A dilapidated grey building is seen as in a 3D plan against a bright red background. 0s and 1s represent data streaming out of the windows and gaps in the roof.

Since AI is such a wide field of applications, tools, systems and more, it would be misleading to talk about it as if it were one universal thing. Even if we would look closer at one specific kind of artificial intelligence, we would see many layers or – let’s say – levels of algorithms that form one bigger system. This collage pictures AI as a building, or even a complex of multiple architectures, with different rooms, floors and windows. These are held together by a grid and are connected through a language. The image tries to communicate the complexity and plurality of these worlds, but also reminds that they are not a living being, rather a human made structure; hollow with no life in it.

AI Architecture

A young black man looks into the camera. He is wearing a dressing gown and has a towel around his neck. He applies a cotton pad to his face and wears a ring on his right hand. a list of words and numbers describe the man’s expression. Happiness 4.185, neutral 0.901, surprise 89.864, sadness 0.01, disgust 0.01, anger 5.021 and fear 0.01.

A young black man looks into the camera. He is wearing a dressing gown and has a towel around his neck. He applies a cotton pad to his face and wears a ring on his right hand. A series of dots linked by lines all in white creates a wireframe tracing the features of his face. In the top left corner, a list of words and numbers describes the man’s expression. Happiness 4.185, neutral 0.901, surprise 89.864, sadness 0.01, disgust 0.01, anger 5.021 and fear 0.01. "Emotion recognition" AI is increasingly being used in applications, despite it being built on scientifically dubious (or pseudoscientific) foundations which are criticised for being inconsistent with human rights, biased, inaccurate, and discriminatory.  If you wish to recreate this image in a different language, a blank version is provided at https://betterimagesofai.org/images?artist=Comuzi&title=MirrorB



Mirror D

This picture is made up of 3 images in a row, on a grey background. The first picture is an original photograph of a bowl of fresh strawberries, contrasted against the white bowl they are in a small white bottle of milk. In the middle, the photograph is now broken down into blocks of colour in the shape of the original strawberries. The final picture has been broken down even more, to the extent that the large blocks of colour are now no longer recognisable as strawberries and milk.

The work illustrates a popular type of machine learning model: the decision tree. Decision trees work by splitting the population into ever smaller segments. In the artist's words, "I try to give people an intuitive understanding of the algorithm. I also want to show that models are simplifications of reality, but can still be useful, or in this case visually pleasing. To create this, I trained a model to predict pixel colour values, based on an original photograph."

Strawberries and milk

Two people are illustrated in a warm, cartoon style, one on the left and one on the right. The person on the left, who has their back to the viewer, and is typing on a laptop which is sitting on a table. They are white, their hair is shoulder length and dark, and they are wearing a green t-shirt. The computer screen is dark with rows of coloured squares representing programming. The person on the right looks similar but their hair is now tied back in a pony tail, and they are wearing a white lab coat and safety goggles. They are reaching down to lift up an orange hazard label which is about the size of a book. The label is an orange square with a black exclamation mark in the middle. The person looks like they are being careful as they lift it.

This image is a still from a video created for the Data Hazards project, which can be watched here https://datahazards.com/. The project provides resources for data scientists. Here you see a researcher programming on the left, and on the right has donned their safety equipment to represent that they are considering the safety precautions necessary for a Data Hazard they have identified. Even though safety precautions may not look the same as they do in wet lab sciences, this image represents that it is still important to take precautions in data science work. We must still be careful, and mindful of risks in the same way that other scientists need to.

Safety Precautions

This picture is made up of 9 images in rows of 3. Each row shows a different image of a pill bottle spilling out pills onto a plain surface, on yellow or white backgrounds. On one side, the image is an original photograph. The next two iterations show it getting represented in progressively larger blocks of colour.

Medicines (flipped)

A hand drawn sketch in coloured pens depicts a small uniform row of houses at the bottom of the picture. From each of the houses, 0s and 1s representing digital data are floating up in plumes which look like smoke from the chimneys of the houses; but these are not uniform, therefore representing the different types of data from each house.

