking about AI and creativity questions. This chapters explores the challenges of defining a test for artistic AI in our era when human creators routinely rely on digital assets and creative software which already has been offering AI-type support for long time. In other worlds: what would it mean for “genuine artistic AI” to compete with contemporary artists who already implicitly use AI implemented in their standard tools (operating in Photoshop, Premiere, After Effects, Blender, Unreal and so on behind the scene)?
Research Interests:
This research report presents the first quantitative analysis of art biennales history. We look at the growth and diffusion of international art biennales using a unique dataset of 200 biennales we created. We discuss the effects of 2008... more
This research report presents the first quantitative analysis of art biennales history. We look at the growth and diffusion of international art biennales using a unique dataset of 200 biennales we created. We discuss the effects of 2008 global financial crisis on biennales diffusion and what it may suggest for further diffusion in the new pandemics era. (This work is a part of a larger Elsewhere project where we investigate the growth of global contemporary culture after 1990 using data science and AI methods.)
Research Interests:
"Artificial Aesthetics. A Critical Guide to AI, Media and Design" is a book by Lev Manovich and Emanuele Arielli. The book is released one chapter at a time on academia.edu, medium.com, and manovich.net. Each chapter will be added to... more
"Artificial Aesthetics. A Critical Guide to AI, Media and Design" is a book by Lev Manovich and Emanuele Arielli. The book is released one chapter at a time on academia.edu, medium.com, and manovich.net. Each chapter will be added to academia.edu as a separate PDF in "Articles" section. Later all chapters will be combined into a single PDF and it will be added to "Books."
Book Preface:
Suppose you are a designer, an architect, a photographer, a video maker, a musician, a writer, an artist, or a professional or student in any other creative field. Or perhaps you are a digital creator making content in multiple media. You may be wondering how AI will affect your professional area in general and your work and career. This book does not aim to predict the future or tell you exactly what will happen. Instead, we want to offer you a set of intellectual tools to help you better navigate any changes that may come along. These tools come from several different fields: aesthetics, philosophy of art and psychology of art (Emanuele), and media theory, digital culture studies, and data science (Lev). As far as we know, our book is the first to bring together all these different perspectives in thinking about creative AI. We started the work on the book in summer 2019, exchanging numerous messages, commenting on each other ideas, and sharing drafts of sections. The final book is a result of this process. Although each chapter is written by one author, it reflects the discussions we had over 27 months.
Book Preface:
Suppose you are a designer, an architect, a photographer, a video maker, a musician, a writer, an artist, or a professional or student in any other creative field. Or perhaps you are a digital creator making content in multiple media. You may be wondering how AI will affect your professional area in general and your work and career. This book does not aim to predict the future or tell you exactly what will happen. Instead, we want to offer you a set of intellectual tools to help you better navigate any changes that may come along. These tools come from several different fields: aesthetics, philosophy of art and psychology of art (Emanuele), and media theory, digital culture studies, and data science (Lev). As far as we know, our book is the first to bring together all these different perspectives in thinking about creative AI. We started the work on the book in summer 2019, exchanging numerous messages, commenting on each other ideas, and sharing drafts of sections. The final book is a result of this process. Although each chapter is written by one author, it reflects the discussions we had over 27 months.
Research Interests: Cognitive Psychology, Computer Science, Artificial Intelligence, Aesthetics, Media Studies, and 15 moreNew Media, Design, Web Design, Digital Media, Psychology of art, Philosophy of Art, Digital Culture, Software Studies, Digital Media & Learning, Photography Theory, Media Theory, New Media Art, New Media Studies, Data Science, and Libros Digitales
Today, it is completely impossible for any single person to follow all the billions of posts and video livestreams happening daily on social networks. Even if you are subscribed to only a few hundred information sources, be they... more
Today, it is completely impossible for any single person to follow all the billions of posts and video livestreams happening daily on social networks. Even if you are subscribed to only a few hundred information sources, be they individual users or news companies posting on a single network, this still may be impossible. All these sources sending messages around the clock can easily overwhelm human cognitive capacities of reading, looking or hearing. By the time that you have caught up with new messages from the last hour, 1000 times more messages have been sent.
So why is this impossible? We have accepted that social media platforms use algorithms that select only a tiny proportion of this constantly changing and expanding universe. The selected content is translated into a single linear stream and this is what we see: a single column of posts that the algorithms have decided are most relevant to us.
This is not the best solution. Why does a proper interface for the age of social media, or “social broadcasting”, still not exist? Will it ever exist in the future? How might it look? Is the problem with our cognitive information processing limits, or with engineers and designers failure to imagine a different interface to our global village?
So why is this impossible? We have accepted that social media platforms use algorithms that select only a tiny proportion of this constantly changing and expanding universe. The selected content is translated into a single linear stream and this is what we see: a single column of posts that the algorithms have decided are most relevant to us.
This is not the best solution. Why does a proper interface for the age of social media, or “social broadcasting”, still not exist? Will it ever exist in the future? How might it look? Is the problem with our cognitive information processing limits, or with engineers and designers failure to imagine a different interface to our global village?
Research Interests:
Aleksei Gastev (Russia,1882– 1939) is a unique figure in the avant-garde culture of the 20th century. In the 1910s-1930s, many avant-garde creators in Europe and in Russia were inspired by the machines and buildings of the industrial age... more
Aleksei Gastev (Russia,1882– 1939) is a unique figure in the avant-garde culture of the 20th century. In the 1910s-1930s, many avant-garde creators in Europe and in Russia were inspired by the machines and buildings of the industrial age - airplanes, factory machines, grain elevators, bridges. The engineer was the hero of the time. Architects, designers, filmmakers, and poets were applying principles of efficiency, economy and strict in their own fields. They developed new languages of design, visual communication and everyday material culture appropriate for the second machine age.
Usually the influence run into one direction - from the world machines, the ideals of engineering, and latest scientific research to the arts and design. However, as far as we know, only Gastev has moved in the opposite direction. Instead of applying principles and methods of work to the arts or design, he left his art (e.g., poetry) to became the director of the institute that aimed to further rationalize work and workers on nation-wide scale.
Usually the influence run into one direction - from the world machines, the ideals of engineering, and latest scientific research to the arts and design. However, as far as we know, only Gastev has moved in the opposite direction. Instead of applying principles and methods of work to the arts or design, he left his art (e.g., poetry) to became the director of the institute that aimed to further rationalize work and workers on nation-wide scale.
Research Interests:
Our second publication in "Lesser-Known Russian Avant-Garde" series is on the first museum of modern art. Although we usually assume think that such museum was MoMA (New York, 1929), another museum called Museum of Pictorial Culture was... more
Our second publication in "Lesser-Known Russian Avant-Garde" series is on the first museum of modern art. Although we usually assume think that such museum was MoMA (New York, 1929), another museum called Museum of Pictorial Culture was established in 1919 and run by most important Russian avant-garde artists until its closing in 1929. In our two essays, we discuss a number of innovations this museum introduced. We also discuss museum's exhibiting methods and how they anticipate the emerging use of visualization in museum interfaces in the 21st century.
Research Interests:
In this article, I argue that the use of numerical representation and data analysis methods offers a new language for describing cultural artifacts, experiences and dynamics. The human languages such as English or Russian that developed... more
In this article, I argue that the use of numerical representation and data analysis methods offers a new language for describing cultural artifacts, experiences and dynamics. The human languages such as English or Russian that developed rather recently in human evolution are not good at capturing analog properties of human sensorial and cultural experiences. These limitations become particularly worrying if we want to compare thousands, millions or billions of artifacts-i.e. to study contemporary media and cultures at their new twenty-first century scale. When we instead use numerical measurements of image properties standard in Computer Vision, we can better capture details of a single artifact as well as visual differences between a number of artifacts-even if they are very small. The examples of visual dimensions that numbers can capture better then languages include color, shape, texture, contours, composition, and visual characteristics of represented faces, bodies and objects. The methods of finding structures and relationships in large numerical datasets developed in statistics and machine learning allow us to extend this analysis to very big datasets of cultural objects. Equally importantly, numerical image features used in Computer Vision also give us a new language to represent gradual and continuous temporal changes-something which natural languages are also bad at. This applies to both single artworks such as a film or a dance piece (describing movement and rhythm) and also to changes in visual characteristics in millions of artifacts over decades or centuries.
Research Interests:
How is a data representation of some phenomenon or process different from other kinds of cultural representations humans used until now, be they representational paintings, literary narratives, historical accounts, or hand-drawn maps?... more
How is a data representation of some phenomenon or process different from other kinds of cultural representations humans used until now, be they representational paintings, literary narratives, historical accounts, or hand-drawn maps? First, a data representation is modular, i.e., it consists of separate elements: objects and their features. Secondly, the features are encoded in such a way that we calculate on them. In other words, today “data” is not just any arbitrary collection of items existing in some medium such as paper. In a computational environment, “data” is something a computer can read, transform, and analyze. This imposes fundamental constraints on how we represent anything. This short article discusses this and other fundamental characteristics of data representation.
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We live in aesthetic society (i.e., the society of aesthetically sophisticated consumer goods and services). In such a society, the production of beautiful images, interfaces, objects and experiences, are central to economic and social... more
We live in aesthetic society (i.e., the society of aesthetically sophisticated consumer goods and services). In such a society, the production of beautiful images, interfaces, objects and experiences, are central to economic and social functioning. Rather than being a property of art, sophisticated aesthetics becomes the key property of commercial goods and services. Aesthetic society values space designers, user-experience designers, architects, photographers, models, stylists, and other design and media professionals, as well as individuals who are able to use social media, including making beautiful and refined images, and work with marketing and analytics tools. “Using” in this context refers to creating successful content, promoting this content, communicating with followers, and achieving desired goals. This article analyses one area of the aesthetic society that became particularly important in 2010s – Instagram. I discuss different types of photos shared on Instagram: casual, professional, and designed. I then cover in detail the strategies used by Instagram authors to create the designed images.
Who are these authors and what does their Instagram aesthetic production tells us about culture in the 21st century? I bring in four relevant terms proposed to describe modern cultures: mainstream, hipsters, subcultures, tribes. I suggest that instagrammers are neither an avant-garde creating something entirely new, nor subcultures that define themselves in opposition to the mainstream, nor the masses consuming commodified versions of aesthetics developed earlier by certain subcultures. They are more similar to Michel Maffesoli’s tribes but exist in the digital global Instagram “city” rather than as “villages” in a physical city. According to Maffesoli, who developed his analysis of the “urban tribe” back in 1980s, the term “refers to a certain ambience, a state of mind, and it is preferably to be expressed through lifestyles that favor appearance and form” (1996). Such ambience and state of mind are the “message” of Instagramism, but now expanded worldwide and crafted through photography.
(The article is a revised version of Instagram and Contemporary Image book I published under CC license in 2017.)
Who are these authors and what does their Instagram aesthetic production tells us about culture in the 21st century? I bring in four relevant terms proposed to describe modern cultures: mainstream, hipsters, subcultures, tribes. I suggest that instagrammers are neither an avant-garde creating something entirely new, nor subcultures that define themselves in opposition to the mainstream, nor the masses consuming commodified versions of aesthetics developed earlier by certain subcultures. They are more similar to Michel Maffesoli’s tribes but exist in the digital global Instagram “city” rather than as “villages” in a physical city. According to Maffesoli, who developed his analysis of the “urban tribe” back in 1980s, the term “refers to a certain ambience, a state of mind, and it is preferably to be expressed through lifestyles that favor appearance and form” (1996). Such ambience and state of mind are the “message” of Instagramism, but now expanded worldwide and crafted through photography.
