Left: c. mae bloom, Firelights flickering on the ceiling of the world, 2025.
Middle: Foresaken-Werewolf-23, r/mildinginteresting/, Reddit, Peice of plaster on my wall looks like someone painted over a moth or butterfly, 7mo ago
Right: Vince Moriarty, Positive Quotes Diaries, Facebook, Does the inside of a cheese grater qualify as a liminal space? 9 January 2024
I watch the recording of the residency induction, and I am struck by how generous the space is – a time to push back against the ‘should’ of productivity. It makes me think about my current work that I have been exploring during my DYCP which focuses on the opposite – on the ridicularity of healthcare and wellness processes based in efficiency and optimisation.
I also watch the recording of first meeting and then select these images of recent work-in-progress: a re-purposing of health and wellness mental and visualisation models and methods through drawing and embroidery. This sample was made with the help of Poppy Nash in April this year. It had a 77 minute stitch time. I feel it speaks to the meeting’s discussion on re-purposing, language and translation.
Image Description: Monochrome pen drawings with thin black lines on a white background. They look like abstracted versions of sinews, tendons, and neurons, twisting and curving. They follow a similar pattern of emerging from the bottom left corner of the page, winding around and up diagonally until the top right of the page.
I can’t be there for the first residency meeting. I talk to Jamie about how sad I am about this: that I can’t even meet the requirements of the residency from day one. We say of course that is ok, and I think about my studio text which says that I am embracing failure during this residency, but I still find it difficult to reconcile with the normative structures I have internalised.
This situation makes me think about my drawing ‘(Un)Productive Spatialisations’ which I made in 2022 to highlight the work to get to make work. I am not in the same place as I was when I made this diagram, which tracks the granular choices a ‘sick’ bodymind makes to get to make work; to be in public, but it resonates with me now in a different way as I work with my sister to care for my mum. All the hidden labour that goes into living is in my mind and how this affects bodyminds in different ways – whether it’s the work of the ‘sick’ body, or the work to make work, or the work to care for someone else’s needs – all of which disproportionately affect those bodyminds whose needs aren’t seamlessly met by the environment around them.
I am grateful that Jamie says it’s fine that I can’t make it, and I viscerally relearn Alison Kafer’s critical aspect of crip time: that it’s more than fine to meet the day, the residency, the work as you are, rather than bend your body and mind to meet the clock, in fact, it’s fundamental.
I’ve included the audio description of the diagram by Char Heather as this is something I would like to explore more of during the residency. It’s an excerpt from a longer description which was written and recorded by Char for the project Public S/Pacing at Bloc Projects, Sheffield in 2024.
(KNOTS) IMAGE DESCRIPTIONS: Three monochrome drawings with thin black sinuous lines on a white background. They look like abstracted versions of sinews, tendons, and neurons, twisting and curving. They follow a similar pattern of emerging from the bottom left corner of the page, winding around and up diagonally until they reach the top right of the page.
In conversations about the residency, there was an emphasis on process rather than completion; I’ve started new medication this week, and as my body adjusts, my heart rate quickens and the neurotransmissions re-wire my brain, I have found that I’ve only been able to draw rather than make anything else. Drawing is always where I start in any creative process, even if it’s not related to the material I’m working on. Despite the conversations we’ve had on the residency about what we share here being ‘enough’, there are many internalised ableist assumptions about productivity, so it never really feels like it’s ‘enough’.
Hello/أهلًا
This is my digital studio; I’m an artist and writer exploring personal experiences of being queer, non-binary, disabled and part of the SWANA (South-West Asian and North African) diaspora to wider forms of structural violence.
I’ve recently started a PhD and have been focusing on writing, so I’m looking forward to be able to have some dedicated studio time again, and to experiment with physical making alongside research. I hope to expand on the themes in my research on collective and intergenerational trauma, colonialism, speech, language and silence as resistance.
Feel free to get in touch with me via the comments.
Bye يلا باي
Hi, I’m Helen Stratford, I’m an artist-architect and academic. I live and work in between the Cambridgeshire Fens and Sheffield.
I have a social practice that embraces performance, art, architecture, speculative diagramming, writing and civic action. Changing life experiences have led me to recalibrate my practice. Exploring personal worlds of pain, health and limited energy, my current work seeks to re-encounter, subvert, or ‘crip’, public spaces and practices built around expectations of hyper-productivity and optimisation; re-interpreting them with care and humour.
At this moment in creatively and sustainably embedding my lived experience in my work, I believe in centring ‘crip time’ – how disabled, sick, and Mad bodyminds navigate time/space – as a critical tool for innovative forms of art.
During the residency I am planning to work between drawing and performance-based actions to develop an interactive space that invites audiences to examine the tensions and ecologies between work, wellness and (radical) rest. I am excited to use this time to connect with my peers in exploring how places of friction, failure, unknowing and ridicularity might critique the normative and extractive presumptions that structure daily life, and support modes of self-care and mutual aid. The residency is a direct development of an Arts Council England DYCP grant (2025/26) exploring ‘Ecologies of Crip Time.’