This image shows the digital flow that has become a proxy for direct and deliberate human communication. The artist sketched live in response to a conversation about generative AI in public services. "The reduction of our understanding of each other (in this case, a neighborhood of people in the UK) assumes a great deal – including people's understanding of, access to, and willingness to engage digital methods of interacting with society. If governments use data as a proxy for understanding, important signals will go unheard – whether they are just obfuscated in the data (one of these house's data smoke is asking for help) or missing entirely."

Little data houses (square)

Five people and a dog are seen outlined in orange, against an orange background. Two of the people talk to each other, one stands along with a stick, one walks a dog, and the other is in a wheelchair. All of them look at their mobile phones intently, and all cast shadows on the ground. The shadows are made up of network diagrams, being representative, rather than literal shadows.

This was originally an A4 report cover painting commissioned by Reset.Tech Australia for a policy briefing on misinformation and disinformation regulatory frameworks. The illustration shows people from all walks of life looking at their phones, being in a world of false information highlighted by the ominous flame-like colour of the picture. The people cast shadows of data and algorithms which represent the way in which they recieved the information, and are passing it on.

People on phones (portrait)

Medicines

An image of two leaves in red and yellow with a small square to the left of them depicting a zoomed in image of a green leaf. The zoomed in area is multidimensional and pixellated.

This image is part of the Google DeepMind collection "Visualising AI" (https://deepmind.google/discover/visualising-ai), with Flying Objects. XK Studio were commissioned and their approach was to abstract different areas where AI has the potential of aiding and enriching our lives, such as science, biology, climate change and environment, engineering and computing.

AI Lands

A group of five cloud shapes float across a blue background. The clouds are collages made out of close up photographs of shiny silver silicon surfaces.

This collage was made from a photograph of a silicon mono-crystal on a white background, a crucial component of AI when made into chips or CPUs. Picturing silicon visually asserts AI’s materiality and illustrates that the ‘mining’ of AI is not purely metaphorical but also a literal, material undertaking. Responsible for sustaining innovation within ‘Silicon valley’ / wider tech, silicon enables the packing of greater computational/processing power into a single surface, which is necessary for machine learning. In this picture, original pictures of silicon rocks are collaged into the shape of clouds to make the point that the "AI Cloud" – the merging of cloud computing and AI services – may sound very ethereal but actually relies on physical materials and mineral extraction. Catherine Breslin, the photographer, operates within the AI supply-chain first-hand in her work as a machine-learning voice engineer and consultant, previously involved in building Amazon Alexa.

AI silicon clouds collage

A shiny rock of silicon is on a plain white reflective surface, against a plain white background. Embedded in the rock is a rough shape of a silicon chip, which is made from the same rock of silicon

This is an AI generated image made from a reference image photograph by Catherine Breslin. The Better Images of AI team used Adobe Firefly as part of our policy to only use image generators which use consented, compensated training data (pictures). Our policy also includes using reference images from contributors. This reference photograph was of a silicon mono-crystal on a white background, a crucial component of AI when made into chips or CPUs. Picturing silicon visually asserts AI’s materiality and illustrates that the ‘mining’ of AI is not purely metaphorical but also a literal, material undertaking. Responsible for sustaining innovation within ‘Silicon valley’ / wider tech, silicon enables the packing of greater computational/processing power into a single surface, which is necessary for machine learning. In this picture, the image generator was prompted to show a rough chip emerging from the raw material. Catherine Breslin, the photographer, operates within the AI supply-chain first-hand in her work as a machine-learning voice engineer and consultant, previously involved in building Amazon Alexa.

Chipping Silicon

Seventeen multicoloured post-it notes are roughly positioned in a strip shape on a white board. Each one of them has a hand drawn sketch in pen on them, answering the prompt on one of the post-it notes "AI is...." The sketches are all very different, some are patterns representing data, some are cartoons, some show drawings of things like data centres, or stick figure drawings of the people involved.

