10 PRINT book from MIT (software studies) is out - download free PDF


Print 10 book cover

Software Studies series at The MIT Press which is co-edited by Noah Wardrip-Fruin (Computer Science, UCSB) and myself (Computer Science, CUNY Graduate Center) just published a pretty pioneering book:

10 PRINT CHR$(205.5+RND(1)); : GOTO 10 [yes, this is the actual title]


Like another just published MIT Press book Digital_Humanities, 10 PRINT is an example of a group collaboration strategy reaching yet another field - academic publishing. 10 PRINT was "coded" by 10 writers. However, rather than producing yet another academic anthology made up by independent parts, they made a coherent single "intellectual software" which executes beautifully.

The design of 10 PRINT was done Casey Reas, the co-creator of popular Processing language and a Professor in UCLA Design | Media arts department.

One of the book writers is our own Jeremy Douglass. Jeremy was a post-doctoral researcher at Software Studies Initiative from 2008 to 2012. This Fall he started his new job as Assistant Professor in English department at UCSB. Jeremy overseen all software development and its applications for big data analysis in our lab, and he now to works with the lab as its Technical Director.



The complete book PDF is freely available online for download:

http://trope-tank.mit.edu/10_PRINT_121114.pdf

If you download the PDF, I encourage to also buy the print edition, because its a very "print object" to have around.


Visit also:

MIT Press page for the book



Writers:

Nick Montfort, Patsy Baudoin, John Bell, Ian Bogost, Jeremy Douglass, Mark C. Marino, Michael Mateas, Casey Reas, Mark Sample, Noah Vawter.

Summary from Amazon:

This book takes a single line of code--the extremely concise BASIC program for the Commodore 64 inscribed in the title--and uses it as a lens through which to consider the phenomenon of creative computing and the way computer programs exist in culture. The authors of this collaboratively written book treat code not as merely functional but as a text--in the case of 10 PRINT, a text that appeared in many different printed sources--that yields a story about its making, its purpose, its assumptions, and more. They consider randomness and regularity in computing and art, the maze in culture, the popular BASIC programming language, and the highly influential Commodore 64 computer.


Sample page:

Print 10 book sample


the meaning of statistics and digital humanities


manga guide to statistics

"I still hear this persistent fear of people using computational analysis in the humanities bringing about scientism, or positivism. The specter of Cliometrics haunts us. This is completely backwards."
Trevor Owens, Discovery and Justification are Different: Notes on Science-ing the Humanities, 11/19/2012.


As the number of people using quantitative methods to study "cultural data" is gradually increasing (right now these people work in a few areas which do not interact: digital humanities, empirical film studies, computers and art history, computational social science), it is important to ask: what is statistics, and what does it mean to use statistical methods to study culture? Does using statistics immediately make you a positivist?

Here is one definition of statistics:

"A branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data" (http://www.merriam-webster.com/dictionary/statistics)

Wikipedia article drops the reference to "mathematics" and repeats the rest:

"Statistics pertains to the collection, analysis, interpretation, and presentation of data." (http://en.wikipedia.org/wiki/Outline_of_statistics).

Without the reference to mathematics, this description looks very friendly - there is nothing here which directly calls for positivism, or scientific method.


But of course this is not enough to argue that statistics and humanities are compatible projects. So let's continue. It is standard to divide statistics into two approaches: descriptive and inferential.

"Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Together with simple graphics analysis, they form the basis of virtually every quantitative analysis of data." (http://www.socialresearchmethods.net/kb/statdesc.php).

Examples of descriptive statistics are mean (the measure of central tendency) and standard deviation (the measure of dispersion).


Range_of_height_measurements_of_union_soldiers_1864
Adolphe Quetelet. A graph showing the distribution of height measurements of soldiers. This graph was a paet of many early statistical studies of Quetelet which led him to formulate a theory of "average man" (1835) which states that many measurements of human traits follow a normal curve. Source: E. B. Taylor, Quentelet on the Science of Man, Popular Science, Volume 1 (May 1872).


Application of descriptive statistics does not have to be followed by inferential statistics. The two serve different purposes. Descriptive statistics is only concerned with the data you have – it is a set of diverse techniques for summarizing the properties of this data in a compact form. In contrast, in inferential statistics, the collected data is only a tool for making statements about what is outside this sample (e.g, a population):

"With descriptive statistics you are simply describing what is or what the data shows. With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone." (http://www.socialresearchmethods.net/kb/statdesc.php).