(The article is a revised version of Instagram and Contemporary Image book I published under CC license in 2017.)
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On first sight, coming with a definition for “AI arts” does not sound hard. AI (an abbreviation for the term Artificial Intelligence) refers to computers being able to perform many human-like cognitive tasks, such as playing games of... more
On first sight, coming with a definition for “AI arts” does not sound hard. AI (an abbreviation for the term Artificial Intelligence) refers to computers being able to perform many human-like cognitive tasks, such as playing games of chess and Go, recognizing content in images, translating between languages, selecting best candidates in a job search based on their CVs, and so on. This is how AI has been traditionally understood, and we can extend this concept to the arts. Following this logic, “AI arts” would refer to humans programing computers to create with a significant degree of autonomy new artifacts or experiences that professional members of the art world recognize as belonging to “contemporary art.” Or, we can teach computers skills of artists from some earlier historical period and expect that professional art historians recognize new artifacts the computer creates as possible art from this period....
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In this article I will discuss a few general challenges for Cultural Analytics research. Now that we have very large cultural data available, and our computers can do complex analysis quite quickly, how shall we look at culture? Do we... more
In this article I will discuss a few general challenges for Cultural Analytics research. Now that we have very large cultural data available, and our computers can do complex analysis quite quickly, how shall we look at culture? Do we only use computational methods to provide better answers to questions already established in the 19th and 20th century humanities paradigms, or do these methods allow fundamentally different new concepts?
I think that such perspectives are necessary because contemporary culture itself is now driven by the same or similar methods. So, for instance, if we want to analyse intentions, ideology and psychology of an author of certain cultural artefact or experience, this author maybe not a human but some form of AI that uses a combination of data analysis, machine learning and algorithmic generation. In fact, the difference between using computational methods and concepts to analyse cultural data today vs. twenty years ago is that now these methods and concepts are driving everyday digital culture lived by billions of people. When small numbers of humanists and social scientists were analysing cultural data with computers in the second part of the 20th century, their contemporary culture was mostly analogue, physical, and non-quantifiable. But today we as academic researchers live in the “shadow” of a world of social networks, recommendations, apps, and interfaces that all use media analytics. As I already explained, I see media analytics as the new stage in the development of modern technological media. This stage is characterized by algorithmic large-scale analysis of media and user interactions and the use of the results in algorithmic decision making such as contextual advertising, recommendations, search, and other kinds of information retrieval, filtering of search results and user posts, document classification, plagiarism detection, video fingerprinting, content categorization of user photos, automatic news production etc.
And we are still only at the beginning of this stage. Given the trajectory of gradual automation of more and more functions in modern society using algorithms, I expect that production and customization of many forms of at least “commercial culture” (characterized by conventions, genre expectations, and templates) will also be gradually automated. So, in the future already developed digital distribution platforms and media analytics will be joined by the third part: algorithmic media generation. We can see this at work already today in automatically generated news stories, online content written about topics suggested by algorithms, production of some television shows, and TV broadcasts during sport events where multiple robotic cameras automatically follow and zoom into dynamic human performances.
Until ten years ago, key cultural techniques we used to represent and reason about the world and other humans included natural languages, lens-based photo and video imaging, various other media for preserving and accessing information, calculus, digital computers, and computer networks. The core concepts of data/AI society are now as important. They form data society’s “mind”—the particular ways of encountering, understanding, and acting on the world and the humans. And this is why even if you have no intention of doing practical Cultural Analytics research yourself, you need anyway to become familiar with these new data-centred cultural techniques.
While both media analytics in industry and Cultural Analytics research use dozens of algorithms, behind them there is a small number of fundamental paradigms. We can think them as types of data/AI society’s cognition. The three most general ones are data visualization, unsupervised machine learning, and supervised machine learning. Others are feature extraction, clustering, dimension reduction, classification, regression, network science, time series analysis, and information retrieval.
I think that such perspectives are necessary because contemporary culture itself is now driven by the same or similar methods. So, for instance, if we want to analyse intentions, ideology and psychology of an author of certain cultural artefact or experience, this author maybe not a human but some form of AI that uses a combination of data analysis, machine learning and algorithmic generation. In fact, the difference between using computational methods and concepts to analyse cultural data today vs. twenty years ago is that now these methods and concepts are driving everyday digital culture lived by billions of people. When small numbers of humanists and social scientists were analysing cultural data with computers in the second part of the 20th century, their contemporary culture was mostly analogue, physical, and non-quantifiable. But today we as academic researchers live in the “shadow” of a world of social networks, recommendations, apps, and interfaces that all use media analytics. As I already explained, I see media analytics as the new stage in the development of modern technological media. This stage is characterized by algorithmic large-scale analysis of media and user interactions and the use of the results in algorithmic decision making such as contextual advertising, recommendations, search, and other kinds of information retrieval, filtering of search results and user posts, document classification, plagiarism detection, video fingerprinting, content categorization of user photos, automatic news production etc.
And we are still only at the beginning of this stage. Given the trajectory of gradual automation of more and more functions in modern society using algorithms, I expect that production and customization of many forms of at least “commercial culture” (characterized by conventions, genre expectations, and templates) will also be gradually automated. So, in the future already developed digital distribution platforms and media analytics will be joined by the third part: algorithmic media generation. We can see this at work already today in automatically generated news stories, online content written about topics suggested by algorithms, production of some television shows, and TV broadcasts during sport events where multiple robotic cameras automatically follow and zoom into dynamic human performances.
Until ten years ago, key cultural techniques we used to represent and reason about the world and other humans included natural languages, lens-based photo and video imaging, various other media for preserving and accessing information, calculus, digital computers, and computer networks. The core concepts of data/AI society are now as important. They form data society’s “mind”—the particular ways of encountering, understanding, and acting on the world and the humans. And this is why even if you have no intention of doing practical Cultural Analytics research yourself, you need anyway to become familiar with these new data-centred cultural techniques.
While both media analytics in industry and Cultural Analytics research use dozens of algorithms, behind them there is a small number of fundamental paradigms. We can think them as types of data/AI society’s cognition. The three most general ones are data visualization, unsupervised machine learning, and supervised machine learning. Others are feature extraction, clustering, dimension reduction, classification, regression, network science, time series analysis, and information retrieval.
Research Interests:
AI plays a crucial role in global cultural ecosystem. It recommends what we should see, listen to, read, and buy. It determines how many people will see our shared content. It helps us make aesthetic decisions when we create media. In... more
AI plays a crucial role in global cultural ecosystem. It recommends what we should see, listen to, read, and buy. It determines how many people will see our shared content. It helps us make aesthetic decisions when we create media. In professional cultural production, AI has already been adopted to produce movie trailers, music albums, fashion items, product and web designs, architecture, etc.
In this short book Lev Manovich offers a systematic framework to help us think about cultural uses of AI today and in the future. He challenges existing ideas and gives us new concepts for understanding media, design, and aesthetics in the AI era.
In this short book Lev Manovich offers a systematic framework to help us think about cultural uses of AI today and in the future. He challenges existing ideas and gives us new concepts for understanding media, design, and aesthetics in the AI era.
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Excerpt: My methodological shift from studying single visual artifacts to analyzing massive collections of such artifacts parallels the shift in how we experience visual culture. Single-artifact research and “close reading” was logical... more
Excerpt:
My methodological shift from studying single visual artifacts to analyzing massive collections of such artifacts parallels the shift in how we experience visual culture. Single-artifact research and “close reading” was logical for 20th century when as cultural consumers we also were focusing on single works. We went to cinema to see a particular movie, or to a museum to see particular artworks, or listened to a single music recording at home over and over. The media available to us was limited in numbers and we would spend significant time with individual artifacts. I remember, for example, that as a teenager looking hundreds of times though the same books with art reproductions in our home library. A few images of modern art from these books that particularly touched me would be imprinted in my memory.
And now? Visual search and recommendations in Google, Yandex, YouTube, Instagram or Pinterest expose us to endless images and video, while websites of major museums, invite us to browse hundreds of thousands of digitized artworks and historical artifacts. A visual “message” or a “sign” (to use semiotic terms) is now never is isolation but instead is a part of the large series which we experience as infinite. (Do you have a feeling for how two billion images people share daily look like? If it was four billion, would you notice?)
My methodological shift from studying single visual artifacts to analyzing massive collections of such artifacts parallels the shift in how we experience visual culture. Single-artifact research and “close reading” was logical for 20th century when as cultural consumers we also were focusing on single works. We went to cinema to see a particular movie, or to a museum to see particular artworks, or listened to a single music recording at home over and over. The media available to us was limited in numbers and we would spend significant time with individual artifacts. I remember, for example, that as a teenager looking hundreds of times though the same books with art reproductions in our home library. A few images of modern art from these books that particularly touched me would be imprinted in my memory.
And now? Visual search and recommendations in Google, Yandex, YouTube, Instagram or Pinterest expose us to endless images and video, while websites of major museums, invite us to browse hundreds of thousands of digitized artworks and historical artifacts. A visual “message” or a “sign” (to use semiotic terms) is now never is isolation but instead is a part of the large series which we experience as infinite. (Do you have a feeling for how two billion images people share daily look like? If it was four billion, would you notice?)
Research Interests:
Digitization of cultural heritage over last 20 years has opened up very interesting possibilities for the study of our cultural past using computational “big data” methods. Today, as over two billion people create global “digital culture”... more
Digitization of cultural heritage over last 20 years has opened up very interesting possibilities for the study of our cultural past using computational “big data” methods. Today, as over two billion people create global “digital culture” by sharing their photos, video, links, writing posts, comments, ratings, etc., we can also use the same methods to study this universe of contemporary digital culture.
In this chapter I discuss a number of issues regarding the “shape” of the digital visual collections we have, from the point of view of researchers who use computational methods. They are working today in many fields including computer science, computational sociology, digital art history, digital humanities, digital heritage and Cultural Analytics – which is the term I introduced in 2007 to refer to all of this research, and also to a particular research program of our own lab that has focused on exploring large visual collections.
Regardless of what analytical methods are used in this research, the analysis has to start with some concrete existing data. The “shapes” of existing digital collections may enable some research directions and make others more difficult. So what is the data universe created by digitization, what does it make possible, and also impossible?
In this chapter I discuss a number of issues regarding the “shape” of the digital visual collections we have, from the point of view of researchers who use computational methods. They are working today in many fields including computer science, computational sociology, digital art history, digital humanities, digital heritage and Cultural Analytics – which is the term I introduced in 2007 to refer to all of this research, and also to a particular research program of our own lab that has focused on exploring large visual collections.
Regardless of what analytical methods are used in this research, the analysis has to start with some concrete existing data. The “shapes” of existing digital collections may enable some research directions and make others more difficult. So what is the data universe created by digitization, what does it make possible, and also impossible?
Research Interests:
Millions of people around the world today use digital tools and platforms to create and share sophisticated cultural artifacts. This book focuses on one such platform: Instagram. It places Instagram image culture within a rich cultural... more
Millions of people around the world today use digital tools and platforms to create and share sophisticated cultural artifacts. This book focuses on one such platform: Instagram. It places Instagram image culture within a rich cultural and historical context, including histories of photography, cinema, graphic design, as well as contemporary social media, design trends, music video, and k-pop. At the same it uses Instagram as a window into the identities of a young global generation connected by common social media platforms, cultural sensibilities, and visual aesthetics.