This image shows the many different ways in which people might visualise the many different facets of AI. It shows the different applications, concepts, processes and technologies which 2 simple letters can mean. These post-it notes were drawn by the audience and team at the launch of the Better Images of AI Guide for Users and Creators https://blog.betterimagesofai.org/better-images-of-ai-guide/, held at The Alan Turing Institute.

Ai is... Banner

A neural network comes out of the top of an ivory tower, above a crowd of people's heads. Some of them are reaching up to try and take some control and pull the net down to them. Watercolour illustration.

The picture uses the metaphor of an ivory tower to show where decision making is done, with a representation of a neural network coming out of it, above a crowd of people's heads. Some of them are reaching up to try and take some control and pull the net down to them, but the image can also be read as AI targeting some of them. This image was commissioned for the relaunch of We and AI's website, which aimed to show a more handmade style, to reference the people involved in AI, the messiness of human production, but also necessity of codesigning together.

People and Ivory Tower AI 2

On the left, there is an image of a tree on a snowy, rocky landscape, with light shining in from the background. On the right, there is the same landscape, but it is now reproduced as grey and silver cubes in the shape of a tree and technical terrain, backgrounded by the light.

An artist’s illustration of artificial intelligence (AI). This visual depicts the process used by text-to-image diffusion models. It was created by artist Linus Zoll as part of the Visualising AI project launched by Google DeepMind. “Using AI to generate images is a fascinating and rapidly growing trend, and I was thrilled to explore this field when I was tasked with showcasing the workings of Large Image Models. My approach was to illustrate how the AI can transform a blank canvas into a finished artwork in an entirely automated manner. To convey the image data that the AI relies on, I decided to use cubes with different materialities that are sorted and reorganised until they form a surreal artwork.”

Generative Image models

A hand-drawn sketch in coloured pens depicts a small uniform row of houses at the bottom of the picture. From each of the houses, 0s and 1s representing digital data are floating up in plumes which look like smoke from the chimneys of the houses; but these are not uniform: they represent the different types of data from each house.

This image shows the digital flow that has become the proxy for direct and deliberate human communication. The artist sketched live in response to a conversation about generative AI in public services. "The reduction of our understanding of each other (in this case, a neighborhood of people in the UK) assumes a great deal – including people's understanding of, access to, and willingness to engage digital methods of interacting with society. If governments use data as a proxy for understanding, important signals will go unheard – whether they are just obfuscated in the data (one of these house's data smoke is asking for help) or missing entirely."

Little data houses

Seven people and a dog are illustrated in watercolour standing against a light blue background, in a variety of poses but all intently looking at their mobile phones. They are coloured in blue with yellow detail, and cast blue shadows. Their phones are all connected via a web like network in the air, showing various connection points. The image is portrait shape and allows space for text in the top left hand corner.

This image was commissioned for a report "Is political content over- or under-moderated?" by Reset.Australia. AI tools are sometimes used for content moderation. The report starts "Content moderation on social media can result in the removal, demotion or labelling of content that platforms deem to have violated their rules. Moderation is an important tool in mitigating systemic risks on platforms, especially when it comes from misinformation and disinformation. However, if moderation goes wrong, it can lead to content being either over-moderated (too much being inappropriately taken down) or under-moderation (not enough content that violates platform’s rules being taken down). Platforms can also create conditions of potential political bias if they over- or under-moderate in particular ways." 

Social media content

An image of multiple 3D shapes representing speech bubbles in a sequence, with broken up fragments of text within them.

An artist’s illustration of artificial intelligence (AI). This image depicts language models which generate text. It was created by Wes Cockx as part of the Visualising AI https://unsplash.com/blog/visualizing-ai-with-google-deepmind/project launched by Google DeepMind. 

“The process involves perceiving, synthesizing, and interfering with the data. Once the answer to the initial question takes shape, the animation displays a sequence of synonyms for three keywords: perceive, synthesize, and interfere. This is achieved by computing a probability distribution across all possible next words or sequences of words. The network then randomly selects a word or sequence by sampling from that distribution to generate the next word or sequence.”

AI large language models

An image of a butterfly perched on a branch, framed in a square, surrounded by seemingly energised earthy elements which are seen in a pixellated form.

