machine learning
TPG
2021-06
We live in a world full of images made by machine for machines, from facial recognition technologies to automatic license plate readers and AI image categorisation. What’s more, these new ‘ways of …
TPG
2021-05
A Screen Walks playlist of recorded live-streamed events touching on machine learning.
TPG
2020-09
HUMAN COMPUTERS is a project that investigates the relationships between computing and labor organisation. The project is a media archaeology research that starts with the simulacra of a computing …
I proposed what would become Lacework in the Summer of 2019. In my proposal, I describe a cycle of videos curated from MIT's 'Moments In Time' dataset, each then slowed down, interpolated, and upscaled immensely into imagined detail, one flowing into another like a river...
TPG
2020-07
an ongoing project to aggregate tools and resources for artists, engineers, curators & researchers interested in incorporating machine learning (ML) and other forms of artificial intelligence …
I write this from my small New York apartment in my fourth month of isolation. The pandemic has required each of us to slow down and do less, and I keep thinking of a childhood friend who once told me, “We’re human beings, not human doings”. Even as a teenager, I knew this was an important paradigm shift: it meant that we could rethink how we …
TPG
2020-05
Transmediale 2020 End to End Exchange #5 Panel discussion Neural Network Cultures with Tega Brain, Stephanie Dick, Katharine Jarmul, Fabian Offert and Matteo Pasquinelli
Given that a person’s gender cannot be inferred by appearance, we have decided to remove these labels in order to align with the Artificial Intelligence Principles at Google, specifically …
I met with Kate Crawford and Trevor Paglen on the press preview of their exhibition Training Humans in Milan at Osservatorio Prada. It was the morning of September 11th –not a neutral day to unthink photography and the power operations of vast populations of images. On the contrary, it was the most apt one to seriously consider Crawford and …
TPG
2019-10
Ramon Amaro introduces the basics of machine learning, its criteria for assigning value, the collision between blackness and the artificial, its flaws, and the problem of impunity that all too often …
What do you see, YOLO9000? by Taller Estampa | Soy Cámara YOLO9000 is a trained object recognition neuronal network with a dataset of 9,418 words and millions of images. It is one of the many …
The success of ImageNet highlighted that in the era of deep learning, data was at least as important as algorithms. Not only did the ImageNet dataset enable that very important 2012 demonstration of …
While humans pay attention to the shapes of pictured objects, deep learning computer vision algorithms routinely latch on to the objects’ textures instead Image: Robert Geirhos …
This labelling job has made me very observant. I have found pictures that made me think “if I had taken such a picture, then I would know what is everything.” For instance, in a picture of a …
Fortunately we are smart people and have found a way out of this predicament. Instead of relying on algorithms, which we can be accused of manipulating for our benefit, we have turned to machine …
TPG
2019-05
Snapchat's new gender-bending filter is a source of endless fun and laughs at parties. The results are very pleasing to look at. As someone ...
Layers of Abstraction: A Pixel at the Heart of Identity Shinji Toya and Murad Khan, 2019 This project centres around a critical examination of the limits of categorisation in machine learning …
"Images now published on social media are valorised in terms of distribution and quantifiable interactions, particularly when triangulated with data about a user’s online purchases or social media behaviour. This process shapes visual representations of human identities into ‘data images’ outside the control of the person the data originates from. …
For the past six years Heather Dewey-Hagborg has been researching, writing and producing artwork engaging the methodology of ‘forensic DNA phenotyping’. In this essay, she explores a different aspect of this technology and questions: is forensic DNA phenotyping a photographic process?
Quotations around automation, image making and labour. Collated by Adam Brown and Nicolas Malevé for Rethinking the workshop: Workers Education in the Age of Intelligent Machines at The …
We are under the illusion that seeing is effortless, but fre- quently the visual system is lazy and makes us believe that we understand something when in fact we don’t. Labeling a picture forces us …
TPG
2017-07
prostheticknowledge: Computed Curation Project by Philipp Schmitt creates a book of photography curated and annotated using Machine Learning: Computed Curation is a photobook created by a computer. …
TPG
2017-07
“The world’s first photo exhibition shot with a car.” Barbara Davidson in collaboration with Volvo
TPG
2017-04
prostheticknowledge: Realtime Neuratorial Art Artist Memo Atken has been exploring methods to generate neural network images in realtime. It started with his #Learningtosee project, with a ‘blank’ …
TPG
2017-04
Matthew Plummer-Fernandez - snowden.ppt, 2017 Machine Learning style transfer used to create portraits of Snowden in the styles of the leaked NSA powerpoint slides. The leaked presentations also …
Terrorists often use masks, scarfs, and hoods to hide their identities. But a new approach aims to distinguish them using the shape of their fingers when they make the “V for victory” sign.
HITO STEYERL. This reminds me of the late 19th century, where there were a lot of scientific efforts being invested into deciphering hysteria, or so-called “women’s mental diseases.” And there were …
MIT Media Lab’s Camera Culture Group focuses on making the invisible visible–inside our bodies, around us, and beyond–for health, work, and connection. The goal is to create an entirely new class of …
we can ask a second neural net to determine whether the output of a first looks real or fake. This technique is called adversarial learning. It’s often compared to the relationship between someone …
2016-11
“The relation between what we see and what we know is never settled.”
The new system, called Dreambit, analyzes the input photo and searches for a subset of photographs available online that match it for shape, pose, and expression, automatically synthesizing them …
TPG
2016-11
prostheticknowledge: AI Experiments Yesterday, Google released a load of creative coding experiments using artificial intelligence and neural networks to demonstrate how the technology can be …
#deepdream is appealing because it gives us access to machine pareidolia, an area of great artistic interest before Google got involved – see Henry Cooke‘s experiments with faces-in-the-cloud and …
TPG
2016-10
Decision Space by Sebastian Schmieg Decision Space by Berlin-based artist @sebastianschmieg takes a closer look at how machine vision datasets are created: developed on the website of The …
Scientists at Google and elsewhere are turning to the 30-year-old digital music standard MIDI to teach neural networks how to write music.
2016-09
Our shares and likes, our annotations and social metadata are training a generation of AI agents. Everyday, we are already all teaching bots and algorithms how to look at images. If we consider the extent of our relationship with algorithms, we realise the magnitude of the effort of teaching and learning that is taking place.
It’s another troubling example of an emerging trend.