Traditionally, descriptive statistics usually summarized the data with numbers. Since the 1970, computers gradually made the use of graphs for studying the data (as opposed to only illustrating the findings) equally important. This was pioneered by John Tukey who came up with the term exploratory data analysis. "Exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics in easy-to-understand form, often with visual graphs, without using a statistical model or having formulated a hypothesis." (http://en.wikipedia.org/wiki/Exploratory_data_analysis). Tukey's work lead to the development of statistical and graphing software S, which in its turn lead to R, which today is the most widelly used computing platform for data exploration and analysis.

Accordingly, the current explanation of descriptive statistics on Wikipedia includes both numbers and graphs: "Descriptive statistics provides simple summaries about the sample and about the observations that have been made. Such summaries may be either quantitative, i.e. summary statistics, or visual, i.e. simple-to-understand graphs." (http://en.wikipedia.org/wiki/Descriptive_statistics).

What we get from this is that we don't have to do statistics with numbers - visualizations are equally valid. This should make the people who are nervous about digital humanities feel more relaxed. (Of course, visualizations bring their own fears - after all, humanities always avoided diagrams, let alone graphs, and the fact that visualization is now allowed to enter humanities is already quite amazing. (For example, current Cambridge University Press auhor guide still says that illustrations can only be used if it’s really necessary, because they distract readers from following the arguments in the text.)

Galton_graph_1886
Francis Galton’s first correlation diagram, showing the relation between head circumference and height, 1886. Source: Michael Friendly and Daniel Danis, The Early Origins and Development of the Scatterplot. The article suggests that this diagram was an intermediate form between a table of numbers and a true graph.


However, we are not finished yet. Besides numbers and graphs, we can also summarize the data using parts of this data. In this scenario, there is no translation of one media type into another type (for example, text translated into numbers or into graphs). Although such summaries are not (or not yet) understood as belonging to statistics, they perfectly fit the definition of descriptive statistics.

For example, we can summarize a text with numbers such as the average sentence length, the proportion between nouns and verbs, and so on. We can also use graphs: for example, a bar chart that shows frequency of all words used in the text in ascending order. But we can also use some words from the text as its summary.

One example is the popular word cloud. It summarizes a text by showing us most frequently used words that are scaled in size according to how often they are used. It carries exactly the same information, as a graph which plots their frequencies - but if the latter foregrounds the graphic representation of the pattern, the former foregrounds the words themselves.

Another example is a phrase net technique available on manyeyes (http://www-958.ibm.com/software/data/cognos/manyeyes/page/Phrase_Net.html). It is a graph that shows most frequent pairs of words in a text. Here is a prase net which shows first 20 most frequent word pairs in Jane Austen's Pride and Prejudice (1813):


Austin

Interactive version of this graph which allows you to change parameters.


In the previous examples, the algoritms extracted some words or phrases from a text and organized them visually - therefore it possible to argue that they still belong to graph method of descriptive statistics. But consider now the technique of topic modeling which recently has been getting lots of attention in digital humanities (http://en.wikipedia.org/wiki/Topic_model; http://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/). The topic model algorithm outputs a number of sets of semantically related words. Each set of words is assumed to represent one theme in the text. Here are examples of three such sets from the topic model of the articles in journal Critical Inquiry (source: Jonathan Goodwin, Two Critical Inquiry Topic Models, 11/14/2012).

meaning theory interpretation question philosophy language point claim philosophical sense truth fact argument knowledge intention metaphor text account speech

history historical narrative discourse account contemporary terms status context social ways relation discussion essay sense form representation specific position

public war time national city american education work social economic space people urban culture corporate building united market business


In my lab, we have been developing free software tools for summarizing large image and video collections. In some of our applications, the whole collection is translated into a single high-resolution visualization (so these are not summaries) but in others, a sample of an image set, or parts of the images are used as the summaries. For example, here is the visual summary of 4535 covers of Time magazine which uses one one pizel wide horizontal column from from each cover. It shows the evolution of the covers design from 1923 to 2008 (left to right), compressing 4535 covers into a single image:

YZ_Time_covers


(For a detailed analysis of this and other related techniques for what I call exploratory media analysis, and their difference from information visualization, see my article What is Visualization?)


We can also think of other examples of summarizing collections / artifacts in different media by using parts of these collections / artifacts. On many web sites, video is summarized by a series of keyframes. (In computer science, there is a whole field called video summarization devoted to the development of algorithms to represent a video by using selected frames, or other parts of a video.)

