IN CLASS
Viewing of ALGORITHMIC VISION exercises.
Discussion of assigned articles around machine vision.
EXERCISE: DECORRELATED VISION
Video & Sound, 2 minutes
This exercise will require at least 2 cameras (phones), with no limit to the amount of cameras used.
1. An action will be filmed by multiple cameras running simultaneously.
2. These images will be overlaid (one on top of the other) and synchronized. Use opacity controls within your software to overlay these tracks.
3, These cameras should not be static, but moving.
4. You will need some form of synching mechanism, such as a conventional “slate” or loud sound, captured by both cameras so you can ensure synch between the tracks.
EX: You are performing an action with a camera strapped to your body AND you are being filmed performing this action by another (or several) other camera(s).
EX: You are filming an action you are not performing with multiple cameras.
RESEARCH ON MACHINE VISION
Research the machine vision-related link you have been assigned: come prepared to talk about its context, implications, modes of operation etc.
______________________________
MACHINE VISION & PANOPTICS
1. MACHINE VISION
Alien / Nonhuman Perspectives
How does a machine see?
How do machines complicate human vision? (We have always been cyborgs!)
“The point here is that if we want to understand the invisible world of machine-machine visual culture, we need to unlearn how to see like humans.” (Paglen)
“We no longer look at images–images look at us. They no longer simply represent things, but actively intervene in everyday life. ” (Trevor Paglen—Invisible Images: Your Pictures Are Looking At You)
Dziga VERTOV—Man with a Movie Camera (1929)
film can GO ANYWHERE
PANOPTIC – anticipates ubiquitous surveillance
KINOEYE – KINOK (already a cyborgian idea)
separation from theatre and literature / no dialogue / no actors / no “storyline”
fast pacing (for the time)! 1800 shots! (Critique: “The producer, Dziga Vertov, does not take into consideration the fact that the human eye fixes for a certain space of time that which holds the attention.”)
Michael SNOW—La Région Centrale (1971) and the original arm (DE LA)
Eva KOCH - Evergreen (2006)
Eric CAZDYN—The Blindspot Variations: Reconfigured Participation (2015)
the fields of view of each camera don’t neatly stitch together / gaps appear in which things still happen
WEBCAM
Dariusz KOWALSKI—Optical Vacuum (2008) - excerpt here
Joana MOLL—AZ: Move and Get Shot (2016)
ANIMAL CAMS
MACHINE TIME
The hidden activity and geography of real-time peer-to-peer file sharing via BitTorrent is revealed in The Pirate Cinema an online piece by Nicolas Maigret. In this monitoring room, omnipresent telecommunications surveillance gains a global face, as the program plunders the core of restless activity online, revealing how visual media is consumed and disseminated across the globe. This live work produces an arbitrary mash-up of the BitTorrent files being exchanged in real time, based on the traffic of the Pirate Bay’s top100 videos. These fragmentary contents in transit are monitored, transforming BitTorrent network users (unknown to them) into contributors to an endless audio-visual composition.
BULLET TIME
TEMPORAL TO SPATIAL (following the underlying logic ot the digital, of the database)
stills converted into movie frames: the filmmakers are able, as Alexander Galloway puts it, “to freeze and rotate a scene within the stream of time,” and to view the scene, at each moment, from any desired angle
BUT, still inscribed into a linear, temporal narrative
SPLITTING THE ATOM (Massive Attack, dir. Edouard Salier)
like a computer game in some way - though the music temporalizes it
doesn’t employ montage (moving the camera and fixing the world – the entire space is given in advance)
Instead of time as “inner sense,” we now have an exterior time, one entirely separate from the time-that-fails-to-pass within the video’s rendered space.