This image depicts how AI could help understand ecosystems and identify species. It was created by Nidia Dias as part of the Visualising AI project launched by Google DeepMind https://unsplash.com/blog/visualizing-ai-with-google-deepmind/ “Driven by the current applications of AI in biodiversity, the concept for this piece aims to illustrate the speed and efficiency with which AI can identify species within a specific environment. To depict this, I wanted to design a piece of land that, in an abstract manner, could display ample diversity indicative of various species, thus symbolizing biodiversity. I tried to pair that with a somehow abstract nature, with the aim to maintain simplicity and to ensure its adaptability across different contexts. It's truly captivating to consider how AI's efficiency in species identification can save scientists valuable time, allowing them to focus more on environmental preservation and the protection of endangered species.”

AI for Biodiversity

Landscape shaped image. Four people are outside on a pavement in front of some railings and lush green foliage. Two of them have a white frame superimposed round their heads, showing them being surveilled, perhaps from a drone. 

Four people are outside on a pavement in front of some railings and lush green foliage. It is a relaxed scene and appears to be viewed through a camera with ‘time log information positioned in the top left and right corners respectively.. One sits on a bench and has an identification box around her bag and head. In original version of this image, identifying features including female and caucasian were displayed, but it has been left blank for use in different languages. Three men are in the centre-right of the image. On the original image one has an identification box with an ID number and identifying features listed as male, black, hoodie. Identifiers can be layered on to this image to show the kind of characteristics biometric systems might identify.



Surveillance View B

A young black man in a dark t-shirt looks at a mobile device. Behind him, a blue digital background shows images of the trainers he's looking at on his phone. A trail of digital heart shapes fades upwards indicating that the man in the image likes one of the images.

A young black man in a dark t-shirt looks at a mobile device, he is slightly turned to the right of the image. Behind him, a blue digital background shows images from a picture-based social network. Some images are washed with blue and one stands out without a blue wash, it is an image of a black sneaker. Above the mobile device a trail of digital heart shapes fades upwards indicating that the man in the image likes the photo of the shoe. White lines illustrate an algorithm making connections between the images.

Long portrait shaped image. 

Likes (tall)

Three groups of icons representing people have shapes travelling between them and a page in the middle of the image. The page is a simple rectangle with straight lines representing data. The shapes traveling towards the page are irregular and in squiggly bands.

This image shows an example of a Large Language Model in use. Data is gathered from groups of people communicating with each other. A machine learning algorithm is used to select segments of data from each group of people and then aggregate that data into a neat output. It is not obvious to human observers why specific segments of data were selected by the algorithm, as represented by the irregular geometric shapes. This image was created as the result of a Better Images of AI workshop at Science Gallery London as part of London Data Week 2023, in response to conversation with Dr Tamsin Nooney of BBC R&D.

Data Processing

Seventeen multicoloured post-it notes are roughly positioned in a square shape on a white board. Each one of them has a hand drawn sketch in pen on them, answering the prompt on one of the post-it notes "AI is...." The sketches are all very different, some are patterns representing data, some are cartoons, some show drawings of things like data centres, or stick figure drawings of the people involved.

This image shows the many different ways in which people might visualise the many different facets of AI. It shows the different applications, concepts, processes and technologies which 2 simple letters can mean. These post-it notes were drawn by the audience and team at the launch of the Better Images of AI Guide for Users and Creators https://blog.betterimagesofai.org/better-images-of-ai-guide/, held at The Alan Turing Institute. 

AI is...

Building blocks are overlayed with digital squares that highlight people living their day-to-day lives through windows. Some of the squares are accompanied by cursors.

This image illustrates the potential for bias in AI, in the setting of smart cities and provision of local services in a city. The image shows a variety of people carrying out their daily activities in a city, while various cursors and boxes show how some might be selected over others, or clustered by the data collected about them. The densely populated city with a mixture of buildings mirrors the design of a GPU. This image was created as the result of a Better Images of AI workshop at Science Gallery London as part of London Data Week 2023, in response to a public conversation with Sam Nutt.