To summarize a complex image we can translate it into a monochrome version that only shows the key shapes. Such images have been commonly used in many human cultures.

picasso_selfport1907
Picasso. Self Portrait. 1907. A summary of the face which uses outlines of the key parts.

Some symbols can also act as a summaries. For example, modernity and industrialization were often summarized by images of planes, cars, gear, workers, and so on. Today in TV commercials, a network society is typically summarized by a visualization showing a globe with animated curve connecting many points.

mechanization_takes_command
Example of an object becoming a symbol. The gears stand in for industrialization. The cover of Siegfried Gedeon's Mechanization Takes Command (1947).


There is nothing "positivist" or "scientific" about such same-media summaries, because they are what humanities and the arts have always been about. Art images always summarized visible or imaginary reality by representing only some essential details (like contours) and omitting others. (Compare to the goal of descriptive statistics to come up with "the basic features of the data.") A novel may summarize everything that happened over twenty years in the life of characters by only showing us a few of the events. And every review of a feature film includes a short text summary of its narrative.

Until development of statistics in the 19th century, all kinds of summaries were produced manually. Statistics "industrializes" this process, substituting subjective summarization by the objective and standardized measures. While at first these were only summaries of numerical data, the development of computational linguistics, digital image processing, and GIS in the 20th century also automates production of summaries of media such as texts, images, and maps.

Given that production of summaries is the key characteristics of human culture, I think that such traditional summaries created manually should not be opposed to more recent algorithmically produced summaries such as a word cloud or a topic model, or the graphs and numerical summaries of descriptive statistics, or binary (i.e., only black and wite without any gray tones) summaries of photographs created with image processing (in Photoshop, use Image > Adjustments > Threshold). Instead, all of them can be situated on a single continuous dimension.

On the one end, we have the summaries that use same media as the original "data." They also use the same the same structure (e.g., a long narration in the film or a novel is summarized by a condensed narrative presented in a few sentences in a review; a visible scene is represented by the outlines of the objects). We can also put here metonymy, (the key rhetorical figure), and Pierce's icon (from his 1867 semiotic triad icons/index/symbol).

On the other end, we have summaries which can use numbers and/or graphs and which present information which is impossible to see immediately by simply reading / viewing / listening / interacting with the cultural text or a set of texts. Examples of such summaries are a number representing the average number of words per sentence in a text which has tens of thousands of sentences, or the graph showing relative frequencies of all the words appearing in a long text.

I don't think that we can find some hard definite threshold which will separate summaries which can only be produced by algorithms (because of the data size) from the ones produced by humans. In the 1970s, without the use of any computers, French film theorists advanced the idea that most classical Hollywood films follow a single narrative formula. This is just one example of countless "summaries" produced in all areas of humanities (whether they are accurate is a separate question).

I don't know if my arguments will help us when we are criticized by people who keep insisting on a wrong chain of substitutions: digital humanities=statistics=science=bad. But if we keep explaining that statistics is not only about inferences and numbers, gradually we will be misunderstood less often.


(Note: in the last fifteen years, new mathematical methods for analyzing data which overlap with statistics become widely used – referred by "umbrella" terms uch as “data mining," "data science" and “machine learning.” They have very different goals than either descriptive or inferential statistics. I will address their assumptions, their use in industry, and how in digital humanities we may use them differently in a future article).


Digital_Humanities book is published - download free open edition



Digital_Humanities - book page on The MIT Press web site


Authors: Peter Lunenfeld / Anne Burdick / Johanna Drucker / Todd Presner / Jeffrey Schnapp.

Publication: The MIT Press, November 2012.


Sample pages from the Open Acess edition:

digital_humanities_pages


There are a number of unique things about this book. First, it was written by five leading practitioner-theorists in digital media and digital humanities.

Another unique thing is that each chapter was written collaboratively. I don't know of any other academic book written by leaders in their respective fields that was created in this way.

Unlike most academic books, Digital_Humanities has sophisticated graphic design. This is also the key message of the book - that the design is essential to digital humanities. This much needed perspective was so far missing from the field.

And finally, you can download the pdf of the book for free.


Quotes from the book:

"Digital_Humanities is not a standard-format academic work. It is not a collection of individually authored scholarly papers or research reports on the history of, or critical engagement with, the Digital Humanities. Neither is it a textbook from which to teach the discipline’s foundations nor a manual of technical specifications, much less a discussion of every facet of the field, its protagonists, successes and failures, and defining moments. In lieu of a bibliography, it includes a “reference network” and list of works cited."