My book is an experiment to see how we can combine traditional “qualitative” approaches of media theory and art history with quantitative analysis that uses “big cultural data” and computational methods. I am drawing on the analysis of 15 million images shared on Instagram in 16 global cities during 2012–2015 carried out in our Cultural Analytics Lab, publications from many other labs, my own informal observations from using Instagram for five years, and my direct observations of mobile phone photography cultures during 2010–2015 in 58 cities located in 31 countries.
My book is an experiment to see how we can combine traditional “qualitative” approaches of media theory and art history with quantitative analysis that uses “big cultural data” and computational methods. I am drawing on the analysis of 15 million images shared on Instagram in 16 global cities during 2012–2015 carried out in our Cultural Analytics Lab, publications from many other labs, my own informal observations from using Instagram for five years, and my direct observations of mobile phone photography cultures during 2010–2015 in 58 cities located in 31 countries.
Research Interests:
In the original vision of artificial intelligence (AI) in 1950s, the goal was to teach computer to perform a range of cognitive tasks. They included playing chess, solving mathematical problems, understanding written and spoken language,... more
In the original vision of artificial intelligence (AI) in 1950s, the goal was to teach computer to perform a range of cognitive tasks. They included playing chess, solving mathematical problems, understanding written and spoken language, recognizing content of images, and so on. Today, AI (especially in the form of supervised machine learning) has become a key instrument of modern economies employed to make them more efficient and secure: making decisions on consumer loans, filtering job applications, detecting fraud, and so on.
What has been less obvious is that AI now plays an equally important role in our cultural lives, increasingly automating the realm of the aesthetic. Consider, for example, image culture. Instagram Explore screen recommends images and videos based on what we liked in the past. Artsy.net recommends the artworks similar to the one you are currently viewing on the site. All image apps can automatically modify captured photos according to the norms of "good photography." Other apps "beatify" selfies. Still other apps automatically edit your raw video to create short films in the range of styles. The App The Roll from EyeEm automatically rates aesthetic quality of you photos. (. . . )
Does such automation leads to decrease in cultural diversity over time? For example, does automatic edits being applied to user photos leads to standardization of “photo imagination”? As opposed to guessing or just following our often un-grounded intuitions, can we use AI methods and large samples of cultural data to measure quantitatively diversity and variability in contemporary culture, and track how they are changing over time?
What has been less obvious is that AI now plays an equally important role in our cultural lives, increasingly automating the realm of the aesthetic. Consider, for example, image culture. Instagram Explore screen recommends images and videos based on what we liked in the past. Artsy.net recommends the artworks similar to the one you are currently viewing on the site. All image apps can automatically modify captured photos according to the norms of "good photography." Other apps "beatify" selfies. Still other apps automatically edit your raw video to create short films in the range of styles. The App The Roll from EyeEm automatically rates aesthetic quality of you photos. (. . . )
Does such automation leads to decrease in cultural diversity over time? For example, does automatic edits being applied to user photos leads to standardization of “photo imagination”? As opposed to guessing or just following our often un-grounded intuitions, can we use AI methods and large samples of cultural data to measure quantitatively diversity and variability in contemporary culture, and track how they are changing over time?
Research Interests:
In this article we make a case for a systematic application of complex network science to study art market history and more general collection dynamics. We reveal social, temporal, spatial, and conceptual network dimensions, i.e. network... more
In this article we make a case for a systematic application of complex network science to study art market history and more general collection dynamics. We reveal social, temporal, spatial, and conceptual network dimensions, i.e. network node and link types, previously implicit in the Getty Provenance Index® (GPI). 1 As a pioneering art history database active since the 1980s, the GPI provides online access to source material relevant for research in the history of collecting and art markets. Based on a subset of the GPI, we characterize an aggregate of more than 267,000 sales transactions connected to roughly 22,000 actors in four countries over 20 years at daily resolution from 1801 to 1820. Striving towards a deeper understanding on multiple levels we disambiguate social dynamics of buying, brokering, and selling, while observing a general broadening of the market, where large collections are split into smaller lots. Temporally, we find annual market cycles that are shifted by country and obviously favor international exchange. Spatially, we differentiate near-monopolies from regions driven by competing sub-centers, while uncovering asymmetries of international market flux. Conceptually, we track dynamics of artist attribution that clearly behave like product categories in a very slow supermarket. Taken together, we introduce a number of meaningful network perspectives dealing with historical art auction data, beyond the analysis of social networks within a single market region. The results presented here have inspired a Linked Open Data conversion of the GPI, which is currently in process and will allow further analysis by a broad set of researchers. 2
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Deluge became a metaphor to describe the amount of information to which we are subjected, and very often we feel we are drowning while our access to information is rising. Devising mechanisms for exploring massive image sets according to... more
Deluge became a metaphor to describe the amount of information to which we are subjected, and very often we feel we are drowning while our access to information is rising. Devising mechanisms for exploring massive image sets according to perceptual attributes is still a challenge, even more when dealing with user-generated social media content. Such images tend to be heteroge-nous, and using metadata-only can be misleading. This paper describes a set of tools designed to analyze large sets of user-created art related images using image features describing color, texture, composition and orientation. The proposed pipeline permits to discriminate Flickr groups in terms of feature vectors and clustering parameters. The algorithms are general enough to be applied to other domains in which the main question is about the variability of the images.
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The concept of “aesthetics” has a unique relation to media studies. I can’t think of another concept that is so central to the modern culture industries and yet also to the creation of media by individuals – such as the tens of millions... more
The concept of “aesthetics” has a unique relation to media studies. I can’t think of another concept that is so central to the modern culture industries and yet also to the creation of media by individuals – such as the tens of millions of people worldwide today who use digital tools to make aesthetically refined photos for posting on Instagram, or the hundreds of millions that have the means to purchase beautiful designer clothes and home décor items. The Pinterest social network that reached 100 million users in 2015 is dominated by images of beautiful cloves, home décor, crafts, fashion, parties ideas, etc. The photos that we see around us every day have been refined in Photoshop to achieve visual perfection, and cinematography similarly uses digital tools to control precisely the aesthetics of every shot and frame.
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Instagram is the perfect medium of the "aesthetic society." In such society, production and presentation of beautiful images, experiences, styles, and user interaction designs is central for its economic and social functioning. Aesthetic... more
Instagram is the perfect medium of the "aesthetic society." In such society, production and presentation of beautiful images, experiences, styles, and user interaction designs is central for its economic and social functioning. Aesthetic society values space designers, user experience designers, architects, photographers, models, stylists, and other design and media professionals, as well as individuals who are skilled in using Instagram, other social networks and blog platforms, and media editing, creation, and analytics tools. “Using” in this context refers to creating successful content, promoting this content, communicating with followers, and achieving desired goals.
Aesthetic society is also the one where urban / social media tribes emerge and sustain themselves through aesthetic choices and experience. In the words of Michel Maffesoli who developed analysis of “urban tribe” already in 1980s, “it refers to a certain ambience, a state of mind, and it is preferably to be expressed through lifestyles that favor appearance and form.” And the ambience and state of mind, as I argued in Chapter 3, is exactly the “message” of Instagramism. If in the modern societies carefully constructed aesthetic lifestyles were the privilege of the rich, today they are available to all who use Instagram, VSCO, or any other of 2000+ photo editing apps, or shop at Zara which offers cool, hip and refined styles in its 2200 stores in 88 countries (2015 data).
Aesthetic society is also the one where urban / social media tribes emerge and sustain themselves through aesthetic choices and experience. In the words of Michel Maffesoli who developed analysis of “urban tribe” already in 1980s, “it refers to a certain ambience, a state of mind, and it is preferably to be expressed through lifestyles that favor appearance and form.” And the ambience and state of mind, as I argued in Chapter 3, is exactly the “message” of Instagramism. If in the modern societies carefully constructed aesthetic lifestyles were the privilege of the rich, today they are available to all who use Instagram, VSCO, or any other of 2000+ photo editing apps, or shop at Zara which offers cool, hip and refined styles in its 2200 stores in 88 countries (2015 data).
Research Interests:
In this paper we theorize, visualize, and analyze the relation between physical places and their social media representations, and describe the characteristics of hyper-locality in social media. While the term " hyper-local " has been... more
In this paper we theorize, visualize, and analyze the relation between physical places and their social media representations, and describe the characteristics of hyper-locality in social media. While the term " hyper-local " has been recently used to describe social media that is produced in particular locations and time periods , existing research has not raised important questions about representation and experience. How is the physical place performed through social media data? How do we experience locality via social media platforms? Our work combines quantitative and qualitative analysis , and employs perspectives from the fields of Digital Humanities and Art History that have yet to be used in social media research. We offer a theory of hyper-local social media, and theorize its manifestations and operations using a particular case study. We start by historicizing the hyper-local, drawing parallels between conceptualizations of " site-specific " art-works created in the 1970s and current organization of geo-temporal social media images. Next, we exemplify the hyper-local using the case study of the famous street artist Banksy's month-long residency in NYC during October 2013. We analyze and visualize 28,419 Insta-gram photos of these artworks to explore how these photos represent space and time specific events, as well as add new meanings to Banksy's original images. Finally , we offer a theoretical analysis, proposing what we see as some of the key characterizations of hyper-local social media data.
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Co-authors: Miriam Redi (Bell Labs), Damon Crockett (UCSD), and Simon Osindero (Flickr). Billions of photos shared online today are created by people with different socioeconomic characteristics living in different locations. We introduce... more
Co-authors: Miriam Redi (Bell Labs), Damon Crockett (UCSD), and Simon Osindero (Flickr).
Billions of photos shared online today are created by people with different socioeconomic characteristics living in different locations. We introduce a number of methods for quantifying the differences between such " photo cultures " and apply them to a large collection of Instagram images shared in five mega-cities around the world. First, we extract image content and style features and use them to design a new visualization technique for qualitative analysis of photo cultures. We then use supervised learning to automatically recognize and compare visual activity at different locations and expose surprising connections between geographically distant photo cultures. Finally, we perform a low-level quantitative analysis to understand what makes photo cultures different from each other.
Billions of photos shared online today are created by people with different socioeconomic characteristics living in different locations. We introduce a number of methods for quantifying the differences between such " photo cultures " and apply them to a large collection of Instagram images shared in five mega-cities around the world. First, we extract image content and style features and use them to design a new visualization technique for qualitative analysis of photo cultures. We then use supervised learning to automatically recognize and compare visual activity at different locations and expose surprising connections between geographically distant photo cultures. Finally, we perform a low-level quantitative analysis to understand what makes photo cultures different from each other.
Research Interests:
This article describes the newest stage in the development of modern technological media. I call this stage “media analytics.” It follows the previous stages of massive reproduction (1500–), broadcasting (1920–), the use of computers for... more
This article describes the newest stage in the development of modern technological media. I call this stage “media analytics.” It follows the previous stages of massive reproduction (1500–), broadcasting (1920–), the use of computers for media creation workflows (1981–), the Web as global content creation and distribution network (1993–), and social media platforms (2004–), to name just a few such stages. Unlike other stages, the new stage is not focused on new mechanisms for creation, publishing, or distribution of media, although it also affects these operations. Instead, this new stage is about automatic computational analysis of the content of all online digital media, personal online behaviors and communication, and automatic actions based on this analysis.
Media analytics is the new stage of media technology that impacts everyday cultural experiences of significant percentages of populations in dozens of countries who use Internet and computing devices. (For figures about use of Internet and social media in the USA by different demographics groups, see latest Pew Research Center Internet & Tech reports.) (…)
While media analytics technologies and concepts are widely discussed in computer and data sciences in business publications, in conferences and trade shows, in leading science journals, and being taught to millions of students worldwide in computer science and data science classes, they are not discussed in either popular press or by academics outside of technology and science fields.