Please take some time to look around my studio, at the work and thoughts. Feel free to ask questions and get in touch through the comments!
With care
Helen
Hi, I’m Mariette Moor, welcome to my digital studio.
My physical studio is currently my bed, a markedly horizontal departure from my previous setup which operated from a strictly upright position: working with all kinds of sculptural materials, disappearing into intensive drawing stints, and collaborating with some wonderful people.
I’m now experimenting with how to make work while stuck inside with a chronic illness that has, over the last two years, shrunk my world to a very small square footage. My practice has continued to develop, but only in my head, which turns out to be a very interesting, if pretty repetitive, place.
My previous research into horror, intimacy, and scale now seems eerily connected to my current circumstances. During this residency, I’ll be considering how my relationship with these subjects, particularly scale, has shifted since living in a body and home shaped by intertwined care roles and health conditions.
Thanks for taking a look. Feel free to get in touch.
Warm wishes,
Mm x
Having set up the object detection pipeline, I proceeded to the image processing stage, where the raw visual data and the model’s outputs are transformed into a more artistic representation. My process involved several key steps. First, I applied edge detection algorithms to the video frames. This technique identifies points in a digital image where the brightness changes sharply, effectively outlining the shapes and contours of objects in the scene. Next, I inverted the black and white colour, creating a stark, high-contrast visual style. Finally, I took the bounding boxes generated by the YOLO detection model and redrew them onto this processed image. This layering of machine perception over a stylised version of reality creates a compelling visual dialogue between the actual scene and the AI’s interpretation of it.
With the video pipeline established, I turned my attention to processing the visual data using a combination of powerful tools. The core of this stage is the YOLO (You Only Look Once) object detection model. YOLO is a state-of-the-art, real-time object detection system that identifies and classifies objects in a single pass of an image, making it incredibly fast and efficient. For this project, I am intentionally using the model with the pre-trained COCO (Common Objects in Context) dataset. The COCO dataset is a large-scale collection of images depicting common objects in everyday scenes and is a standard benchmark for training and evaluating computer vision models.
My goal is not to achieve flawless object recognition but rather to play with the inherent “mistakes” and misinterpretations the machine makes. The default COCO dataset is perfectly suited for this, as its generalised training can lead to incorrect predictions when applied to novel or ambiguous scenes. To manipulate the image data, which is essentially a collection of pixels, I am using NumPy (Numerical Python). NumPy is a fundamental library for scientific computing in Python that allows for efficient manipulation of large, multi-dimensional arrays and matrices—the very structure that represents digital images.
What is Object Detection?
Object detection is a field of computer vision and image processing concerned with identifying and locating instances of objects within images and videos. Unlike simple image classification, which assigns a single label to an entire image, object detection models draw bounding boxes around each detected object and assign a class label to it, providing more detailed information about the scene.
What are NumPy and the COCO Dataset?
Objects Detectable by the COCO Dataset:
The COCO dataset can identify 80 common object categories, including:
With a large dataset of generated policies, the next step is to import them back into the primary software application that displays the 360-degree video. This integration allows the dynamically generated rules to influence the visual output or behaviour of the system in real-time. My use of the term “policy” is a deliberate nod to its origins in the field of Reinforcement Learning (RL), a concept dating back to the 1990s. In RL, a policy is the strategy an agent employs to make decisions and take actions in its environment. It is the core component that dictates the agent’s behaviour as it learns through trial and error to maximise cumulative reward. By generating policies based on visual input, my system is, in a sense, creating its own world model—a simplified, learned representation of its environment and the relationships within it. This process echoes the fundamental principles of how an AI agent learns to react to and make sense of the real world, a topic I have delved into in more detail in some of my earlier writings.
In a more experimental turn, I developed a separate piece of software to explore the concept of emergent behaviour based on the object detection output. This program uses a Large Language Model (LLM) to generate “policies” when objects from the COCO dataset are detected in close proximity on the screen. The system calculates the normalised distance between the bounding boxes of detected objects. This distance value is then fed to the LLM, which has been prompted to generate a policy or rule based on the perceived danger or interaction potential of the objects being close together. For instance, if a “person” and a “car” are detected very close to each other, the LLM might generate a high-alert policy, whereas a “cup” and a “dining table” would result in a benign, functional policy. This creates a dynamic system where the AI is not just identifying objects, but also creating a narrative or a set of rules about their relationships in the environment.
The initial phase of this project involved tackling the technical groundwork required to process 360-degree video. I began by using OpenCV, a powerful open-source computer vision library, to stitch together the two separate video feeds from my 360-degree camera. OpenCV is an essential tool for real-time image and video processing, providing the necessary functions to merge the hemispheric views into a single, equirectangular frame. After successfully connecting the camera to my computer, I set up a basic Python workspace within my integrated development environment (IDE). The next step was to write a script that could access the camera’s video stream and display it in a new window, confirming that the foundational hardware and software were communicating correctly. This setup provides the visual canvas upon which the subsequent layers of AI-driven interpretation will be built.