"The model we have created is experimental. It moves design—information
design, graphics, typography, formal and rhetorical patterning—to the center of
the research questions that it poses."


The book description from The MIT Press web site:

Digital_Humanities is a compact, game-changing report on the state of contemporary knowledge production. Answering the question, “What is digital humanities?,” it provides an in-depth examination of an emerging field. This collaboratively authored and visually compelling volume explores methodologies and techniques unfamiliar to traditional modes of humanistic inquiry--including geospatial analysis, data mining, corpus linguistics, visualization, and simulation--to show their relevance for contemporary culture.

Included are chapters on the basics, on emerging methods and genres, and on the social life of the digital humanities, along with “case studies,” “provocations,” and “advisories.” These persuasively crafted interventions offer a descriptive toolkit for anyone involved in the design, production, oversight, and review of digital projects. The authors argue that the digital humanities offers a revitalization of the liberal arts tradition in the electronically inflected, design-driven, multimedia language of the twenty-first century.

Written by five leading practitioner-theorists whose varied backgrounds embody the intellectual and creative diversity of the field, Digital_Humanities is a vision statement for the future, an invitation to engage, and a critical tool for understanding the shape of new scholarship.

Peter Kubelka's Arnulf Rainer: the film as a visualization


kubelka
Peter Kubelka with a wall Iinstallation of Arnulf Rainer (1960).



Arnulf Rainer is a famous 1960 film by Austrian experimental filmmaker Peter Kubelka. The film consists only from black and white frames, organized in a strict system. Kubelka also presented the film as a wall installation, with film frames arranged in a sequential order (see the photo above).

------------

Normally when we visualize a film or a video, we have to go through a process of reduction: scaling frames to a small size (for instance, the visualizations by William Huber shows 22,500 frames sampled from a 62.5 hr videoof Kingdom Hearts gameplay) or using geometric forms such as bars to represent some visual properties of the shots (for instance, see my visualization sketches showing patterns in shot length in two films by Dziga Vertov).

Eleven_RowReels_pointsize_shotlength
A visualization of shot lengths in The Eleventh Year (Dziga Vertov, 1928). The length of a shot is represented by the size of a corresponding circle. Each row represents one film reel.

However, since Arnulf Rainer only contains black or white frames, it becomes its own visualization as soon as we unfold these frames in a sequence, as can be seen in the photo of Kuleblka's installation.


Image Processing and Software Epistemology


Turning everything into data, and using algorithms to process is analyze it has a number of major consequences for what it means to know something. It creates new strategies which together make up software epistemology. Epistemology is a branch of philosophy which asks questions such as what is knowledge, how it can acquired, and to what extent a subject can be known. Digital code, algorithms, machine learning, big data, social media, faster processors and larger storage, and other parts of modern techno-social universe introduce new ways of acquiring knowledge, in the process redefining what it means to know something.

if you have a dataset, its always possible to invent new algorithms (or new ways to scale existing algorithms to analyze big data faster) which can analyze the information in ways the previous algorithms could not. You can extract additional patterns and also generate new information from the existing data.

Algorithms and software applications which analyze images and video provide particularly striking examples of the ability to generate additional information from the data years or even decades after it was recorded.

In Blowup, a 1966 film directed by Michelangelo Antonioni, the photographer who takes pictures of a couple kissing in the park uses the strategy of printing progressive enlargements of one area of the photograph, until a grainy close-up reveals a body in the grass and a man with a gun hiding in the trees.

Blow-Up_print
Blowup: Photographer examining the photo he took in the park.

Around the same time as this film was used, computer science researchers were already developing the new field of digital image processing, including the algorithms for image enhancement such as sharpening of edges, increasing contrast, noise reduction, and blur reduction. The early articles in the field show the blurry photographs taken by surveillance planes which were sharpened by the algorithms. Today many of these techniques are build into every image manipulation software such as Photoshop, as well as in the lens and firmware of digital cameras. They become essential to both consumer photography, and commercial visual media, as every published photograph first goes through some software manipulation.

example-digital-image-processing-original
examples-digital-image-processing-result
Typical example of digital image processing taken from a textbook. Top: the original image captured by the camera mounted on a plane. Bottom: the image after it was automatically processed by software.

blow_up_frame1
The still frame from the Blowup.

blow_up_frame.Adjusted_Photoshop
The same frame adjusted in Photoshop.


