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1. From the two readings that you read for today, note sentences or short passages that strike you as particularly interesting, quotable, and relevant to matters of AI, pattern recognition & surveilance that we wrestled with in our class discussion.

2. When you find such a sentence, copy or type it into the Padlet, and add the quote's page from the course reader.

3. Find an image that illustrates your chosen passage and add it to your post.

4. Once you've selected your quote and image, see and read over what your colleagues have contributed so far.

5. After you've read all the quotations, choose five to comment on.

Don't forget to indicate your name!

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“A Sea of Data: Apophenia and Pattern (Mis-)Recognition”: “Yet all the seemingly swift and invisible action of algorithms, their elegant optimization of everything, their recognition of patterns and anomalies—this is based on the endless and utterly senseless labor of providing or fixing messy data"

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"By 'recognizing' things and patterns that were not given, inceptionist neural networks eventually end up effectively identifying a new totality of aesthetic and social relations. Presets and stereotypes are applied, regardless ofwhether they “apply” or not: “The results are intriguing—even a relatively simple neural network can be used to over-interpret an image, just like as children we enjoyed watching clouds and interpreting the random shapes.”But inceptionism is not just a digital hallucination. It is a document of an era that trains smartphones to identify kittens, thus hardwiring truly terrifying jargons of cutesy into the means of production. It demonstrates a version of corporate animism in which commodities are not only fetishes but morph into franchised chimeras." page 209 The image I've attached is a "rainbow cat" ai generated image I just made on Dall-E Mini, alongside an illustration by artist Louis Wain. I am struck by the patterns created by machine versus human, and curious whether people think one is more obviously a recognizable pattern.

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1. From the first text: "Let’s come back to Amani al-Nasasra, the woman blinded by an aerial attack in Gaza. We know: the abstract images recorded as intercepts of IDF drones by British spies do not show the aerial strike in Gaza that blinded her in 2012. The dates don’t match. There is no evidence in Snowden’s archive. There are no images of this attack, at least as far as I know of. All we know is what she told Human Rights Watch. This is what she said: “I can’t see—ever since the bombing, I can only see shadows.” p213

From second text: "For instance, we are prompted to consider what it might mean when Deepfakes and others rely on existing figurations of masculine desire in the absence of a criticality that establishes links between itself, sex, the enduring violences against the feminine body, and issues of purity and deviancy." p218

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Neural networks were trained to discern edges, shapes, and a number of objects and animals and then applied to pure noise. They ended up “recognizing” a rainbow-colored mess of disembodied fractal eyes, mostly without lids, incessantly surveilling their audience in a strident display of conscious pattern overidentification. (pg 208)

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"Deepfakes insisted that their mission was one of goodwill: the technology was already out in the world, and their experiment helped highlight the fact that these technologies can be used by people with bad motivations(p.218)"

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The patterns were projections not reality 

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The patterns were projections not reality 

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“The patterns were projections, not reality. While today statisticians and other experts routinely acknowledge that their findings are mostly probabilistic projections, policymakers of all sorts conveniently ignore this message. In practice you become coextensive with the data-constellation you project. Social scores of all different kinds—credit scores, academic scores, threat scores—as well as commercial and military pattern-of-life observations impact the real lives of people, both reformatting and radicalizing social hierarchies by ranking, filtering, and classifying.”  Steyerl, A Sea of Data: Apophenia and Pattern (Mis-recognition), p. 211

Lisa

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By “recognizing” things and patterns that were not given, inceptionist neural networks eventually end up effectively identifying a new totality of aesthetic and social relations. Presets and stereotypes are applied, regardless of whether they “apply” or not"

pg 209

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1- First Reading:

We train an artificial neural network by showing it millions of training examples and gradually adjusting the network parameters until it gives the classifications we want. The network typically consists of 10–30 stacked layers of artificial neurons. Each image is fed into the input layer, which then talks to the next layer, until eventually the “output” layer is reached. The network’s “answer” comes from this final output layer. (p. 208)

Second Reading:

"Although photorealism and human image synthesis technology push the boundaries of lifelike representation, Deepfakes’s porn swaps seem to exceed a motivation to showcase the sophistication of the technology.” (p. 218)

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It’s not about the data or even access to the data. It’s about getting information from the truckloads of data. (205)

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"Apophenia is defined as the perception of patterns within random data. Apophenia is defined as the perception of patterns within random data. The most common examples are people seeing faces in clouds or on the moon. Apophenia is about “drawing connections and conclusions from sources with no direct connection other than their indissoluble perceptual simultaneity,” as Benjamin Bratton recently argued" (p. 205)

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Past predictions of ‘a jobless future’ have

been falsified: AI will endlessly repeat its ‘spring/winter’ cycles of high

hopes and disappointing actualities. Whatever technological realities

underlie AI-capital’s promises and threats are not sufficient to alter the

sober realities of capital’s continued large-scale dependence on exploited

human labour. There are therefore no immediate major implications of

AI for socialist or communist struggles. (pg 187 INHUMAN POWER)

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"AI is arguably the ultimate test for the reconfiguration debate, because general AI would be the ultimate technological system. The question of ‘refunctioning’ AI is bigger than that of reconfiguring logistics systems, which have in some ways become a subset of AI, and at least as large as the problem of energy system design, with which AI is, as we will see, closely bound up." (pg 190)

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Quote 1: “Dirty data is where all of our refusals to fill out the constant onslaught of online forms accumulate. Everyone is lying all the time, whenever possible, or at least cutting corners.

Not surprisingly, the “dirtiest” area of data collection is consistently pointed out to be the health sector, especially in the US. Doctors and nurses are singled out for filling out forms incorrectly. It seems that health professionals are just as unenthusiastic about filling out forms for systems designed to replace them, as consumers are about performing clerical work for corporations that will spam them in return” (206).

Quote 2: “All sorts of agricultural and mining technologies—that were developed during the neolithic—are reinvented to apply to data. The stones and ores of the past are replaced

by silicone and rare earth minerals, while a Minecraft paradigm of extraction describes the processing of minerals into elements of information architecture” (210).

Emily

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"Seeing is superseded by calculating probabilities. Vision loses importance and is replaced by filtering, decrypting, and pattern recognition. Snowden’s image of noise could stand in for a more general human inability to perceive technical signals unless they are processed and translated accordingly. But noise is not nothing. On the contrary, noise is a huge issue, not only for the NSA but for machinic modes of perception as a whole." page 204

-Hito Steyerl, A Sea of Data.

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the supposed easing of domestic labor provided by Alexa is in fact equivalent to the colonisation of domestic sphere by algorithmic capital and the intensification of labor exploitation in Amazon's ecosystem around the world. (p.37)

2- Alexa can help with tasks like setting reminders, playing music, and controlling smart home devices, which seems convenient.

3- Similar technologies are increasingly becoming a part of our daily lives and homes, influencing how we live and interact within our private spaces.

4-Amazon warehouse workers and delivery drivers face intense pressure and strict monitoring, which can lead to exploitation.

5-last but not least giving access to technology will raise questions like how secure our privacy would be.

jamal

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DALL·E 2024-06-20 16.15.34 - A modern living room with an Amazon Alexa device on a table. The room subtly shows a sense of tension and technological invasion. In the background, a

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"Today, expressions of life as reflected in data trails became farmable, harvestable, miracle resource managed by informational biopolitics"

(Hito Steyerl, A sea of data, p210)