AI City

Five people and a dog are seen in outline in orange, against an orange background. Two of the people talk to each other, one stands along with her stick, one walks a dog, and the other is in a wheelchair. All of them look at their mobile phones intently, and all cast shadows on the ground. The shadows are made up of network diagrams, being representative rather than a literal shadow.

This is a detail from a watercolour commissioned by Reset.Tech Australia for a policy briefing on Misinformation and disinformation regulatory frameworks. The illustration shows people from all walks of life looking at their phones, being in a world of false information highlighted by the ominous flame-like colour of the picture. The people case shadows of data and algorithms which represent the way in which they received the information, and are passing it on.

People with phones

A young black man looks into the camera. He is wearing a dressing gown and has a towel around his neck, and there is green wallpaper behind. His face is mapped with a white wireframe. 

A young black man looks into the camera. He is wearing a dressing gown and has a towel around his neck. He is running his right hand over his hair and wears a ring on that same hand. Overlaid on his face, a series of dots linked by lines all in white creates a mask-like image. The picture was inspired by thinking about emotion-sensing AI, in which facial recognition technology is used to attempt to detect different expressions such as disgust, fear, and happiness. It is used in applications such as hiring, marketing, online learning, and monitoring employees. It can be inaccurate and biased, and not sensitive to cultural differences. 

Mirror B

Several small cartoon representations of people are linked by brown handdrawn lines representing a network.

This is a detail from a watercolour commissioned by Reset.Tech Australia for a report series on misinformation and disinformation.  It shows how people are connected in networks through which information can quickly spread, and the way the people are positioned within the representation of the network makes them seem as they are just points in an algorithm, which makes them vectors. The original image is a portrait cover with space for text, and a watercolour background.

Detail from Connected People

A watercolour illustration in two strong colours showing the silhouettes of four people, two of whom have dogs on leads. They all cast shadows, and vary between realistic representations and those formed by representations of algorithms, data points or networks. The people and their data become indistinguishable form each other.

As we live our semi-digital lives, we generate so much data, which is used for AI models. We can’t see it, but it’s like a shadow that follows us around. It’s usually profitable to companies that create platforms through which we generate this data. 

Data People

A neural network comes out of the top of an ivory tower, above a crowd of people's heads (shown in green to symbolise grass roots). Some of them are reaching up to try and take some control and pull the net down to them. Watercolour illustration.

The picture uses the metaphor of an ivory tower to show where decision making is done, with a representation of a neural network coming out of it, above a crowd of people's heads (shown in green to symbolise grass roots). Some of them are reaching up to try and take some control and pull the net down to them, but the image can also be read as AI targeting and some of them. This image was commissioned for the relaunch of We and AI's website, which aimed to show a more handmade style, to reference the people involved in AI, the messiness of human production, but also necessity of codesigning together.

People and Ivory Tower AI

Young adults are walking on a street. Two young women are on the pavement to the left of the image and five young men walk in the road. The road is empty of traffic and the people in the picture are relaxed and appear to be engaged in chatting with each other.  Overlaid on the image are identification boxes. All of the figures have ID boxes around them. 

Young adults are walking on a street. Two young women are on the pavement to the left of the image and five young men walk in the road. The road is empty of traffic and the people in the picture are relaxed and appear to be engaged in chatting with each other.

Overlaid on the image are identification boxes. All of the figures have ID boxes around them. The pictures appear to have been taken by a drone.

The image shows how information can be found about people using biometrics based on computer vision technology, and how unaware they might be.

Surveillance View A.

A young black man in a dark t-shirt looks at a mobile device. Behind him, a blue digital background shows images of the trainers he's looking at on his phone. A trail of digital heart shapes fades upwards indicating that the man in the image likes one of the images White lines illustrate an algorithm making connections between the images.

A young black man in a dark t-shirt looks at a mobile device. He is slightly turned to the right and stands in the right side of the image. On the left of the image, a blue digital background shows images from a picture-based social network. Some images are of trainers and footwear washed with blue and one stands out without a blue wash, it is an image of a black sneaker. Above the mobile device a trail of digital heart shapes fades upwards indicating that the man in the image likes the photo of the shoe. White lines illustrate an algorithm making connections between the images.