Contemporary digital cameras - both dSLR and high-end compacts - can record images both in JPEG and RAW formats. In the former, an image is compressed, which limits the possibilities for later extraction of additional information using software. Raw format contains the unprocessed data from the camera's image sensor. Raw format assumes that a photographer will later manipulate the photo in software to get best results from the millions of pixels recorded by camera. "Working with a Raw file, you’ll have a greater chance of recovering highlights in overexposed clouds, rescuing detail from areas of shadow or darkness, and correcting white balance problems. You’ll be able to minimize your images' noise and maximize their sharpness, with much greater finesse." (William Porter, Raw vs. JPEG: Which should you shoot and when? techhive.com, 9/19/2012.)

This means that new software can generate new information from a photo in Raw format captured years early. The following example from Porter's article demonstrates this dramatically (go to the article link to see high resolution photos; the captions are from the article):

image-camera-jpeg
Porter: "JPEG straight from the camera, a high-end compact (Panasonic DMC LX3). It’s a nominally decent exposure, preserving highlight detail at the expense of detail in the shadows. But it’s an unusable photo."

image-saved-as-row-manipulated-adobe-lightroom
Porter: "Fortunately, I was shooting Raw + JPEG at that moment. This is the best version I got from processing the Raw file myself in Adobe Lightroom 3."

image-saved-as-row-manipulated-Image-Processor
Porter: "A year after I took the photo, I discovered the amazing, Mac-only Raw Photo Processor. Running the Raw file through RPP, I was able to recover detail that even Lightroom’s converter hadn’t found."


"Software epistemology" also includes ability to generate new information from old analog data. For example, years ago I saw a demo at SIGGRAPH where a few film shots from the 1940s Hollywood film were manipulated in software to generate the same shots as seen from a different point of view.

Another important type of software epistemology is fuzing data sources to create the new knowledge which is not explicitly contained in any of them. Using the web, it is possible to create a description of an individual by combining piece of information from his/her various social media profiles and making deductions form them.

Combining separate sources can give additional meanings to each of the sources. Consider the technique of stitching a number of separate photos into a single panorama available in many digital cameras. Strictly speaking, the underlying algorithms do not add any new information to each of the images (their pixels are not changed). But since each image can now becomes a part of the larger whole, its meanings for a human observer change.


The abilities to generate new information from the old data, fuse separate information sources together, and create new knowledge from analog sources are just some techniques of "software epistemology." In my future publications and notes I am hoping to gradually describe others - as I am mastering the appropriate algorithms and their applications.


Software Studies Initiative is expanding: New York + San Diego


CUNY Graduate Center, Fifth Avenue
CUNY Graduate Center buiding, 361 Fifth Ave, New York City. Build in 1865.

Calit2 Atkinson Hall Engineering Courtyard
Calit2 building, La Jolla, CA. Built in 2005.


View Larger Map
Graduate Center location in NYC.


As some people already know, I am moving to CUNY Graduate Center in January 2013. I will be a Professor in graduate computer science program. I am looking forward to working with faculty and students in CUNY program on developing further cultural analytics methods, tools, and projects.

CUNY Graduate Center is making serious investments in building digital humanities, including grants, events, research programs, workshops, etc. My first course to be offered in Winter 2012 is Big Data, Visualization, and Digital Humanities, and its open to both students in MA program in digital humanities, and PhD students in all departments.

Here are the links to some of the many digital initiatives and events at CUNY Graduate Center:

JustPublics@365 - reimagining scholarly communication for the public good

Craduate Center Digital Initiatives

Theorizing the Web 2013 conference

Digital media programs at CUNY


We will also continue collaborate with California Institute for Telecommunication and Information (Calit2) and their labs and scientists developing next generation visualization systems. Software studies inittiative will have offices both in NYC at CUNY, and in San Diego at Calit2.

if you are interested to study with me at CUNY, I have funding to support two new PhD students for 5 years (125K per student). To be considered for Fall 2013 admission, send me one page proposal by Dec 1, 2012.


