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 jill/txt i'm jill walker rettberg, professor of digital culture at the university of bergen. blogging here since october 2000. latest book free download or buy in print other publications browse my other publications or talks and presentations on social media, electronic literature, electronic art. lesson plan: dikult103 29.01.2019 – video game aesthetics and orientalism in games readings : understanding video games chapter 5, woke gaming chapter 6 (kristin bezio: the perpetual crusade: rise of the tomb raider, religious extremism, and the problem of empire. (p 119-138)) learning goals: after doing the reading, taking the quiz and attending the class, students can explain how video game aesthetics incorporate game mechanics as well as visuals, sounds, etc. use some of the terms in understanding video games chapter 5 to describe games explain said’s concept of orientalism and discuss it in relation to video games continue reading → 18. february 2019 by jill categories: uncategorized | leave a comment hostile machine vision one of our goals in machine vision is to analyse how machine vision is represented in art, stories, games and popular culture. a really common trope is showing machine vision as hostile and as dangerous to humans. machine vision is used as an effective visual metaphor for something alien that threatens us. my eight-year-old and i watched ralph breaks the internet last weekend. i found it surprisingly satisfying – i had been expecting something inane like that emoji movie, but the story was quite engaging, with an excellent exploration of the bad effects of neediness in friendships. but my research brain switched on in the computer virus scene , towards the end of the movie, because we see “through the eyes of the virus”. here is a shot of the virus, depicted as a dark swooshing creature with a single red eye: and here you see the camera switch to what the virus sees. it is an “insecurity virus”, that scans for “insecurities” (such as vanellope’s anxious glitching and ralph’s fear of losing vanellope) and replicates them. and of course it uses easily-recognisable visual cues that signify “machine vision” – a computer is seeing here. i noticed an almost-identical use of this visual metaphor at another visit to the cinema with the kids, though this time in an ad from the australian cancer council. here, the sun is presented as seeing human skin like an alien. the way humans see skin is not the same way the sun sees skin. and each time the sun sees your skin, when the uv is 3 or above, it’s doing damage beneath the surface. it builds up, until one day, it causes a mutation in your dna, which can turn to skin cancer. don’t let the sun see your dna. defend yourself. the visuals are different. while ralph breaks the internet uses an overlay of data, the ad shifts from a “human” camera angle to zooming in, black and white, fading around the sides of the image, a shaky camera, and then appears to penetrate the skin to show what we assume is the dna mutating. the sound effects also suggest something dangerous, perhaps mechanic. certainly machine vision isn’t always represented as hostile. it’s often presented as useful, or protective, or simply as a tool. this year we are going to be tracking different representations and simulations of machine vision in order to sort through the different ways our culture sees machine vision. hostile is definitely one of those ways. if you have suggestions for other examples we should look at, please leave a comment and tell us about them! 11. february 2019 by jill categories: machine vision | 2 comments seeing brainwaves last week i was in london, where i visited pierre huyghe’s exhibition uumwelt at the serpentine gallery . you walk in, and there are flies in the air, flies and a large screen showing images flickering past, fast. the images are generated by a neural network and are reconstructions of images humans have looked at, but that the neural network hasn’t had direct access to – they are generated based on brainwave activity in the human subjects. the images flicker past in bursts, fast fast fast fast fast slow fast fast fast, again and again, never resting. jason farago describes the rhythm as the machine’s “endless frantic attempts to render human thoughts into visual form”, and frantic describes it well, but it’s a nonhuman frantic, a mechanical frantic that doesn’t seem harried. it’s systematic, mechanical, but never resting, never quite sure of itself but trying again and again. i think (though i’m not sure) that this is an artefact of the fmri scanning or the processing of the neural network that huyghe has chosen to retain, rather than something huyghe has introduced. huyghe uses technology from yukiyasu kamitani’s lab at kyoto university . a gif kamitani posted to twitter gives a glimpse into how the system uses existing photographs as starting points for figuring out what the fmri data might mean – the images that flicker by on the right hand side sometimes have background features like grass or a horizon line that is not present in the left image (the image shown to the human). here is a youtube version of the gif he tweeted: the images and even the flickering rhythms of the kamitani lab video are really quite close to huyghe’s uumwelt. at the exhibition i thought perhaps the artist had added a lot to the images, used filters or altered colours or something, but i think he actually just left the images pretty much as the neural network generated them. here’s a short video from one of the other large screens in uumwelt – there were several rooms in the exhibition, each with a large screen and flies. sections of paint on the walls of the gallery were sanded down to show layers of old paint, leaving large patterns that at first glance looked like mould. the neural network kamitani’s lab uses has a training set of images (photographs of owls and tigers and beaches and so on) which have been viewed by humans who were hooked up to fmri, so the system knows the patterns of brain activity that are associated with each of the training images. then a human is shown a new image that the system doesn’t already know, and the system tries to figure out what that image looks like by combining features of the images it knows produce similar brain activity. or to be more precise, “the reconstruction algorithm starts from a random image and iteratively optimize the pixel values so that the dnn [dnn=deep neural network] features of the input image become similar to those decoded from brain activity across multiple dnn layers” ( shen et.al. 2017 ) looking at the lab’s video and at uumwelt, i suspect the neural network has seen a lot of photos of puppy dogs. i’ve read a few of the kamitani lab’s papers, and as far as i’ve seen, they don’t really discuss how they conceive of vision in their research. i mean, what exactly does the brain activity correspond to? yes, when we look at an image, our brain reacts in ways that deep neural networks can use as data to reconstruct an image that has some similarities with the image we looked at. but when we look at an image, is our brain really reacting to the pixels? or are we instead imagining a puppy dog or an owl or whatever? i would imagine that if i look at an image of somebody i love my brain activity will be rather different than if i look at an image of somebody i hate. how would kamitani’s team deal with that? is that data even visual? kamitani’s lab also tried just asking people to imagine an image they had previously been shown. to help them remember the image, they were “asked to relate words and visual images so that they can remember visual images from word cues” (shen et.al. 2017). as you can see below, it’s pretty hard to tell the difference between a subject’s remembered swan or aeroplane and their remembered swan or aeroplane. i wonder if they were really remembering the image at all, or just thinking of the concept or thing itself. figure 4 in shen

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