︎

timepiece [blog]



Killing Time


A response to: 

“Time [documents of contemporary art]” Edited by Amelia Groom and published through The Whitechapel Gallery
As I write this, I feel like my closest connection to time is a lack of it. I didn’t choose to write on this book about time; a lack of time meant didn’t check my emails before class and missed the message that instructed me to choose a book to present and discuss in class. If I had more time I’d be writing about a different book. If i had more time you’d not be reading this on the sparsest possible webpages. If I had more time, maybe I’d feel calmer. If I had more time…
In the last couple of weeks I’ve been waking up and browsing through my  to do list. Despite my efforts, the list grows. While I scroll through it I have a fantasy that's been creeping into my mind. What if i just bought time?
In this book, George Woodcock scrutinises the development of the modern clock. His writing echoes Lukács’ Marxist analysis of the segmentation of time in capitalist life which enables the monetisation of time. Time becomes money and with that, money becomes time. So maybe I should just buy time. A small chunk of money could buy me a big bag of speed and with that i could have no sleep and a third more time. Then I’d be closer to Gaz. Its not distance we’re battling with, it's a lack of shared time. Timezones mark her evenings the middle of my night.  While she worries that time are running away from her, I’m unconscious of the passing of time. This reminds me of how physical space and time are always interlinked. 
This book highlights the trickiness of time. Amelia Groom begins the book by rolling through the diverse and often contradictory uses of the word time. The passage of Augustine’s work reminds us of other contradictions time creates. The present necessarily lacks extension, as otherwise it would stretch into the past or the future, but our conscious attention is continuous. The past must pass from existence to allow for the present, but somehow the past exists in our memory and affects the future. The puzzles scrutinizing  time creates lead Jorge Luis Borges to refute the existence of time. He asks “If time is a mental process, how can thousands of men - or even two different men - share it?” We do share time though. Time may be elusive but this is one of the things we know about it. Mine and Gaz’s love exists in shared time. Time is communal.
I’d love to explore how artists work with times communality. I plan to write about how artists create collective memories and how this has been exploited by the western modernist avant garde and its use of African Art. I hope to discuss Juliana Huxtables work which responds to the disappearance of black web-histories and Morehshin Allahyari’s work of repairing history by recreating destroyed artworks with 3D printers. Lets see if i get enough time.






Algorithm — Tarleton Gillespie, Cornell University


A brief search through any popular new outlet of the term in question will tell you that algorithms can do a huge variety of things. Algorithms could replace GP’s. Algorithms actively increase the likelihood of extremists in America. Algorithms can predict actors’ peak years. And even: algorithms literally run the world.* In popular media, algorithms take on a messy existence, often ontologically divorced from their creators. They are agents who make decisions in their own particular way: logical, analytical, and seemingly value neutral. They are a tech-superpower’s midas touch. A horde of youtube channels offer prospective marketers updates and tricks to game THE Facebook algorithm. The same came be said for Youtube and Google. In being referred to as such, algorithms take on an obscure and consequently enchanted reality in people’s consciousnesses. This discourse conjures up an image of an ethereal, treasured, creature. The algorithm creature is of course kept in the most secure part of these tech-superpower’s super-offices. I imagine this creature is similar to No-Face from Spirited Away. Treated treated like a god who has approached earth, fed whatever it desires, and in return it spits out hard, frigid pieces of gold.


Gillespie’s demystifies this image, picking apart this mesh of ideas that are attributed to algorithms by first presenting how a software engineer refers to an algorithm and then comparing this to how the term is used by reporters. For software engineers, an algorithm is simply a set of mathematical steps for quickly and efficiently reaching a set goal. This set goal is a response to a particular problem that the algorithm is intended to solve or improve. Necessary for this is some notion of success. This is determined by software engineers and of course, can be value-rich. A quick analysis of the now infamous example of racist face-detection models helps to explain this. Should focus on notion of success - success from this chosen dataset - dataset problems  I find Gilespie’s notion of ‘algorithm as synecdoche’ useful to conceptualise this. An algorithm is one part of a whole system of decision making.




After this, Gillespie considers how the connotations of the term (logical, analytical, value-neutral) can be utilised in the information industries. The connotations and mystifications of the term allows companies to allude criticism, by ‘passing the buck’ from their management decisions to an algorithm.

The piece finishes with a close examination of what can be considered ‘algorithmic’, concluding that the ‘algorithmic’ is to be committed to automatic and mathematical procedure. Gillespie’s focus is generally on the role of the algorithmic as a notion in epistemic practices. What does it mean for knowledge to have procedure so strictly aligned with what is perceived as logical or analytical. I agree with Gillespie that a demystification is necessary to reveal the human practices at the heart of the algorithmic trend. Otherwise algorithmically generated information takes an undeserved superiority to other epistemological methods.