VR (enhanced experience: cyborg)
Harun Farocki—Serious Games III: Immersion (2009) excerpt here and here and here
The Philosophy of VR (David Chalmers)
How Filmmakers Push Your Eyes Around the Screen At Will
modeling of human behavior in tandem with modeling AI behavior (both evolving simultaneously)
eyes transition from exploratory mode (overall) to information-extraction mode (details filled in)
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“focal points”
“motion onsets” capture attention
ongoing activity parsed into discrete events (beginning of new shot initiates new exploratory phase)
COST CUTTING!!!: perceptual technics (like MP3 – saving bandwidth / money on regions that perception discards)
SURVEILLANCE—EXPANDED PANOPTICS
Harun Farocki—I Thought I Was Seeing Convicts (2000)
Informatic Opacity (Zach Blas)
PRISM: making visible / informatic visibilities
PRISM transparently mediates light – TRANSPARENCY
construction of models to evaluate against = HUMAN ALL TOO HUMAN
human fully knowable (quantified self) – identity reduced to aggregates of quantifiable data
minoritarian persons not measurable (dark skin undetectable)
OPACITY as resistance – TOR network – as mutated QUEERNESS – subverting identification standardization
These are withdraws from power through collective stylings but also occupations of zones that lie outside the perceptual registers of control. Informatic opacity, then, is not about simply being unseen, disappearing, or invisible, but rather about creating autonomous visibilities, which are trainings in difference and transformation.
DARPA Sponsors Surveillance Technology to Predict Future Behavior (2012)
“an artificial intelligence system that can watch and predict what a person will ‘likely’ do in the future using specially programmed software designed to analyze various real-time video surveillance feeds. The system can automatically identify and notify officials if it recognized that an action is not permitted, detecting what is described as anomalous behaviors.”
“VIDEO SUMMARIZING TECHNOLOGIES”
DARPA is building a drone to provide ‘persistent’ surveillance virtually anywhere in the world
CORRELATION of surveillance: photos on Facebook that never go away – and the better AI’s get at trawling and collating data, the more of your past becomes subject to examination, the more your own history is rewritten
LAWSUIT asking for Google to remove news stories: Google Spain SL, Google Inc. v Agencia Española de Protección de Datos, Mario Costeja González (2014), a decision by the Court of Justice of the European Union (CJEU).
your past never stops haunting you
1. individualization / differentiation based on a wealth of factors
2. reification of those categories, removing ambiguity so these metadata profiles can be operationalized
amplifies diversity of METADATA signatures in order to capitalize on smaller and smaller domains of everyday life
GENERATIVE VIDEO: MACHINE LEARNING
generalizations + prediction
this happens all the time with the images your phone takes (comparison with past photos — the past does determine the future!!!) (See Hito Steyerl—Proxy Politics)
the world is reproduced through computational models – including bias, prejudice
continues to reflect bias (Try Googling “three white teenagers” and ”three black teenagers”)
(underrepresentation in datasets of certain populations)
A beauty contest was judged by AI and the robots didn’t like dark skin
GOOGLE: Algorithms trained on white people interpret black people as GORILLAS
FACEBOOK ELIMINATES HUMAN EDITORS (since rescinded)
hard for the average individual to intervene in
everyday Youtube navigation is training YouTube algorithms
Fei-Fei Li—How We’re Training Computers to Understand Pictures (see errors at 14:30)
INTERESTING.JPG (a project by the University of Toronto’s Deep Learning Group)
Blade Runner Neural Net Reconstruction AND more technical details
machine encoded video mistaken for the real thing by another algorithm
two stages: compression (encoding) - decompression (reconstruction): teaching an artificial neural net to do this without human parameter decision
ADVERSARIAL NETWORK (noise / signal adjustments through feedback cycles)
seeing the film THROUGH the neural network
using the BLADE RUNNER model to reconstruct other films!
Machine Predictive Video : Deep Learning Program Hallucinates Videos and Generating Videos with Scene Dynamics
takes advantage of huge, unprecedented databases of online videos
GENERATOR network <—> DISCRIMINATOR network (stand-in for a human viewer)
Hito Steyerl—A Sea of Data: Apophenia and Pattern (Mis-)Recognition
Hito Steyerl—Proxy Politics: Signal and Noise
“computational photography”—interfaced with all kinds of systems / disabling / altering ECOLOGY
speculative and relational
makes seeing unforeseen things more difficult (novelty?)
who decides on signal vs. noise? (noise = what one doesn’t want to hear —> leads to vertical class hierarchies)
rendering cognition, behavior quantifiable and commensurable to a system of exchange value based in data
TWITTER BOTS—your picture becomes autonomous (detached from its body, context)
ENHANCEMENT, CSI STYLE: GOOGLE Super Resolution Zoom Enhance
Fake Images are Getting Harder and Harder to Detect
manipulated images distort the historical record
FORENSIC TESTS These tests range from analyzing the position and shape of people’s irises in photographs to whether or not the sources of light in a photograph are consistent for the entire image.
but these detection tests can also be used constructively!!!
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