This lack of systematic knowledge on the part of many academics and journalists who write about digital cultures about the details of the computational processes that drive web services, apps, desktop applications, video games, search, image detection, voice recognition, recommendation systems, behavioral advertising, and so on, as well as contemporary software engineering and the field of data science in general often prevents them, in my view, from seeing the full picture. (Understanding of many of these details does require knowledge of computer science, and today very few people in academic humanists, social sciences or journalism have this background.)
Media analytics is the new stage of media technology that impacts everyday cultural experiences of significant percentages of populations in dozens of countries who use Internet and computing devices. (For figures about use of Internet and social media in the USA by different demographics groups, see latest Pew Research Center Internet & Tech reports.) (…)
While media analytics technologies and concepts are widely discussed in computer and data sciences in business publications, in conferences and trade shows, in leading science journals, and being taught to millions of students worldwide in computer science and data science classes, they are not discussed in either popular press or by academics outside of technology and science fields.
This lack of systematic knowledge on the part of many academics and journalists who write about digital cultures about the details of the computational processes that drive web services, apps, desktop applications, video games, search, image detection, voice recognition, recommendation systems, behavioral advertising, and so on, as well as contemporary software engineering and the field of data science in general often prevents them, in my view, from seeing the full picture. (Understanding of many of these details does require knowledge of computer science, and today very few people in academic humanists, social sciences or journalism have this background.)
Research Interests:
Notes on Instagrammism and mechanisms of contemporary cultural identity (and also photography, design, Kinfolk, k-pop, hashtags, mise-en-scène, and cостояние). How do we characterize a style in general? And, in particular, the... more
Notes on Instagrammism and mechanisms of contemporary
cultural identity (and also photography, design, Kinfolk, k-pop,
hashtags, mise-en-scène, and cостояние).
How do we characterize a style in general? And, in particular, the contemporary aesthetic that was born in early 2010s and can be seen today in numerous Instagram photos? And are there any differences today between commercial and personal photography even when they feature the same subjects and the same attitude? Can a style be defined through a list of features, or is it a larger gestalt that cannot be simply detected by finding images that have some of these features?
cultural identity (and also photography, design, Kinfolk, k-pop,
hashtags, mise-en-scène, and cостояние).
How do we characterize a style in general? And, in particular, the contemporary aesthetic that was born in early 2010s and can be seen today in numerous Instagram photos? And are there any differences today between commercial and personal photography even when they feature the same subjects and the same attitude? Can a style be defined through a list of features, or is it a larger gestalt that cannot be simply detected by finding images that have some of these features?
Research Interests:
Social media content shared today in cities, such as Instagram images, their tags and descriptions, is the key form of contemporary city life. It tells people where activities and locations that interest them are and it allows them to... more
Social media content shared today in cities, such as Instagram images, their tags and descriptions, is the key form of contemporary city life. It tells people where activities and locations that interest them are and it allows them to share their urban experiences and self-representations.
Therefore, any analysis of urban structures and cultures needs to consider social media activity. In our paper, we introduce the novel concept of social media inequality. This concept allows us to quantitatively compare pattern in social media activities between parts of a city, a number of cities, or any other spatial areas.
We define this concept using an analogy with the concept of economic inequality. Economic inequality indicates how some economic characteristics or material resources, such as income, wealth or consumption are distributed in a city, country or between countries. Accordingly, we define social media inequality as unequal spatial distribution of social media sharing in a particular geographic area or between areas. To quantify such distributions, we can use many characteristics of social media such as number of people sharing it, the number of photos they have shared, their content, and user assigned tags.
We propose that the standard inequality measures used in other disciplines, such as the Gini coefficient, can also be used to characterize social media inequality. To test our ideas, we use a dataset of 7,442,454 public geo-coded Instagram images shared in Manhattan during five months (March-July) in 2014, and also selected data for 287 Census tracts in Manhattan. We compare patterns in Instagram sharing for locals and for visitors for all tracts, and also for hours in a 24 hour cycle. We also look at relations between social media inequality and socio-economic inequality using selected indicators for Census tracts. The inequality of Instagram sharing in Manhattan turns out to be bigger than inequalities in levels of income, rent, and unemployment.
Therefore, any analysis of urban structures and cultures needs to consider social media activity. In our paper, we introduce the novel concept of social media inequality. This concept allows us to quantitatively compare pattern in social media activities between parts of a city, a number of cities, or any other spatial areas.
We define this concept using an analogy with the concept of economic inequality. Economic inequality indicates how some economic characteristics or material resources, such as income, wealth or consumption are distributed in a city, country or between countries. Accordingly, we define social media inequality as unequal spatial distribution of social media sharing in a particular geographic area or between areas. To quantify such distributions, we can use many characteristics of social media such as number of people sharing it, the number of photos they have shared, their content, and user assigned tags.
We propose that the standard inequality measures used in other disciplines, such as the Gini coefficient, can also be used to characterize social media inequality. To test our ideas, we use a dataset of 7,442,454 public geo-coded Instagram images shared in Manhattan during five months (March-July) in 2014, and also selected data for 287 Census tracts in Manhattan. We compare patterns in Instagram sharing for locals and for visitors for all tracts, and also for hours in a 24 hour cycle. We also look at relations between social media inequality and socio-economic inequality using selected indicators for Census tracts. The inequality of Instagram sharing in Manhattan turns out to be bigger than inequalities in levels of income, rent, and unemployment.
Research Interests:
What are some of the types of Instagram photos today and how they relate to the 20th century photo culture? I analyze three common types of Instagram photos. We call these types "casual," "professional," and "designed." "Casual" photos... more
What are some of the types of Instagram photos today and how they relate to the 20th century photo culture? I analyze three common types of Instagram photos. We call these types "casual," "professional," and "designed." "Casual" photos are similar in function to personal photographers of the 20th century: they are created for friends; they privilege content of photos and ignore the aesthetics. Both “professional” and “designed” photo type are examples of what Alise Tifentale calls “competitive photography.” The difference is whom the authors compete with for likes and followers. The authors of professional photos aim for “good photo” aesthetics established in the second part of the 20th century, so they compete with other authors and lovers of such “classic” aesthetics including many commercial photographers. The authors of “designed” photos associate themselves with more “contemporary,” hip,” “cool” and “urban” lifestyle choices and corresponding aesthetics, so this is their peer group on Instagram.
The first part of “Subjects and Styles in Instagram Photography” discussed the casual type. This second part discusses professional and designed types.
My text is an experiment to see how we can combine traditional “qualitative” approach of media theory and art history and newer quantitative analysis that uses “big cultural data” and computational methods. I draw on the analysis of 15 million images shared on Instagram in 16 global cities during 2012-2015 carried out in our lab (softwarestudies.com); results from other labs; my own informal observations from using Instagram for 3 years; and histories of photography, art and design.
The first part of “Subjects and Styles in Instagram Photography” discussed the casual type. This second part discusses professional and designed types.
My text is an experiment to see how we can combine traditional “qualitative” approach of media theory and art history and newer quantitative analysis that uses “big cultural data” and computational methods. I draw on the analysis of 15 million images shared on Instagram in 16 global cities during 2012-2015 carried out in our lab (softwarestudies.com); results from other labs; my own informal observations from using Instagram for 3 years; and histories of photography, art and design.
Research Interests:
What are some of the types of Instagram photos today and how they relate to the 20th century photo culture? Based on the analysis of fifteen million images shared on Instagram in sixteen global cities during 2012-2015 carried in our lab,... more
What are some of the types of Instagram photos today and how they relate to the 20th century photo culture? Based on the analysis of fifteen million images shared on Instagram in sixteen global cities during 2012-2015 carried in our lab, this text presents the analysis of three common types of Instagram photos. We call these types "casual," "professional," and "designed."
"Casual" photos are similar in function to personal photographers of the 20th century: they are created for friends, they privilege content of photos and ignore the aesthetics. Both “professional” and “designed” photo type are examples of what Alise Tifentale calls “competitive photography.” The difference is whom the authors compete with for likes and followers. The authors of professional photos aim for “good photo” aesthetics established in the second part of the 20th century, so they compete with other authors and lovers of such “classic” aesthetics including many commercial photographers. The authors of “designed” photos associate themselves with more “contemporary,” hip,” “cool” and “urban” lifestyle choices and corresponding aesthetics, so this is their peer group on Instagram.
This first part of the text explains why Instagram platform is perfect to study contemporary photography; discusses the importance of differences in content and style in photos shared in different locations worldwide, and then focuses on "casual" photo type.
"Casual" photos are similar in function to personal photographers of the 20th century: they are created for friends, they privilege content of photos and ignore the aesthetics. Both “professional” and “designed” photo type are examples of what Alise Tifentale calls “competitive photography.” The difference is whom the authors compete with for likes and followers. The authors of professional photos aim for “good photo” aesthetics established in the second part of the 20th century, so they compete with other authors and lovers of such “classic” aesthetics including many commercial photographers. The authors of “designed” photos associate themselves with more “contemporary,” hip,” “cool” and “urban” lifestyle choices and corresponding aesthetics, so this is their peer group on Instagram.
This first part of the text explains why Instagram platform is perfect to study contemporary photography; discusses the importance of differences in content and style in photos shared in different locations worldwide, and then focuses on "casual" photo type.
Research Interests:
Many discussions of photography and other types of visual culture including user-generated content often rely on professional—amateur distinction. In this article we introduced a different pair of concepts: competitive—non-competitive. We... more
Many discussions of photography and other types of visual culture including user-generated content often rely on professional—amateur distinction. In this article we introduced a different pair of concepts: competitive—non-competitive. We believe that analyzing photography history and present such as Instagram’s visual universe using these new concepts allows us to notice phenomena and patterns that traditional professional—amateur distinction hides. The analysis of presentation of self in online digital photography is a case in point. We can now see that the selfie genre is complemented by an “anti-selfie” genre that presents the self in a different way. The two genres correspond to different understanding and uses of Instagram by non-competitive and competitive photographs.
Research Interests:
I define Cultural Analytics as “the analysis of massive cultural data sets and flows using computational and visualization techniques,” I developed this concept in 2005, and in 2007 we established a research lab (Software Studies... more
I define Cultural Analytics as “the analysis of massive cultural data sets and flows using computational and visualization techniques,” I developed this concept in 2005, and in 2007 we established a research lab (Software Studies Initiative, softwarestudies.com) to start working on practical projects. The following are the examples of theoretical and practical questions that are driving our work: What does it mean to represent “culture” by “data”? What are the unique possibilities offered by computational analysis of large cultural data in contrast to qualitative methods used in humanities and social science? How to use quantitative techniques to study the key cultural form of our era – interactive media? How can we combine computational analysis and visualization of large cultural data with qualitative methods, including "close reading”? (In other words, how to combine analysis of larger patterns with the analysis of individual artifacts and their details?) How can computational analysis do justice to variability and diversity of cultural artifacts and processes, rather than focusing on the "typical" and "most popular"?