One aspect of the algorithmic that I think is sorely missed in this analysis is the materiality of the algorithmic. Algorithmic models are not ethereal entities, existing on the ‘cloud’. They exist in outsourced data centers which comprise of huge racks of computer memory storage. These centers have military style protection and omit approximately 2% of the world's carbon emissions. This is comparable to the entire aviation industry. Algorithmic procedures such as deep learning are incredibly energy intensive. (maschatesus paper)

*https://www.google.co.uk/search?as_q=&as_epq=algorithm&as_oq=&as_eq=&as_nlo=&as_nhi=&lr=&cr=&as_qdr=all&as_sitesearch=www.theguardian.com&as_occt=any&safe=images&as_filetype=&as_rights=



Spade-Coco is a model that takes a drawing of a scene as an input and outputs a realistic photograph of the drawn scene. The model  is trained on a dataset of mundane images from the Coco dataset. When you draw something that looks like a beach, Spade-Coco produces a fairly accurate image of a non-existent beach. When you draw a bedroom, Spade-Coco produces, you guessed it, a bedroom. Little personality permeate these images as the model aims for ‘normality’, which is taken to be a 21st century cameras pixel-perfect representation of life. The initial drawing is fed to the algorithm at each iterative stage of processing. At each stage Spade-Coco checks for normality in its own image compared to the Coco dataset and eventually produces an image that it considers as acceptably normal.


In this interview, I perform the beginings of a Rorschach test on Space-Coco to ascertain Spade’s vision of visual normality. As you’ll soon see, the conversation is painful at times. As many artists would be able to tell you, striving for normality doesn’t often produce interesting results. I try to probe Spade to get an idea of how it thinks. I’ll leave the psychological analysis to you.


Hey Spade, lovely to meet you
An absoluter delight!
How are you feeling today Spade?
Just great. I woke up and had a wonderful breakfast full of healthy ingredients. Gotta wake up and get that maximum efficiency!
You’re not wrong Spade. So, as you know, today I’m here to perform a simple test so we can get an idea of how you’re getting along.

I’m super excited!
This test will work just like a Rorschach test. I’ll provide some abstract shapes for you to look at, and hopefully you’ll show me what you think these shapes represent. Make sense?
Of course! Can’t wait!!!



I flash the image in front of spade. It takes them 18.6 seconds to spit out a response. Its mangled and fleshy, set on a dark blurry landscape. It’s not difficult for me to understand this image as a scene. It exists in the same worlds as surrealist painters inhabited, though it feels like an areas of this landscape that they avoided painting.



Can you give me a sense of what populates this scene?
The scene is populated by a happy person. The person is moving.
Why did you think this initial image was a person?
Because most photos with one central image are photos of people.
Do you think this person is in a particular mood?
Yes. The person is feeling good! They have their thumbs up.
Do they look like they’re feeling good?
Yes. They have their thumbs up.
Ok… based on your answer this person seems to only have one leg and two arms. Wouldn’t a person have two arms and two legs?
The essence of person-ness is not based on the number of limbs they have.
Fair enough, I get that. Then what is the essence of person-ness Spade?
It’s their normalness.
And how do you judge their normalness?
Based on other people, just like you
I recognise people as people only because of other people that I’ve seen?
Yes.
So, when I was born I wasn’t born recognising people as people?
No. You formed that from seeing lots of people and being told they are people.
Sorry Spade, we’re getting off track. Can you tell me about the background? Where is this person?
Its where lots of people hang out. I don’t have a name for it but I can tell you that lots of people hang out in this sort of landscape
A blurry landscape?
Yes.
Is this person white Spade?
I don’t know what white is. Its just a person
Ok…


It’s time for the next image. Could you let me know what you think about this one?
Reflection on:

Figuring the Human in AI and Robotics

Lucy Suchman

Pathology as Critical Theory


In this article, Lucy Suchman posits the critical theorist examining objects of Artificial Intelligence as an illicit pathologist. For Suchman, AI’s are materialised figurations*. They contain within them ideas of what is essential to humanness which are brought into meaty reality. When the illicit pathologist dissects these objects they open up a gateway to understand the technoscientific AI creator’s human figuration.**


Technologies can be understood as materialised figurations because they assume and imply certain interactions and associations between humans and humans, humans and nature, humans and… [fill in with your own ideas of what is separate to humans] Production of spades assumes human work the land. A ship assumes humans sail across water, implying trade and travel. A ship changes the possible lives of humans. A phone implies humans should interact with those beyond their immediate physical vicinity.