Letterbox landscape image 

Likes (wide) 

A person with their hands on a laptop keyboard is looking at something happening over their screen with a worried expression. They are white, have shoulder length dark hair and wear a green t-shirt. The overall image is illustrated in a warm, sketchy, cartoon style.  Floating in front of the person are three small green illustrations representing different industries, which is what they are looking at.  On the left is a hospital building, in the middle is a bus, and on the right is a siren with small lines coming off it to indicate that it is flashing or making noise.  Between the person and the images representing industries is a small character representing artificial intelligence made of lines and circles in green and red (like nodes and edges on a graph) who is standing with its ‘arms’ and ‘legs’ stretched out, and two antenna sticking up.  A similar patten of nodes and edges is on the laptop screen in front of the person, as though the character has jumped out of their screen.  The overall image makes it look as though the person is worried the AI character might approach and interfere with one of the industry icons.

A data scientist had found that their work (the algorithm depicted on their laptop screen) has ‘jumped’ out of the screen and threatens to cause problems with a variety of different industries. Here a hospital, bus and siren could represent healthcare, transport and emergency services. The data scientist looks shocked and worried about what trouble the AI may cause there.

AI across industries.

A laptopogram on a plain background with very simplistic black-outlined blocks stretching across the centre, almost end to end. The blocks are linked, but not solidly, often leaving small gaps between the objects suggesting they could still shift around and recombine.

This image shows a diagram of a neural network algorithm, created by a machine learning researcher to communicate the system’s architecture. Diagrams like this one are often drawn as a series of connected boxes. They depict cognitive functions as a linear procedures, comprised of basic building blocks that can be recombined to one’s heart’s content. The image is a ‘laptopogram’, created by exposing photographic paper using a computer screen and developed in the artist’s bathtub. The process preserves a digital artifact of AI research in silver crystals, returning a physical dimension to sterile data. Dust, scratches, and the marks left by the artist’s hands draw a connection to the role of the researchers' subjectivity in making AI.

Neural network diagram

Three colorful GPUs with their packaging cleanly removed laying on a white surface

The GPU (Graphics Processing Unit) is an essential part of modern AI infrastructure. It’s a special type of chip or electronic circuit that is used in mobile phones, personal computers, game consoles, workstations and cloud computing infrastructure. Its close cousin is the CPU (Central Processing Unit). GPUs were originally designed to process images and render graphics. Nowadays they’re used for other computational tasks as well such as training neural networks in deep learning. GPUs can process many chunks of data simultaneously which is called parallelization. As of 2018 the companies NVIDIA and AMD control nearly 100% of the GPU market. These photographs are known as “die-shots“. A die is a small block of semiconducting material, such as Silicon, that serves as the supporting material on which an integrated circuit is fabricated. Die-shots are close-up photographs of computer chips, from which the “packaging“ is removed, usually by undergoing a quite dangerous etching process involving sulfuric acid and high temperatures. The typical bright rainbow colors in die-shots do not represent the actual colors of the chips, which are mainly gray and silver. The artist has used a combination of external light sources, polarising filters on the camera lense and image post production to create the colourful effect. This picture shows three NVIDIA Turing Chips (TU104, TU106, TU116)

GPU shot etched 2

An person is illustrated in a warm, cartoon-like style in green. They are looking up thoughtfully from the bottom left at a large hazard symbol in the middle of the image. The Hazard symbol is a bright orange square tilted 45 degrees, with a black and white illustration of an exclamation mark in the middle where the exclamation mark shape is made up of tiny 1s and 0s like binary code. To the right-hand side of the image a small character made of lines and circles (like nodes and edges on a graph) is standing with its ‘arms’ and ‘legs’ stretched out, and two antenna sticking up. It faces off to the right-hand side of the image.