Research Interests:
I present a number of core concepts from data science that are relevant to digital art history and the use of quantitative methods to study any cultural artifacts or processes in general. These concepts are objects, features, data,... more
I present a number of core concepts from data science that are relevant to digital art history and the use of quantitative methods to study any cultural artifacts or processes in general. These concepts are objects, features, data, feature space, and dimension reduction. These concepts enable computational exploration of both large and small visual cultural data. We can analyze relations between works on a single artist, many artists, all digitized production from a whole historical period, holdings in museum collections, collection metadata, or writings about art. The same concepts allow us to study contemporary vernacular visual media using massive social media content. (In our lab, we analyzed works by van Gogh, Mondrian, and Rothko, 6000 paintings by French Impressionists, 20,000 photographs from MoMA photography collection, one million manga pages from manga books, one million artworks of contemporary non-professional artists, and over 13 million Instagram images from 16 global cities.) While data science techniques do not replace other art historical methods, they allow us to see familiar art historical material in new ways, and also to study contemporary digital visual culture.
In addition to their relevance to art history and digital humanities, the concepts are also important by themselves. Anybody who wants to understand how our society “thinks with data” needs to understand these concepts. They are used in tens of thousands of quantitative studies of cultural patterns in social media carried out by computer scientists in the last few years. More generally, these concepts are behind data mining, predictive analytics and machine learning, and their numerous industry applications. In fact, they are as central to our “big data society” as other older cultural techniques we use to represent and reason about the world and each other – natural languages, material technologies for preserving and accessing information (paper, printing, digital media, etc.), counting, calculus, or lens-based photo and video imaging. In short, these concepts form the data society’s “mind” – the particular ways of encountering, understanding, and acting on the world and the humans specific to our era.
In addition to their relevance to art history and digital humanities, the concepts are also important by themselves. Anybody who wants to understand how our society “thinks with data” needs to understand these concepts. They are used in tens of thousands of quantitative studies of cultural patterns in social media carried out by computer scientists in the last few years. More generally, these concepts are behind data mining, predictive analytics and machine learning, and their numerous industry applications. In fact, they are as central to our “big data society” as other older cultural techniques we use to represent and reason about the world and each other – natural languages, material technologies for preserving and accessing information (paper, printing, digital media, etc.), counting, calculus, or lens-based photo and video imaging. In short, these concepts form the data society’s “mind” – the particular ways of encountering, understanding, and acting on the world and the humans specific to our era.
Research Interests:
User-generated visual media such as images and video shared on Instagram, YouTube, Sino Weibo, VK, Flickr and other popular social media services open up amazing opportunities for the study of contemporary visual culture and urban... more
User-generated visual media such as images and video shared on Instagram, YouTube, Sino Weibo, VK, Flickr and other popular social media services open up amazing opportunities for the study of contemporary visual culture and urban environments. By analyzing media shared by millions of users today, we can understand what people around the world imagine and create; how people represent themselves and others; what topics, styles and visual techniques are most popular and most unique, and how these topics and techniques differ between locations, genders, ages, and many other demographic characteristics. In a number of projects completed between 2012 and 2015, we analysed large number of images shared on Instagram by people in urban areas. This article discusses two of these projects: Selfiecity (2014) and On Broadway (2015). In Selfiecity, we compared patterns in self-representations using a collection of “selfie” photos shared on Instagram by people in five global cities. In On Broadway, we focused on a single street in NYC – part of Broadway running through Manhattan for 13 miles – and analysed images shared along Broadway on Instagram and Twitter, Foursquare check-ins, taxi rides, and selected economic and social indicators using U.S. Census data. The article presents our methods, findings, and unique interactive interfaces for explorations of the collected data we constructed for each project.
Research Interests:
Manovich and Tifentale discuss the construction of popular photographic self-representation in digital visual culture. Since 2008, Software Studies Initiative (a research lab led by Manovich) used computational and data visualization... more
Manovich and Tifentale discuss the construction of popular photographic self-representation in digital visual culture. Since 2008, Software Studies Initiative (a research lab led by Manovich) used computational and data visualization methods to analyze large numbers of Instagram photos. The chapter focuses on Selfiecity.net, a research project analyzing 3,200 selfies shared via Instagram from five global cities: Bangkok, Berlin, Moscow, New York, and Sao Paulo. Manovich and Tifentale analyze the construction of the dataset, the choice and application of computational and “manual” methods of image analysis as well as the findings presented as visualizations and as interactive web application. The authors place the selfie into a broader context of history of photography and argue that it is a new sub-genre of photography that differs from the tradition of self-portraiture.
Research Interests:
How can we use computational analysis and visualization of content and interactions on social media network to write histories? Traditionally, historical timelines of social and political upheavals give us only distant views of the... more
How can we use computational analysis and visualization of content and interactions on social media network to write histories? Traditionally, historical timelines of social and political upheavals give us only distant views of the events, and singular interpretation of a person constructing the timeline. However, using social media as our source, we can potentially present many thousands of individual views of the events. We can also include representation of the everyday life next to the accounts of the exceptional events. This paper explores these ideas using a particular case study – images shared by people in Kyiv on Instagram during 2014 Ukrainian Revolution. Using Instagram public API we collected 13208 geo-coded images shared by 6165 Instagram users in the central part of Kyiv during February 17-22, 2014. We used open source and our own custom software tools to analyze the images along with upload dates and times, geo locations, and tags, and visualize them in different ways.
Research Interests:
Mining the constituent parts of this “documentary” can teach us about vernacular photography and habits that govern digital-image making. When people photograph one another, do they privilege particular framing styles, à la a professional... more
Mining the constituent parts of this “documentary” can teach us about vernacular photography and habits that govern digital-image making. When people photograph one another, do they privilege particular framing styles, à la a professional photographer? Do tourists visiting New York photograph the same subjects; are their choices culturally determined? And when they do photograph the same subject (for example, plants on the High Line Park on Manhattan’s West Side), do they use the same techniques?
To begin answering these questions, we can use computers to analyze the visual attributes and content of millions of photographs and their accompanying descriptions, tags, geographical coordinates, and upload dates and times, and then interpret the results.
To begin answering these questions, we can use computers to analyze the visual attributes and content of millions of photographs and their accompanying descriptions, tags, geographical coordinates, and upload dates and times, and then interpret the results.
Research Interests:
In contrast to 20th-century "texts" such as a examined a novel, movie, or TV program, interactive software-driven media often has no finite boundaries. For instance, a user of Google Earth is likely to experience a different "earth" every... more
In contrast to 20th-century "texts" such as a examined a novel, movie, or TV program, interactive software-driven media often has no finite boundaries. For instance, a user of Google Earth is likely to experience a different "earth" every time he or she uses the application. Google could have updated some of the satellite photographs or added new Street Views and 3D buildings. At any time, a user of the application can also load more geospatial data created by other users and companies.
Even when a user is working only with a single local media file stored in his or her computer, the experience is still only partly defined by the file's content and organization. The user is free to navigate the document, choosing both what information to see and the sequence in which to see it. (In Google Earth, I can zoom in and out, switching between a bird's-eye view of the area, and its details; I can also switch between different kinds of maps.)
Most important, software is not hard-wired to any document or machine: New tools can be easily added without changing the documents themselves. With a single click, I can add sharing buttons to my blog, thus enabling new ways to circulate its content. When I open a text document in Mac OS Preview media viewer, I can highlight, add comments and links, draw and add thought bubbles. Photoshop allows me to save my edits on separate "adjustment layers," without modifying the original image. And so on.
All that requires a new way to analyze media and culture. Since the early 2000s, some of us (mostly from new-media studies and digital arts) have been working to meet that challenge. As far as I know, I was the first to use the terms "software studies" and "software theory" in 2001. The field of software studies gradually took shape in the mid-2000s. In 2006, Matthew Fuller, author of the pioneering Behind the Blip: Essays on the Culture of Software (Sagebrush Education Resources, 2003), organized the first Software Studies Workshop in Rotterdam. "Software is often a blind spot in the theorization and study of computational and networked digital media," Fuller wrote in introducing the workshop. "In a sense, all intellectual work is now 'software study,' in that software provides its media and its context, but there are very few places where the specific nature, the materiality, of software is studied except as a matter of engineering.
Even when a user is working only with a single local media file stored in his or her computer, the experience is still only partly defined by the file's content and organization. The user is free to navigate the document, choosing both what information to see and the sequence in which to see it. (In Google Earth, I can zoom in and out, switching between a bird's-eye view of the area, and its details; I can also switch between different kinds of maps.)
Most important, software is not hard-wired to any document or machine: New tools can be easily added without changing the documents themselves. With a single click, I can add sharing buttons to my blog, thus enabling new ways to circulate its content. When I open a text document in Mac OS Preview media viewer, I can highlight, add comments and links, draw and add thought bubbles. Photoshop allows me to save my edits on separate "adjustment layers," without modifying the original image. And so on.
All that requires a new way to analyze media and culture. Since the early 2000s, some of us (mostly from new-media studies and digital arts) have been working to meet that challenge. As far as I know, I was the first to use the terms "software studies" and "software theory" in 2001. The field of software studies gradually took shape in the mid-2000s. In 2006, Matthew Fuller, author of the pioneering Behind the Blip: Essays on the Culture of Software (Sagebrush Education Resources, 2003), organized the first Software Studies Workshop in Rotterdam. "Software is often a blind spot in the theorization and study of computational and networked digital media," Fuller wrote in introducing the workshop. "In a sense, all intellectual work is now 'software study,' in that software provides its media and its context, but there are very few places where the specific nature, the materiality, of software is studied except as a matter of engineering.
Research Interests:
This publication contains all articles I wrote between 1991 and 2007 (65 articles, 288,000 words). We reformatted all articles in the same way and assembled them into a single text document. This is a PDF version of this file. You can... more
This publication contains all articles I wrote between 1991 and 2007 (65 articles, 288,000 words). We reformatted all articles in the same way and assembled them into a single text document. This is a PDF version of this file. You can download another version of this PDF where TOC is interactive - you can click on any title and you will be taken to this text in the file:
http://manovich.net/index.php/projects/lev-manovich-all-articles-1991-2007
You can also download text file version and HTML versions at the same URL.
The text version uses minimal Markdown markup. It can be opened in any text editor, and you can also easily convert it in any other format such as .docx or ebook.
Note: if you use a text editor that understands markup, the text file will be nicely formatted. Examples of such editors include Ulysses, Typora, Draft, IA Writer, Obsidian.
(The second part of this publication with all 2008-2022 articles will come out in a few months).
http://manovich.net/index.php/projects/lev-manovich-all-articles-1991-2007
You can also download text file version and HTML versions at the same URL.
The text version uses minimal Markdown markup. It can be opened in any text editor, and you can also easily convert it in any other format such as .docx or ebook.
Note: if you use a text editor that understands markup, the text file will be nicely formatted. Examples of such editors include Ulysses, Typora, Draft, IA Writer, Obsidian.
(The second part of this publication with all 2008-2022 articles will come out in a few months).
Research Interests:
Book color illustrations: https://www.dropbox.com/s/bsudgi90xm9p1zw/Manovich.figures.Cultural_Analytics.2020.pdf?dl=0 /////// How can we see a billion images? What analytical methods can we bring to bear on the astonishing scale of... more
Book color illustrations:
https://www.dropbox.com/s/bsudgi90xm9p1zw/Manovich.figures.Cultural_Analytics.2020.pdf?dl=0
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How can we see a billion images? What analytical methods can we bring to bear on the astonishing scale of digital culture—the terabytes of photographs shared on social media every day, the hundreds of millions of songs created by twenty million musicians on Sound Cloud, the content of four billion Pinterest boards? In Cultural Analytics, Lev Manovich presents concepts and methods for computational analysis of cultural data, with a particular focus on visual media. Drawing on more than a decade of research and projects from his own lab, Manovich—the founder of the field of cultural analytics—offers a gentle, nontechnical introduction to selected key concepts of data science and discusses the ways that our society uses data and algorithms.