Press release:

RENOWNED DIGITAL HUMANITIES EXPERT LEV MANOVICH JOINING GRADUATE CENTER FACULTY





new book: Coding Places Software Practice in a South American City


Coding Places: Software Practice in a South American City

By Yuri Takhteyev

The MIT Press, 2012






Software development would seem to be a quintessential example of today’s Internet-enabled “knowledge work”--a global profession not bound by the constraints of geography. In Coding Places, Yuri Takhteyev looks at the work of software developers who inhabit two contexts: a geographical area--in this case, greater Rio de Janeiro--and a “world of practice,” a global system of activities linked by shared meanings and joint practice. The work of the Brazilian developers, Takhteyev discovers, reveals a paradox of the world of software: it is both diffuse and sharply centralized. The world of software revolves around a handful of places--in particular, the San Francisco Bay area--that exercise substantial control over both the material and cultural elements of software production. Takhteyev shows how in this context Brazilian software developers work to find their place in the world of software and to bring its benefits to their city.

Takhteyev’s study closely examines Lua, an open source programming language developed in Rio but used in such internationally popular products as World of Warcraft and Angry Birds. He shows that Lua had to be separated from its local origins on the periphery in order to achieve success abroad. The developers, Portuguese speakers, used English in much of their work on Lua. By bringing to light the work that peripheral practitioners must do to give software its seeming universality, Takhteyev offers a revealing perspective on the not-so-flat world of globalization.

About the Author:
Yuri Takhteyev is Assistant Professor at the University of Toronto in the Faculty of Information and the Institute of Communication, Culture, and Information Technology at the University of Toronto. Takhteyev, who grew up in the Soviet Union, worked in Silicon Valley before he went to Brazil to study the software industry there.

Submit proposals to Theorizing the Web 2013 Conference at CUNY Graduate Center


Theorizing the Web 2013 (#TtW13) Conference

Deadline for Abstracts: Sunday, January 6th

Society has been infiltrated by new digital technologies with potentially profound consequences. It makes sense to ask what’s changed? How has it changed? How much? Researchers and companies have gathered enormous amounts of data to ostensibly answer these questions, but the full implications of this data too often go unexplored. The Web is not a new, separate sphere, but part of the same social reality about which critical social theorist have produced several centuries worth of insight. These theories may be profitably used, tweaked, or even abandoned in light of contemporary realities. What previous theoretical tools help us understand these changes? What new theories should be created?

Now in its third year, the Theorizing the Web conference seeks to bring together an inter/non-disciplinary group of scholars, journalists, artists, and commentators to theorize the Web. As in the past, we encourage critical discussions of power, social inequality, and social justice.

The keynote speaker for this year is scholar David Lyon, author of many books on surveillance technologies and society (most recently Liquid Surveillance with Zygmunt Bauman), who will discuss the significance and nuances of surveillance in the social and digital media environment. How well do pre-existing theories of social observation, such as panopticism, map onto new realities such as Facebook? Do we need new conceptual tools?

Distinguished Visiting Fellows at CUNY in Digital Humanities



Distinguished Visiting Fellows at CUNY:

For scholars and researchers who are not employed by the City University of New York.

A Distinguished Visiting Fellow will receive a stipend of up to $70,000 for the full academic year or up to $35,000 for a single semester.

Research areas:

Digital Humanities
Social and Economic Inequality
Globalization
Immigration
Theoretical Sciences
Religion
Science Studies
American Studies


Unfortunately, I only noticed this today, and the deadline was October 31. However, if you contact CUNY, the may still consider. Short-term fellowshops may also be possible.

Two digital fellows positions at The JustPublics@365 at CUNY Graduate Center


The JustPublics@365 initiative seeks two Digital Fellows with technological and pedagogical skills to work on events, seminars, and conferences with Graduate Center faculty, students, and staff who work on issues of social injustice. We are especially interested in candidates whose research examines social inequality and have demonstrated excellence in one-on-one and small group teaching. The initiative aims to have a significant impact of the digital and social environment within the GC and beyond it, and we seek candidates who also are interested in this mission.

The total compensation for the award is $22,000. This funding includes a GC graduate assistant appointment that will carry eligibility to purchase low-cost NYSHIP health insurance (as well as in-state tuition remission for fellows who are within their first 10 registered semesters of study). JustPublics@365 Digital Fellows will be required to work a total of 450 non-teaching hours during the academic year. The appointment is from January 1st, 2013, through January 1st, 2014.

Applications must be received by November 30th, 2012 to be considered. A review of applications will begin immediately.

Image now: it is not cinema, or animation, or visual effects

(The following text is adapted from my next book Software Takes Command, forthcoming from Bloomsbury Academic in 2013)


Psyop - panosonic anthem 2012 - montage 2x2
Panosonic anthem commercial by Psyop, 2012.