An illustrated person looks up at a large hazard symbol, which has a character representing data science and AI ‘standing’ next to it. The AI character is made up of nodes and edges (like a network diagram). The hazard symbol is the General Data Hazard from the Data Hazards project, and is a large exclamation mark made up of binary code.

Here, the person is considering the Data Hazard and how they could use it to help them manage the risks the AI has been causing.

Managing Data Hazards

A block of silicon (also known as a mono-crystal) placed on a plain black background and photographed in HD to make its rich, reflective and complex surface visible.

This image shows a silicon mono-crystal on a neutral background, a crucial component of AI manufacture: a block like this would be sliced into 12 inch diameter wafers to form the base of CPUs. Picturing silicon visually asserts AI’s materiality and illustrates that the ‘mining’ of AI is not purely metaphorical (e.g. data mining) but also a literal, material undertaking. Responsible for sustaining innovation within  ‘Silicon valley’/wider tech, silicon enables the packing of greater computational/processing power into a single surface allowing for more efficient data movement, the co-location of memory and processing and parallelisation of function, necessary for machine learning. Catherine Breslin, the photographer, operates within the AI supply-chain first-hand in her work as a machine-learning voice engineer and consultant, previously involved in building Amazon Alexa.

Silicon on Black 1

A photographic rendering of a young black man standing in front of a cloudy blue sky, seen through a refractive glass grid and overlaid with a diagram of a neural network.

This image shows a young black man wearing a black coat staring past the camera in front of a blue cloudy sky. The scene is refracted in different ways by a fragmented glass grid. This grid is a visual metaphor for the way that new artificial intelligence (AI) and machine learning technologies can be used to extract and analyse behavioural and demographic data in innovative ways. Some of the grid squares reveal graphical interpretations of the man that exceed the capabilities of human vision, indicating how cutting edge technologies offer ways to augment traditional human understandings of complex phenomena. A neural network diagram is overlaid, familiarising the viewer with the formal architecture of AI systems.

Quantified Human

A young woman sitting in front of a computer in an office while another woman standing next to her is pointing at something on her screen. They are both dressed in casual clothes - a bright blue top and furry waistcoat, and a denim jacket.

Data annotation is becoming a professional field of its own, with many annotators working on it as full-time employees, following complex guidelines, and using a variety of different tools and platforms. What used to be considered 'menial' and 'low-skilled' work, is today a nascent field with its own complexities and skills requirements, which involves extensive training and specializing in different modalities: video labeling, medical imagery labeling, 3D LiDar labeling, and so on. It is important to recognize the value that data annotators bring and their expertise, in order to uplift them from the 'ghost work' position in which they are now.

A trainer instructing a data annotator on how to label images

A laptopogram based on a neutral background and populated by scattered squared portraits, all monochromatic, grouped according to similarity. The groupings vary in size, ranging from single faces to overlapping collections of up to twelve. The facial expressions of all the individuals featured are neutral, represented through a mixture of ages and genders.

This image shows face images from the Olivetti faces dataset, created at AT&T Laboratories Cambridge in the early 1990s. The portraits are scattered across the image and grouped by similarity. The image is a ‘laptopogram’, created by exposing photographic paper using a computer screen and developed in the artist’s bathtub. The process preserves a digital artifact of AI research in silver crystals, returning a physical dimension to sterile data. Dust, scratches, and the marks left by the artist’s hands draw a connection to the role of the researchers' subjectivity in making AI.

Data flock (faces)

Abstract microscopic photography of a Graphics Processing Unit resembling a satellite image of a big city

The GPU (Graphics Processing Unit) is an essential part of modern AI infrastructure. It’s a special type of chip or electronic circuit that is used in mobile phones, personal computers, game consoles, workstations and cloud computing infrastructure. Its close cousin is the CPU (Central Processing Unit). GPUs were originally designed to process images and render graphics. Nowadays they’re used for other computational tasks as well such as training neural networks in deep learning. GPUs can process many chunks of data simultaneously which is called parallelization. As of 2018 the companies NVIDIA and AMD control nearly 100% of the GPU market. These photographs are known as “die-shots“. A die is a small block of semiconducting material, such as Silicon, that serves as the supporting material on which an integrated circuit is fabricated. Die-shots are close-up photographs of computer chips, from which the “packaging“ is removed, usually by undergoing a quite dangerous etching process involving sulfuric acid and high temperatures. The typical bright rainbow colors in die-shots do not represent the actual colors of the chips, which are mainly gray and silver. The artist has used a combination of external light sources, polarising filters on the camera lense and image post production to create the colourful effect. This picture shows a “NVIDIA GeForce GTX 280”