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Manovich offers examples of computational cultural analysis and discusses the shift from “new media” to “more media”; explains how to turn cultural processes into computational data, and introduces concepts for exploring cultural datasets using data visualization as well as other recently developed methods for analyzing image and video datasets. He considers both the possibilities and the limitations of computational methods, and how using them challenges our existing ideas about culture and how to study it.
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Cultural Analytics is a book of media theory. Arguing that before we can theorize digital culture, we need to see it, and that, because of its scale, to see it we need computers, Manovich provides scholars with practical tools for studying contemporary media.
https://www.dropbox.com/s/bsudgi90xm9p1zw/Manovich.figures.Cultural_Analytics.2020.pdf?dl=0
///////
How can we see a billion images? What analytical methods can we bring to bear on the astonishing scale of digital culture—the terabytes of photographs shared on social media every day, the hundreds of millions of songs created by twenty million musicians on Sound Cloud, the content of four billion Pinterest boards? In Cultural Analytics, Lev Manovich presents concepts and methods for computational analysis of cultural data, with a particular focus on visual media. Drawing on more than a decade of research and projects from his own lab, Manovich—the founder of the field of cultural analytics—offers a gentle, nontechnical introduction to selected key concepts of data science and discusses the ways that our society uses data and algorithms.
///////
Manovich offers examples of computational cultural analysis and discusses the shift from “new media” to “more media”; explains how to turn cultural processes into computational data, and introduces concepts for exploring cultural datasets using data visualization as well as other recently developed methods for analyzing image and video datasets. He considers both the possibilities and the limitations of computational methods, and how using them challenges our existing ideas about culture and how to study it.
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Cultural Analytics is a book of media theory. Arguing that before we can theorize digital culture, we need to see it, and that, because of its scale, to see it we need computers, Manovich provides scholars with practical tools for studying contemporary media.
Research Interests: Humanities Computing (Digital Humanities), Communication, Art History, Media Studies, Library Science, and 14 moreDigital Humanities, Film Studies, Film Theory, Internet Studies, Digital Media, Digital History, Library and Information Science, Social Media, Architectural Theory, Visual and Cultural Studies, Data Visualization, Communication Studies, Digital Art History, and Cultural analytics
AI plays a crucial role in the global cultural ecosystem. It recommends what we should see, listen to, read, and buy. It determines how many people will see our shared content. It helps us make aesthetic decisions when we create media. In... more
AI plays a crucial role in the global cultural ecosystem. It recommends what we should see, listen to, read, and buy. It determines how many people will see our shared content. It helps us make aesthetic decisions when we create media. In professional cultural production, AI has already been adapted to produce movie trailers, music albums, fashion items, product and web designs, architecture, etc. In this short book, Lev Manovich offers a systematic framework to help us think about cultural uses of AI today and in the future. He challenges existing ideas and gives us new concepts for understanding media, design, and aesthetics in the AI era.
Research Interests:
Millions of people around the world today use digital tools and platforms to create and share sophisticated cultural artifacts. This book focuses on one such platform: Instagram. It places Instagram image culture within a rich cultural... more
Millions of people around the world today use digital tools and platforms to create and share sophisticated cultural artifacts. This book focuses on one such platform: Instagram. It places Instagram image culture within a rich cultural and historical context, including history of photography, cinema, graphic design, and social media, contemporary design trends, music video, and k-pop. At the same it uses Instagram as a window into the identities of first truly global generation connected by common social media platforms, programming languages, and visual aesthetics. The book demonstrates how humanistic close reading and computational analysis of large datasets can work together by drawing on the work in Manovich's Cultural Analytics Lab with 16 million Instagram photos shared in 17 large cities worldwide since 2012.
Research Interests:
From the publisher: "Software has replaced a diverse array of physical, mechanical, and electronic technologies used before 21st century to create, store, distribute and interact with cultural artifacts. It has become our interface to the... more
From the publisher: "Software has replaced a diverse array of physical, mechanical, and electronic technologies used before 21st century to create, store, distribute and interact with cultural artifacts. It has become our interface to the world, to others, to our memory and our imagination - a universal language through which the world speaks, and a universal engine on which the world runs. What electricity and combustion engine were to the early 20th century, software is to the early 21st century. Offering the first theoretical and historical account of software for media authoring and its effects on the practice and the very concept of 'media,' the author of The Language of New Media (2001) develops his own theory for this rapidly-growing, always-changing field. What was the thinking and motivations of people who in the 1960 and 1970s created concepts and practical techniques that underlie contemporary media software such as Photoshop, Illustrator, Maya, Final Cut and After Effects? How do their interfaces and tools shape the visual aesthetics of contemporary media and design? What happens to the idea of a 'medium' after previously media-specific tools have been simulated and extended in software? Is it still meaningful to talk about different mediums at all? Lev Manovich answers these questions and supports his theoretical arguments by detailed analysis of key media applications such as Photoshop and After Effects, popular web services such as Google Earth, and the projects in motion graphics, interactive environments, graphic design and architecture. Software Takes Command is a must for all practicing designers and media artists and scholars concerned with contemporary media."
Research Interests:
From the publisher: ""Qual è l’anima della cultura contemporanea? Qual è la nostra nuova interfaccia con il mondo, con la nostra memoria e la nostra immaginazione? Quali sono il linguaggio e il motore universale della società... more
From the publisher: ""Qual è l’anima della cultura contemporanea? Qual è la nostra nuova interfaccia con il mondo, con la nostra memoria e la nostra immaginazione? Quali sono il linguaggio e il motore universale della società dell’informazione globale? Il software. A dieci anni di distanza dal Linguaggio dei nuovi media, Lev Manovich ci racconta i nuovi scenari digitali che stanno trasformando profondamente la nostra cultura e le nostre società. La cultura contemporanea è creata o mediata dal software culturale, utilizzato da milioni di individui, che trasporta atomi di cultura sotto forma di contenuti mediali, informazioni e interazioni umane. Questa è la prima storia dello sviluppo della software culture e del suo ruolo nella definizione dell'estetica e dei linguaggi visuali utilizzati oggi dai media."
Research Interests:
From the publisher: "Unter den verschiedenen Gegensätzen, die die Kultur des 20. Jhdts. strukturiert haben [und die wir geerbt haben], gab es den Gegensatz zwischen Kunstgalerie und Kino. Zum einen Hochkultur, zum anderen niedere Kultur.... more
From the publisher: "Unter den verschiedenen Gegensätzen, die die Kultur des 20. Jhdts. strukturiert haben [und die wir geerbt haben], gab es den Gegensatz zwischen Kunstgalerie und Kino. Zum einen Hochkultur, zum anderen niedere Kultur. Zum einen ein ‘White Cube’, zum anderen eine ‘Black Box’.
Angesichts der Ökonomie in der Kunstproduktion - eine der Arten und Weisen, in der Objekte von individuellen Künstlern geschaffen wurden, haben die Künstler des 20. Jhdts. viel Energie aufgewandt, um experimentell herauszufinden, was im Innern der neutralen Hülle eines ‘White Cube’ untergebracht werden kann, indem sie den flachen und rechteckigen Rahmen aufbrachen [und sich in die dritte Dimension begaben], indem sie den ganzen Boden bedeckten, Objekte an die Decke hängten [, und so weiter]. Mit anderen Worten, wenn wir eine Analogie zwischen einem Kunstobjekt und einem [digitalen] Computer herstellen, können wir sagen, dass in der modernen Kunst sowohl das „physikalische Interface” als auch das „Software- Interface“ eines Kunstobjektes nicht festgelegt, sondern offen für das Experiment waren."
Angesichts der Ökonomie in der Kunstproduktion - eine der Arten und Weisen, in der Objekte von individuellen Künstlern geschaffen wurden, haben die Künstler des 20. Jhdts. viel Energie aufgewandt, um experimentell herauszufinden, was im Innern der neutralen Hülle eines ‘White Cube’ untergebracht werden kann, indem sie den flachen und rechteckigen Rahmen aufbrachen [und sich in die dritte Dimension begaben], indem sie den ganzen Boden bedeckten, Objekte an die Decke hängten [, und so weiter]. Mit anderen Worten, wenn wir eine Analogie zwischen einem Kunstobjekt und einem [digitalen] Computer herstellen, können wir sagen, dass in der modernen Kunst sowohl das „physikalische Interface” als auch das „Software- Interface“ eines Kunstobjektes nicht festgelegt, sondern offen für das Experiment waren."
Research Interests:
From the publisher: "In this book Lev Manovich offers the first systematic and rigorous theory of new media. He places new media within the histories of visual and media cultures of the last few centuries. He discusses new media's... more
From the publisher: "In this book Lev Manovich offers the first systematic and rigorous theory of new media. He places new media within the histories of visual and media cultures of the last few centuries. He discusses new media's reliance on conventions of old media, such as the rectangular frame and mobile camera, and shows how new media works create the illusion of reality, address the viewer, and represent space. He also analyzes categories and forms unique to new media, such as interface and database.
Manovich uses concepts from film theory, art history, literary theory, and computer science and also develops new theoretical constructs, such as cultural interface, spatial montage, and cinegratography. The theory and history of cinema play a particularly important role in the book. Among other topics, Manovich discusses parallels between the histories of cinema and of new media, digital cinema, screen and montage in cinema and in new media, and historical ties between avant-garde film and new media."
Manovich uses concepts from film theory, art history, literary theory, and computer science and also develops new theoretical constructs, such as cultural interface, spatial montage, and cinegratography. The theory and history of cinema play a particularly important role in the book. Among other topics, Manovich discusses parallels between the histories of cinema and of new media, digital cinema, screen and montage in cinema and in new media, and historical ties between avant-garde film and new media."
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From the publisher: "Lev Manovič je jedan od vodećih teoretičara novih medija, profesor teorije i prakse digitalnih umetnosti na Kalifornijskom univerzitetu u San Dijegu. U svojim istraživanjima genealogije jezika novih medija, Manovič... more
From the publisher: "Lev Manovič je jedan od vodećih teoretičara novih medija, profesor teorije i prakse digitalnih umetnosti na Kalifornijskom univerzitetu u San Dijegu. U svojim istraživanjima genealogije jezika novih medija, Manovič mapira istorijske i teleološke trajektorije komunikacijskih, reprezentacijskih i formalnih tehnika novih medija, polazeći od poznatog načela da nijedan medij ne deluje izolovano od drugih i da pronalazak svakog novog medija nužno referiše na prethodna iskustva."
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Info-Aesthetics scans contemporary culture to detect emerging aesthetics and computer-based cultural forms specific to information society. Its method is a systematic comparison of our own period with the beginning of the 20th century... more
Info-Aesthetics scans contemporary culture to detect emerging aesthetics and computer-based cultural forms specific to information society. Its method is a systematic comparison of our own period with the beginning of the 20th century when modernist artists created new aesthetics, new forms, new representational techniques, and new symbols of industrial society. How can we go about searching for their equivalents in information society – and does this very question make sense? Can there be forms specific to information society, given that software and computer networks redefine the very concept of form as something solid, stable and limited in space and time?