I am revisiting a creative process that has captivated my interest for some time: enabling an agent to perceive and learn about its environment through the lens of a computer vision model. In a previous exploration, I experimented with CLIP (Contrastive Language-Image Pre-Training), which led to the whimsical creation of a sphere composed of text, a visual representation of the model’s understanding. This time, however, my focus shifts to the YOLO (You Only Look Once) model. My prior experiences with YOLO, using the default COCO dataset, often yielded amusingly incorrect object detections—a lamp mistaken for a toilet, or a cup identified as a person’s head. Instead of striving for perfect accuracy, I intend to embrace these algorithmic errors. This project will be a playful exploration of the incorrectness and the fascinating illusions generated by an AI model, turning its faults into a source of creative inspiration.
* visualization using CLIP and Blender for artwork “Golem Wander in Crossroads”
Ultralytics YOLO
Ultralytics YOLO is a family of real-time object detection models renowned for their speed and efficiency. Unlike traditional models that require multiple passes over an image, YOLO processes the entire image in a single pass to identify and locate objects, making it ideal for applications like autonomous driving and video surveillance. The architecture divides an image into a grid, and each grid cell is responsible for predicting bounding boxes and class probabilities for objects centered within it. Over the years, YOLO has evolved through numerous versions, each improving on the speed and accuracy of its predecessors.
(Text from Gemini-2.5-Pro and edited by artist)
CLIP
https://github.com/openai/CLIP
CLIP (Contrastive Language-Image Pre-Training), developed by OpenAI, is a neural network that learns visual concepts from natural language descriptions. It consists of two main components: an image encoder and a text encoder, which are trained jointly on a massive dataset of 400 million image-text pairs from the internet. This allows CLIP to create a shared embedding space where similar images and text descriptions are located close to one another. A key capability of CLIP is “zero-shot” classification, meaning it can classify images into categories it wasn’t explicitly trained on, simply by providing text descriptions of those categories.
(Text from Gemini-2.5-Pro and edited by artist)
COCO
https://docs.ultralytics.com/datasets/detect/coco
COCO (Common Objects in Context), is a large-scale object detection, segmentation, and captioning dataset. It is designed to encourage research on a wide variety of object categories and is commonly used for benchmarking computer vision models. It is an essential dataset for researchers and developers working on object detection, segmentation, and pose estimation tasks.
(Text from Ultralytics YOLO Docs)
Since I went to Bath, I’ve been dreaming about its backrooms. Divots in sandy stone filled with green water that I can slide my arm into, an exact fit. Semi-organic cave networks getting darker and deeper, pools of water deep and shallow suspended and plunging at different levels.
Do you know about The Backrooms? They’re this concept of liminal spaces, usually devoid of living beings, that you can enter by leaving reality. There’s a Dream Pool genre of Backrooms that I’ve been following for a while, and earlier this year a game: Dreamcore, came out which holds a lot of these to explore. There’s something cyclical about recognising these as a pocket of your subconscious mind, then taking the images back into your sleep…
Had a little moment earlier in the week feeling the breeze on my hand while in traffic. I wanted to recreate this image or at least experiment with this idea a little more but then some barriers got in the way (broken lifts) so I’ve been stuck indoors for a couple days.
‘Those seeking divine help for an illness or affliction might rest overnight in special temple buildings. On waking, priests of the Roman god of healing, Aesculapius, helped them interpret their dreams or visions’
I made it to the temple.
Image IDs in Alt Text, Video IDs here:
This week, Hong Kong was battered by heavy rain, and I took the chance to take a breather and recharge. The last few weeks have been manic. I’ve been working on three software projects at once. The non-stop pace had left me totally overloaded, so this rain break was just what I needed. I decided to visit my wife home village, a recharging place in the middle of the city’s forests. The air smelt of earth, and the quiet beauty of the landscape was a nice change. I could feel the tension of my tightly wound days begin to unravel, replaced by a sense of calm that felt long overdue. The mountains were like silent guards, making me think about the balance between creativity and rest.
I might have got myself a little stuck in my searches. I tried a few online image libraries… trawling the many pages of the Wellcome Collection’s catalogue which I still haven’t reached the end of.
I already have examples of what I would like from some of my previous work that I have shared so I’m giving myself a reminder that the task isn’t impossible. I am considering the thought that maybe I’m already surrounded by the images I’m looking for. For example, I have a mug with this John William Waterhouse painting on it.
Wanted to include some phone images I took from a book on drawing people that I found in the local library. There are loads of them on drawing people, cats, dogs, flowers, buildings… It’s all very Drawing™️.
A little fascinated by the eery “perfection” of it all. Especially in this book which was full of sketches and descriptions of muscles that make up a body part and how to combine it all together on a super athletic male body. It’s quite the opposite to what I was hoping to find when I set out on this search for images. It’s almost too healthy and tense. There’s no ease.
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