TV commercials, television and film titles, and many feature films produced since 2000 feature a highly stylized visual aesthetics supported by animation and compositing software. Many layers of live footage, 3D and 2D animated elements, particle effects, and other media elements are blended to create a seamless whole. This result has the crucial codes of realism (perspective foreshortening, atmospheric perspective, correct combination of lights and shadows), but at the same time it enhances visible reality. (I can’t call this aesthetics “hyperreal” since the hybrid images assembled from many layers and types of media look quite different from the works of the hyperrealist artists such as Denis Peterson that visually look like standard color photographs.) Strong gray scale contrast, high color saturation, tiny waves of particles emulating from moving objects, extreme close-ups of textured surfaces (water drops, food products, human skin, finishes of consumer electronics devices, etc.), the contrasts between the natural uneven textured surfaces and smooth 3D renderings and 2D gradients, the rapidly changing composition and camera position and direction, and other devices heighten our perception. (For good examples of all these strategies, you can, for example, look at the commercials made by Psyop.)

We can say that they create a “map” which is bigger than the territory being mapped, because they show you more details and texture spatially, and at the same time compress time, moving through information more rapidly. We can also make another comparison with the Earth observation satellites which circle the planet, capturing its whole surface in detail impossible for any human observer to see – just as a human being can’t simultaneously see the extreme close-up of the surfaces and details of the movements of objects presented in the fictional space of a commercial.

None of the 20th century terms we inherited to talk about moving images describes this aesthetics, or production processes involved in creating it. It is not cinema, animation, special effects, invisible effects, visual effects, or even motion graphics. And yet, it characterizes the image today, and calls for its theoretical analysis and appreciation.

It is not cinema because live action is only a part of the sequence, and also because this live action is overlaid with 2D and/or 3D elements, and additional imagery layers. It is not animation or motion graphics because live action is central, as opposed to being just one element in a sequence. It is not special effects, because every frame in a sequence is an "effect" (as opposed to only selected shots). It is not invisible effects because the artifice and manipulations are made visible. It is not visual effects defined as "various processes by which imagery is created and/or manipulated outside the context of a live action shoot" - because here live action is manipulated.

Rather than seeing this new aesthetics, and production processes involved in its creation, as an extension of special effects / visual effects model, I think that it is more appropriate to see as an extension of the job of cinematographer of cinematography. 20th century cinematographer was responsible for selecting and choreographing all material elements which together produced the shots seen by the audiences: film stock, camera, lenses, lens filters, depth of field, focus, lighting, camera movements. Today a shot is likely to include many other elements and processes - image processing, compositing, 2D and 3D animation and models, relighting, particle systems, camera tracking, matte creation, effects, etc. (See for example the lists of features in Autodesk Flame software). However, at the end result is similar to what we had in the 20th century: a 3D scene that includes (real or constructed in 3D) bodies and objects. While now it also incorporates all kinds of digital transformations, and layers, it is still defined by three-dimensionality, perspective and MO movement of objects, just as the first films by The Lumiere brothers.

Psyop - Fage Plain commercial - 2011 - montage
Fage Plain commercial by Psyop, 2011.


ImagePlot 1.1 visualization software: explore patterns in large image collections


Today we’re excited to announce the release of ImagePlot 1.1 - a free software tool that visualizes collections of images and video. This updated version was developed by Jay Chow and Matias Giachino. The development was supported by Mellon Foundation grant "Tools for the Analysis and Visualization of Large Image and Video Collections for the Humanities."


Download ImagePlot 1.1

ImagePlot creates new types of visualizations not offered by any other application. It displays your data and images as a 2D line graph or a scatter plot, with the images superimposed over data points. The images can be sorted using any available metadata (e.g, dates), or by their visual properties which can be automatically measured with the included software. ImagePlot works on Mac, Windows, and Linux. Max visualization size: up to 2.5 GB (for example, you can render a 23,000 x 23,000 pixels visualization.) There no limits on how many images can be shown in a single visualization. (Largest image collections we visualized so far: 1 million manga pages; 1 million deviantArt artworks.)

What's New in Version 1.1
  • Each new visualization is given automatically generated meaningful unique filename. It includes the names of data file and the data columns used for x-axis and y-axis.
  • Option to automatically save the visualization after it have been rendered (appears in the first application dialog box).
  • Option to render the visualization using a better resize algorithm (runs slower but generates nicer images; the option appears in the Image Options dialog box).
  • File open error checking: if ImagePlot can't find a particular image, the filename is printed in the Log window, but rendering continues.