There are radically new representational techniques unique to own time, given that new media has largely been used in the service of older visual languages and media practices: Web TV, electronic book, interactive cinema? Can information society be represented iconically, if all its most characteristic activities – information processing, interaction between a human and a computer, telecommunication, networking – are dynamic processes? How does the super-human scale of our information structures – from 16 million lines of computer codes making Windows OS, to forty years which would take one viewer to watch all video interviews stored on digital servers of the Shoah Foundation, to the Web itself which cannot be even mapped as a whole – be translated to the scale of human perception and cognition?
In short, if the shift from modernism to informationalism (the term of Manual Castells) has been accompanied by a shift from form to information, can we reduce information to forms, meaningful to a human?
There are radically new representational techniques unique to own time, given that new media has largely been used in the service of older visual languages and media practices: Web TV, electronic book, interactive cinema? Can information society be represented iconically, if all its most characteristic activities – information processing, interaction between a human and a computer, telecommunication, networking – are dynamic processes? How does the super-human scale of our information structures – from 16 million lines of computer codes making Windows OS, to forty years which would take one viewer to watch all video interviews stored on digital servers of the Shoah Foundation, to the Web itself which cannot be even mapped as a whole – be translated to the scale of human perception and cognition?
In short, if the shift from modernism to informationalism (the term of Manual Castells) has been accompanied by a shift from form to information, can we reduce information to forms, meaningful to a human?
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From the publisher: "Taking its title from a Russian word that can refer to the 'texture' of life, painting, or writing, this anthology assembles thirteen key essays in art history and cultural theory by Russian-language writers. The... more
From the publisher: "Taking its title from a Russian word that can refer to the 'texture' of life, painting, or writing, this anthology assembles thirteen key essays in art history and cultural theory by Russian-language writers. The essays erase boundaries between high and low, official and dissident, avant-garde and socialist realism, art and everyday life. Everything visual is deemed worthy of analysis, whether painting or propaganda banners, architecture or candy wrappers, mass celebrations or urban refuse.
Most of the essays appear here in English for the first time. The editors have selected works of the past twenty years by philosophers, literary critics, film scholars, and art historians. Also included are influential earlier essays by Mikhail Bakhtin, V. N. Voloshinov, and Sergei Eisenstein. Compiled for general readers and specialists alike, Tekstura is a valuable resource for anyone interested in Russian and Soviet cultural history or in new theoretical approaches to the visual."
Most of the essays appear here in English for the first time. The editors have selected works of the past twenty years by philosophers, literary critics, film scholars, and art historians. Also included are influential earlier essays by Mikhail Bakhtin, V. N. Voloshinov, and Sergei Eisenstein. Compiled for general readers and specialists alike, Tekstura is a valuable resource for anyone interested in Russian and Soviet cultural history or in new theoretical approaches to the visual."
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Neste artigo, originalmente publicado em outubro de 2010 na página eletrônica pessoal de Lev Manovich (http://manovich.net), o autor faz uma análise dos princípios fundamentais da visualização de informação (infovis) nos últimos 300 anos... more
Neste artigo, originalmente publicado em outubro de 2010 na página eletrônica pessoal de Lev Manovich (http://manovich.net), o autor faz uma análise dos princípios fundamentais da visualização de informação (infovis) nos últimos 300 anos e discute seu desenvolvimento no século XXI.
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Tendo como aporte teórico-metodológico o paradigma da complexidade e como objeto de análise softwares utilizados na exposição Abstraction now, o presente ensaio investiga as transformações na produção e usos de imagens abstratas ocorridas... more
Tendo como aporte teórico-metodológico o paradigma da complexidade e como objeto de análise softwares utilizados na exposição Abstraction now, o presente ensaio investiga as transformações na produção e usos de imagens abstratas ocorridas na sociedade informacional global a partir do surgimento de imagens numéricas.
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A mediados de la década de los noventa asistimos a una transformación fundamental en la cultura de la imagen en movimiento. Los medios de comunicación por separado―películas cinematográficas, gráficos, fotografía, animación, animación 3D... more
A mediados de la década de los noventa asistimos a una transformación fundamental en la cultura de la imagen en movimiento. Los medios de comunicación por separado―películas cinematográficas, gráficos, fotografía, animación, animación 3D por ordenador y la tipografía―comenzaron a combinarse de mil maneras. De este modo, finalizando la década, el medio" puro" de las imágenes en movimiento se volvió una excepción y los medios híbridos, la norma.
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En aquesta entrevista, realitzada per Marta García Quiñones i Daniel Ranz de la revista de pensament Mania, de la Facultat de Filosofia de la Universitat de Barcelona, el teòric dels nous media ens parla del seu llibre" The Language of... more
En aquesta entrevista, realitzada per Marta García Quiñones i Daniel Ranz de la revista de pensament Mania, de la Facultat de Filosofia de la Universitat de Barcelona, el teòric dels nous media ens parla del seu llibre" The Language of New Media", publicat per MIT Press, i dels projectes en els quals està treballant actualment.
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Resumé I artiklen “Avantgarde som software”, der hermed for første gang er oversat til dansk, viser den russisk-amerikanske kunstner og medieteoretiker Lev Manovich, hvordan det moderne computerinterface har rødder i 1920'ernes... more
Resumé I artiklen “Avantgarde som software”, der hermed for første gang er oversat til dansk, viser den russisk-amerikanske kunstner og medieteoretiker Lev Manovich, hvordan det moderne computerinterface har rødder i 1920'ernes avantgarde. IT-revolutionen er ifølge Manovich ikke udtryk for en nytænkning
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L'article analitza la singularitat de la revolució dels nous media i la compara amb la revolució avantguardista en el disseny, el cinema i les arts visuals que es va esdevenir als anys deu i vint. L'autor argumenta que les tècniques... more
L'article analitza la singularitat de la revolució dels nous media i la compara amb la revolució avantguardista en el disseny, el cinema i les arts visuals que es va esdevenir als anys deu i vint. L'autor argumenta que les tècniques avantguardistes dels anys vint es van transformar en convencionalismes del programari i la interfície de l'ordinador i reivindica que, en realitat, els nous media representen una nova avantguarda per a la societat de la informació, encara que facin servir formes modernes antiquades.
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In a process of cultural reconceptualization it is extremely important to identify the fundamental categories on which this historical change is based. Following the transition from industrial mass society to individualized... more
In a process of cultural reconceptualization it is extremely important to identify the fundamental categories on which this historical change is based. Following the transition from industrial mass society to individualized post-industrial society, the author contextualizes basic principles of so called new visual media. The goal is the definition of differential features making it possible for readers to better understand the language and the logic of new (digital) media in contrast with old (analogue) ones.
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Slides of my lecture.
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How are users’ experiences of production, sharing, and interaction with the media they create mediated by the interfaces of particular social media platforms? How can we use computational analysis and visualizations of the content of... more
How are users’ experiences of production, sharing, and interaction with the media they create mediated by the interfaces of particular social media platforms? How can we use computational analysis and visualizations of the content of visual social media (e.g., user photos, as opposed to upload dates, locations, tags and other metadata) to study social and cultural patterns? How can we visualize this media on multiple spatial and temporal scales? In this paper, we examine these questions through the analysis of the popular mobile photo–sharing application Instagram. First, we analyze the affordances provided by the Instagram interface and the ways this interface and the application’s tools structure users’ understanding and use of the “Instagram medium.” Next, we compare the visual signatures of 13 different global cities using 2.3 million Instagram photos from these cities. Finally, we use spatio–temporal visualizations of over 200,000 Instagram photos uploaded in Tel Aviv, Israel over three months to show how they can offer social, cultural and political insights about people’s activities in particular locations and time periods.
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Cultural Analytics (C.A.) is an approach for analyzing media and digital culture using data methods and visual computing techniques. This article explores the aesthetic value of C.A. by approaching cultural visualizations as digital... more
Cultural Analytics (C.A.) is an approach for analyzing media and digital culture using data methods and visual computing techniques. This article explores the aesthetic value of C.A. by approaching cultural visualizations as digital artworks. The authors present a variety of techniques developed since 2007 by members of the C.A. lab for creating visualizations of media artifacts and collections of images. Through a series of projects conducted by them, the authors discuss the artistic meaning of media visualizations and their experience in art exhibitions, workshops and seminars.
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Sharing photos, videos and comments on social media may seem an idle pastime, but it is not without its uses where urban design is concerned. Analysing such posts can yield helpful indicators as to how people experience the built... more
Sharing photos, videos and comments on social media may seem an idle pastime, but it is not without its uses where urban design is concerned. Analysing such posts can yield helpful indicators as to how people experience the built environment. Lev Manovich and Agustin Indaco, of the Software Studies Lab at the University of California, San Diego and the Graduate Center, City University of New York, here outline two of the Lab's recent research projects, which have involved examining extensive Instagram data from various cities around the globe.
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Humanists use historical images as sources of information about social norms, behavior, fashion, and other details of particular cultures, places and periods. Dutch Golden Era paintings, works by French Impressionists, and 20th century... more
Humanists use historical images as sources of information about social norms, behavior, fashion, and other details of particular cultures, places and periods. Dutch Golden Era paintings, works by French Impressionists, and 20th century street photography are just three examples of such images. Normally such visuals directly show objects of interests such as social scenes, city streets, or peoples dresses. But what if masses of images shared on social networks contain information about social trends even if these images do not directly represent objects of interest? This is the question we investigate in our study. In the last few years researchers have shown that aggregated characteristics of large volumes of social media are correlated with many socio-economic characteristics and can also predict a range of social trends. The examples include flu trends, success of movies, and measures of social well-being of populations. Nearly all such studies focus on text content, such as posts on Twitter and Facebook. In contrast, we focus on images. We investigate if features extracted from Tweeted images can predict a number of socio-economic characteristics. Our dataset is one million images shared on Twitter during one year in 20 different U.S. cities. We classify the content of these images using the state-of-the-art Convolutional Neural Network GoogLeNet and then select the largest category that we call "image-texts" - non-photographic images that are typically screen shots of websites or text-message conversations. We construct two features describing patterns in image-texts: aggregated sharing rate per year per city, and the sharing rate per hour over a 24-hour period aggregated over one year in each city. We find that these features are correlated with self-reported social well-being responses from Gallup surveys, and also median housing prices, incomes, and education levels. These results suggest that particular types of social media images can be used to predict social characteristics not readily detectable in images.
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From its beginnings in the 18th century until the end of the 20 th century, information visualisation used points, lines, curves and simple geometric shapes to stand in for objects and relations between them. This article discusses a new... more
From its beginnings in the 18th century until the end of the 20 th century, information visualisation used points, lines, curves and simple geometric shapes to stand in for objects and relations between them. This article discusses a new visualization method that can be called ���direct visualisation���(or ���media visualisation���): creating new visual representations from the visual media objects (images, video) or their parts. This method is particularly relevant for humanities, media studies and cultural institutions. Using the actual visual ...
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I present a number of core concepts from data science that are relevant to digital art history and the use of quantitative methods to study any cultural artifacts or processes in general. These concepts are objects, features, data,... more
I present a number of core concepts from data science that are relevant to digital art history and the use of quantitative methods to study any cultural artifacts or processes in general. These concepts are objects, features, data, feature space, and dimension reduction. These concepts enable computational exploration of both large and small visual cultural data. We can analyze relations between works on a single artist, many artists, all digitized production from a whole historical period, holdings in museum collections, collection metadata, or writings about art. The same concepts allow us to study contemporary vernacular visual media using massive social media content. (In our lab, we analyzed works by van Gogh, Mondrian, and Rothko, 6000 paintings by French Impressionists, 20,000 photographs from MoMA photography collection, one million manga pages from manga books, one million artworks of contemporary non-professional artists, and over 13 million Instagram images from 16 globa...