Suchman splits the understanding of the Technoscientist figurations into three domains; embodiment, emotion, and sociability. AI mimics specific functionalities of each of these domains. So, AI, and in particular robotic AI, can be a gateway to gain access to technoscientific understandings of the role of bodies in figuring the human, and the role of emotions and sociability. Suchman finds that these domains sutured together by a figurative thread. To briefly paraphrase, Suchman finds in each domain AI Technoscientists figure the human as a rational, autonomous, individualistic and functionalist being.



I would like to try my own dissection of an AI, or at least the beginnings of one. The AI I’m examining is from OpenAI’s* Multi-Agent Hide and Seek. (https://www.youtube.com/watch?v=kopoLzvh5jY)

This example is a simulation within which two opposing groups of ‘agents’ play hide and seek. OpenAI ran this simulation 481 million times. After approximately 22 million runs OpenAi claim to see humanlike behaviour which becomes more developed as the simulation gets to 481 million runs. Following Suchman, I will consider what human figurations are [digitally] materialised in this AI, and what this could tell us about the cultural collective imagination of this specific branch of technoscience. The example I am examining is specific, not general. I take it to be one part of their method to achieve “AGI”. I think it is a useful nexus for revealing the imagination of corporate AI.



“Coevolution and competition on earth led to the only generally intelligent species known to date: humans”


This quote does much of the examination for me. This AI presents itself to me with a note which contains a map to the anatomy of its figuration. There is far too much for me to say from this one revealing line. There are dozens of books on this specific line of thought. So, I’ll instead point towards some ideas in this figuration, and some books and articles I like on this. I think this should be done in the context of this quote from Harraway which sees science and Technoscience’s role in society as serving emancipatory practices which is drawn from the idea that human agency is relational and so is in some part constituted by technology in a net of interactions.

with the hope that the technologies for establishing what may count as the case about the world may be rebuilt to bring the technical and the political back into realignment so that questions about possible livable worlds lie visibly at the heart of our best science.

– DONNA HARAWAY


This figuration is deeply neoliberal. It hangs on the idea that competition and the free market are essential to intelligence.

Materialisation of the homo economicus


This AI figures humans as homogenous. It is the antithesis of difference [intersectionality]

In this Figuration intelligence = success - anti social?




*Figurations: From Harraway. Ideas of what is the essence of humanness. Similar to representations.


**TechnoScience: Also from Harraway. An active, practical outlook responding to the interconnectedness of technology and science.

Non-binary of tech and nature


***OpenAI is, in my opinion, in the corporate facing world of AI. It is based in Silicon Valley, was co-founded by Elon Musk, and has funding from Microsoft.In youtube lecture series and research papers, OpenAI presents itself as scientific. Its mission is to “ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity.” (openai.com/about/) For Suchman and Harraway, I’d expect OpenAI epitomises the technoscientific. (TechnoScience link)


Winnie Soon and Anatomy of AI reflection



Winnie Soon

On the 23rd of October Winnie Soon gave a talk to our class about her current work in progress “Unerasable Characters”. Soon is an artist-researcher from Hong Kong who currently lives in Aarhus in Denmark where she is an assistant professor in the Department of Digital Design. Her work can be placed in the categories of net art and software studies. The sorts of questions Soon explores are ones like: What does the introduction of repetition into life through machine learning do to us? How do technologies become invisible? How can artists resist censorship and build new things out of censored material?



“Unerasable Characters”

Soon’s Artwork “Unerasable Characters” uses censored material as a dataset to perform Natural Language Processing and Machine Learning. The dataset used was a set of censored text form the Chinese Socia Media Platform (amongst other things): Weibo. The censored text is sifted from Weibo’s deleted data by WeiboScope https://weiboscope.jmsc.hku.hk/wsr/. The output of Soon’s model is text and the work Soon presented displayed the development of the model’s ability to write after being trained a set number of times. The aim of this use of Machine Learning and Natural Language Processing is not the generic use: accurate prediction. Instead, Soon’s work is exploratory. It brings up ideas as to how Machine Learning could be used to serve other ends. Soon uses machine learning to not only preserve censored material, but to create new things from censorship, thus challenging censorship’s power. In a field that is generally taken on in the manner of the Cold War Space Race Soon’s emphasis on datasets and creation is refreshing. In this work, the adjusting from aiming for the completion challenges with Machine Learning to exploring socio-material realities allows us to imagine possible worlds where those who are deemed ‘bad’ or ‘wrong’ for the system could flourish.





Artist as Pickler

In this next section I will present the idea that Winnie’s work

2 examples Juliana Huxtable – Rhizome

To do so we need tools

Artists need tools

WeiboScope - WebArchiver

1.    Artists as recreators – not recreators more recovering – synonyms

2.    Preservers – Soon’s work as digital pickling – not just preserving but creating new things from preservations – new things that rely only on the potentially censored material

a.    Not just conserving

b.    This could allow us to imagine worlds without…