90000_DigitalArt.90000_TraditionalArt.scaled
Comparing 90,000 images from Digital Art category on deviantArt (left) and 90,000 images from Traditional Art category (right). In each image plot, images are sorted by average brightness (x) and average saturation (y). Software which analyzes these and other image properties is included with ImagePlot.

crop.Matrix_users.X_BrMean.Y_SatMean.Color_cat1_cat2.hue_shift..top_225
ImagePlot code can be easily extended and customized. This visualization compares image galleries from a number of deviantArt users. The colors of the circles indicate the primary and secondary categories of each image (e.g., Traditional Art / Portraits). The individual plots are combined into a matrix using ImageJ built-in command.

ImagePlot can create animated visualizations. Here we animated 128 paintings by Piet Mondrian from 1904-1917 over time. The year is indicated in the upper left corner. The distances between paintings indicate their visual similarity.


new article by Lev Manovich: "Media After Software"


Lev Manovich. "Media After Software." Forthcoming in Journal of Visual Culture, volume 12, number 1, April 2013.


DOWNLOAD:

Manovich.Media After Software.2012.pdf (1.7 MB).



Dieter Rams.Braun_record_player.1956.color.cor
Dieter Rams. Braun record player, 1956. Rams can be called the first interface designer. His designs of media recording and access devices – cameras, record players, magnetic tape players, etc. – created the aesthetics of interface, and defined a new type of consumer object dominated by controls.


Screen Shot 2012-11-08 at 9.29.40 AM
"For users who interact with media content through application software, the 'properties' of digital media are defined by the particular software as opposed to solely being contained in the actual content (i.e., inside digital files). For example, depending if you open an image file in the default media viewer of your computing device, or in Photoshop, its 'properties change dramatically."

250K for two students to do their PhD degrees with Lev Manovich at CUNY


To be considered, email your 1 page proposal by December 1, 2012.

Global News group reviewing Obama montages



I am looking for two exceptional individuals who want do their PhDs with me at CUNY Graduate Center beginning in Fall 2013 (or Fall 2014).

To be considered, email your proposal by Dec 1, 2012.
(See "process" section below.)
If I select you, you then need to prepare your formal application to CUNY. Remember that you need to have recent GRE (and TOEFL if you are not a native English speaker).

Funding:
Each student will receive a 25k/year stipend for 5 years (125K in total); tuition fees and medical insurance are also covered. This funding is available for both US and foreign students. The students supported by this package will need to perform some service every year (RA, TA, or teaching one class a semester).

Research areas:
cultural analytics, social computing, digital humanities.

Research goals:
See cultural analytics page at softwarestudies.com

Programs:
One funding package is for a student who will do a PhD in computer science program (my home program at CUNY). The second package is for a student who can enter any other graduate program at CUNY Graduate Center.

CUNY applications information:
Computer science
Other programs

CUNY Deadlines
CUNY application deadlines vary by program. (Computer Science deadline is Jan 15, 2013).

My addmission criteria:
A student who will be admitted to computer science program should have adequate background in this field, and be passionate about using computation to study contemporary cultures and societies using large visual data sets. The possible research areas include computer vision, data mining, and visualization.

A student who will study in a humanities or social science program should have some technical background, and an interest to combine cultural and social theory with computational methods for the analysis of large visual data.

Other requirements: professional publications and/or projects, very good writing skills.

Master degree (in any field) is recommended.

Visual communication skills (including graphic and web design and visualization) are a plus.

Process:
Interested individuals should email me using this address: manovich [dot] lev [at] gmail [dot] com. Please put the phrase "CUNY PhD" in the header. Attach your current CV. The body of the email should containe the following (maximum one page in length):

- Desciption of your interests in cultural analytics / social computing / digital humanities;
- examples of two research projects you want to do with me at CUNY.
- the links to your web site / blog / projects / publications.

The applications that do not follow this format will not be considered.

After I select the semi-finalists, I will contact them to invite them to participate in a small competition (you will have to analyze, visualize, and interpret a sample data set).

The two finalists selected on the basis of this competition and Skype interviews will need to formally apply to the appropriate programs at CUNY Graduate Center. They need to meet the requirements and admission deadlines of these programs.

If I don't find appropriate students for 2013, the support packages will be still available, and the screening process will be repeated for 2014 admission.

Interested students who are already enrolled in PhD programs at CUNY:
you are also welcome to apply.


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