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for “Subjects and Styles in Instagram Photography” What are some of the types of Instagram photos today and how they relate to the 20th century photo culture? I analyze three common types of Instagram photos. We call these types... more
for “Subjects and Styles in Instagram Photography” What are some of the types of Instagram photos today and how they relate to the 20th century photo culture? I analyze three common types of Instagram photos. We call these types "casual," "professional," and "designed." "Casual" photos are similar in function to personal photographers of the 20th century: they are created for friends; they privilege content of photos and ignore the aesthetics. Both “professional” and “designed” photo type are examples of what Alise Tifentale calls competitive photography. The difference is whom the authors compete with for likes and followers. The authors of professional photos aim for “good photo” aesthetics established in the second part of the 20th century, so they compete with other authors and lovers of such “classic” aesthetics including many commercial photographers. The authors of “designed” photos associate themselves with more “contemporary,” hip,” “...
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AI plays a crucial role in the global cultural ecosystem. It recommends what we should see, listen to, read, and buy. It determines how many people will see our shared content. It helps us make aesthetic decisions when we create media. In... more
AI plays a crucial role in the global cultural ecosystem. It recommends what we should see, listen to, read, and buy. It determines how many people will see our shared content. It helps us make aesthetic decisions when we create media. In professional cultural production, AI has already been adapted to produce movie trailers, music albums, fashion items, product and web designs, architecture, etc. In this short book, Lev Manovich offers a systematic framework to help us think about cultural uses of AI today and in the future. He challenges existing ideas and gives us new concepts for understanding media, design, and aesthetics in the AI era. “[The Analytical Engine] might act upon other things besides number...supposing, for instance, that the fundamental relations of pitched sounds in the science of harmony and of musical composition were susceptible of such expression and adaptations, the engine might compose elaborate and scientific pieces of music of any degree of complexity or ...
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In this article methods developed for the purpose of what I call “Media Analytics” are contextualized, put into a historical framework and discussed in regard to their relevance for “Cultural Analytics”. Largescale analysis of media and... more
In this article methods developed for the purpose of what I call “Media Analytics” are contextualized, put into a historical framework and discussed in regard to their relevance for “Cultural Analytics”. Largescale analysis of media and interactions enable NGOs, small and big businesses, scientific research and civic media to create insight and information on various cultural phenomena. They provide quantitative analytical data about aspects of digital culture and are instrumental in designing procedural components for digital applications such as search, recommendations, and contextual advertising. A survey on key texts and propositions from 1830 on until the present sketches the development of “Data Society’s Mind”. I propose that even though Cultural Analytics research uses dozens of algorithms, behind them there is a small number of fundamental paradigms. We can think them as types of data society’s and AI society’s cognition. The three most general paradigmatic approaches are d...
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O artigo propoe uma reflexao sobre o nascimento de uma “estetica de ferramentas da informacao”. Como os dispositivos – celular, laptop, PDAs, media layer, câmara digital, jogos portateis – passaram a ser usados como objetos de consumo em... more
O artigo propoe uma reflexao sobre o nascimento de uma “estetica de ferramentas da informacao”. Como os dispositivos – celular, laptop, PDAs, media layer, câmara digital, jogos portateis – passaram a ser usados como objetos de consumo em todas as areas da vida das pessoas, sua estetica foi alterada em conformidade.
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This article presents a visualization analysis of the films The Eleventh Year (1928) and Man with a Movie Camera (1929) by the famous Russian filmmaker Dziga Vertov. It uses experimental visualization techniques that supplement familiar... more
This article presents a visualization analysis of the films The Eleventh Year (1928) and Man with a Movie Camera (1929) by the famous Russian filmmaker Dziga Vertov. It uses experimental visualization techniques that supplement familiar bar charts and line graphs often found in quantitative studies of cultural artifacts. The Vertov visualization project is a part of a larger research program to develop techniques for the exploration of massive image and video collections that the author has been directing at Software Studies Initiative (softwarestudies.com) since 2007. In this project, I explore how the “media visualization” techniques that we have developed can help us see films in new ways, adding to the already well-developed methods and tools in film and media studies. Its other goal is to make a bridge between the field of digital humanities which recently started to embrace many newer techniques of data visualization (but not for the study of visual media) and the quantitative film studies research which until now has used graphs in a more limited way.
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Did McLuhan ‘miss’ computers? In his major work, Understanding Media: The Extensions of Man (1964) the word ‘computer’ appears 21 times in the book, and a few of those references are to ‘computer age’. However, despite these references,... more
Did McLuhan ‘miss’ computers? In his major work, Understanding Media: The Extensions of Man (1964) the word ‘computer’ appears 21 times in the book, and a few of those references are to ‘computer age’. However, despite these references, his awareness of computers did not have a significant effect on his thinking. The book contains two dozens chapters each devoted to a particular medium – which for McLuhan range from writing and roads to cars and television. (The last chapter ‘Automation’ addresses the role of computers for industrial control, but not its other roles.)
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... 3 | Dez 2010 | vol 1 | PASSAGENS ... Os trabalhos de Peter Luining, Return e James Tindall evocam composições típicas criadas pelos estudantes da Bauhaus e da Vhtutemas (equivalente russa da Bauhaus nos anos vinte). ...
In the first decade of the 21st century, the researchers in the humanities and humanistic social sciences have gradually started to adopt computational and visualization tools. The majority of this work often referred as ���digital... more
In the first decade of the 21st century, the researchers in the humanities and humanistic social sciences have gradually started to adopt computational and visualization tools. The majority of this work often referred as ���digital humanities��� has focused on textual data (eg, literature, historical records, or social media) and spatial data (eg, locations of people, places, or events). 1 However, visual media have remained outside of the new computational paradigm. To fill this void, in 2007 I established the Software Studies ...
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Resumen: En esta entrevista a Lev Manovich, realizada por Marta Garcia Qui��ones [url2] y Daniel Ranz [url3] de la revista de pensamiento Mania, de la Facultad de Filosof��a de la Universidad de Barcelona, el te��rico de los nuevos media... more
Resumen: En esta entrevista a Lev Manovich, realizada por Marta Garcia Qui��ones [url2] y Daniel Ranz [url3] de la revista de pensamiento Mania, de la Facultad de Filosof��a de la Universidad de Barcelona, el te��rico de los nuevos media nos habla de su libro" The Language of New Media" publicado por MIT Press as�� como de los pr��ximos proyectos en los cuales est�� trabajando actualmente.
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The dissertation presents a history of modern ideas about vision. I believe that vision is not a timeless concept; rather, each period understands vision differently depending on how it is used. In the twentieth century, vision acquired... more
The dissertation presents a history of modern ideas about vision. I believe that vision is not a timeless concept; rather, each period understands vision differently depending on how it is used. In the twentieth century, vision acquired new roles as the medium of mass communication and the instrument of labor, and, as any other productive tool, it was subjected to engineering, rationalization and automation. Such new disciplines as applied experimental psychology and cognitive science, communication engineering and film, ...
Abstract: The article analyses the uniqueness of the new media revolution by comparing it with the avant-garde revolution of the 1910s-1920s in the visual arts, design and film. The author argues that the 1920s avant-garde techniques were... more
Abstract: The article analyses the uniqueness of the new media revolution by comparing it with the avant-garde revolution of the 1910s-1920s in the visual arts, design and film. The author argues that the 1920s avant-garde techniques were transformed into the conventions of modern human-computer interface and software, thus functioning as a foundation of post-industrial labour. He also claims that new media does, in fact, represent a new avant-garde for the information society, although it uses old modernist forms. If the 1920s avant-garde ...
Lev Manovich (17223) ... Manovich construye este texto en forma de espiral, la misma espiral a la que alude para describir “el regreso de la ... Todo tiene el mismo potencial de cambiar loslenguajes culturales existentes y todo tiene el... more
Lev Manovich (17223) ... Manovich construye este texto en forma de espiral, la misma espiral a la que alude para describir “el regreso de la ... Todo tiene el mismo potencial de cambiar loslenguajes culturales existentes y todo tiene el mismo portencial de dejar la cultura tal como ...
In this paper we theorize, visualize, and analyze the relation between physical places and their social media representations, and describe the characteristics of hyper-locality in social media. While the term “hyperlocal” has been... more
In this paper we theorize, visualize, and analyze the relation between physical places and their social media representations, and describe the characteristics of hyper-locality in social media. While the term “hyperlocal” has been recently used to describe social media that is produced in particular locations and time periods, existing research has not raised important questions about representation and experience. How is the physical place performed through social media data? How do we experience locality via social media platforms? Our work combines quantitative and qualitative analysis, and employs perspectives from the fields of Digital Humanities and Art History that have yet to be used in social media research. We offer a theory of hyper-local social media, and theorize its manifestations and operations using a particular case study. We start by historicizing the hyper-local, drawing parallels between conceptualizations of “site-specific” artworks created in the 1970s and cur...
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In this article we make a case for a systematic application of complex network science to study art market history and more general collection dynamics. We reveal social, temporal, spatial, and conceptual network dimensions, i.e. network... more
In this article we make a case for a systematic application of complex network science to study art market history and more general collection dynamics. We reveal social, temporal, spatial, and conceptual network dimensions, i.e. network node and link types, previously implicit in the Getty Provenance Index (GPI). As a pioneering art history database active since the 1980s, the GPI provides online access to source material relevant for research in the history of collecting and art markets. Based on a subset of the GPI, we characterize an aggregate of more than 267,000 sales transactions connected to roughly 22,000 actors in four countries over 20 years at daily resolution from 1801 to 1820. Striving towards a deeper understanding on multiple levels we disambiguate social dynamics of buying, brokering, and selling, while observing a general broadening of the market, where large collections are split into smaller lots. Temporally, we find annual market cycles that are shifted by count...
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What is the most important reason for using Computer Vision methods in humanities research? In this article, I argue that the use of numerical representation and data analysis methods offers a new language for describing cultural... more
What is the most important reason for using Computer Vision methods in humanities research? In this article, I argue that the use of numerical representation and data analysis methods offers a new language for describing cultural artifacts, experiences and dynamics. The human languages such as English or Russian that developed rather recently in human evolution are not good at capturing analog properties of human sensorial and cultural experiences. These limitations become particularly worrying if we want to compare thousands, millions or billions of artifacts—i.e. to study contemporary media and cultures at their new twenty-first century scale. When we instead use numerical measurements of image properties standard in Computer Vision, we can better capture details of a single artifact as well as visual differences between a number of artifacts–even if they are very small. The examples of visual dimensions that numbers can capture better then languages include color, shape, texture, contours, composition, and visual characteristics of represented faces, bodies and objects. The methods of finding structures and relationships in large numerical datasets developed in statistics and machine learning allow us to extend this analysis to very big datasets of cultural objects. Equally importantly, numerical image features used in Computer Vision also give us a new language to represent gradual and continuous temporal changes—something which natural languages are also bad at. This applies to both single artworks such as a film or a dance piece (describing movement and rhythm) and also to changes in visual characteristics in millions of artifacts over decades or centuries.
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The dissertation presents a history of modern ideas about vision. I believe that vision is not a timeless concept; rather, each period understands vision differently depending on how it is used. In the twentieth century, vision acquired... more
The dissertation presents a history of modern ideas about vision. I believe that vision is not a timeless concept; rather, each period understands vision differently depending on how it is used. In the twentieth century, vision acquired new roles as the medium of mass communication and the instrument of labor, and, as any other productive tool, it was subjected to engineering, rationalization and automation.