GPU shot etched 5

A photographic rendering of a young houseplant against a neutral background, seen through a refractive glass grid and overlaid with an single neuron from a neural network diagram.

This image shows a houseplant against a neutral background. The scene is refracted in different ways by a fragmented glass grid. This grid is a visual metaphor for the way that artificial intelligence (AI) and machine learning technologies can be used to extract and analyse biological data in innovative ways, and the repetitive and self-similar structure of these systems. A single neuron from a neural network diagram is overlaid, revealing an augmented data view of the plant.

Plant

Silicon on Black 2

Silicon Closeup

Abstract microscopic photography of a Graphics Processing Unit resembling a floor plan or fractal art

The GPU (Graphics Processing Unit) is an essential part of modern AI infrastructure. It’s a special type of chip or electronic circuit that is used in mobile phones, personal computers, game consoles, workstations and cloud computing infrastructure. Its close cousin is the CPU (Central Processing Unit). GPUs were originally designed to process images and render graphics. Nowadays they’re used for other computational tasks as well such as training neural networks in deep learning. GPUs can process many chunks of data simultaneously which is called parallelization. As of 2018 the companies NVIDIA and AMD control nearly 100% of the GPU market. These photographs are known as “die-shots“. A die is a small block of semiconducting material, such as Silicon, that serves as the supporting material on which an integrated circuit is fabricated. Die-shots are close-up photographs of computer chips, from which the “packaging“ is removed, usually by undergoing a quite dangerous etching process involving sulfuric acid and high temperatures. The typical bright rainbow colors in die-shots do not represent the actual colors of the chips, which are mainly gray and silver. The artist has used a combination of external light sources, polarising filters on the camera lense and image post production to create the colourful effect. This picture shows a “Radeon R9 290” by AMD.

GPU shot etched 1

A laptopogram displaying a dataset as cloud-like clusters of black blobs on a neutral background. There are three larger collections, almost resembling a map, with some data points leaking out into the negative space.

This image shows a machine learning dataset visualized spatially, in cloud-like clusters according to visual similarity of the data. Although visualizations like this one always simplify and fail to represent the data’s true complexity and nuance, they guide the researchers’ intuitions for their subject matter. The image is a ‘laptopogram’, created by exposing photographic paper using a computer screen and developed in the artist’s bathtub. The process preserves a digital artifact of AI research in silver crystals, returning a physical dimension to sterile data. Dust, scratches, and the marks left by the artist’s hands draw a connection to the role of the researchers' subjectivity in making AI.

Data flock (digits)

Abstract microscopic photography of a Graphics Processing Unit in rainbow colors

The GPU (Graphics Processing Unit) is an essential part of modern AI infrastructure. It’s a special type of chip or electronic circuit that is used in mobile phones, personal computers, game consoles, workstations and cloud computing infrastructure. Its close cousin is the CPU (Central Processing Unit). GPUs were originally designed to process images and render graphics. Nowadays they’re used for other computational tasks as well such as training neural networks in deep learning. GPUs can process many chunks of data simultaneously which is called parallelization. As of 2018 the companies NVIDIA and AMD control nearly 100% of the GPU market. These photographs are known as “die-shots“. A die is a small block of semiconducting material, such as Silicon, that serves as the supporting material on which an integrated circuit is fabricated. Die-shots are close-up photographs of computer chips, from which the “packaging“ is removed, usually by undergoing a quite dangerous etching process involving sulfuric acid and high temperatures. The typical bright rainbow colors in die-shots do not represent the actual colors of the chips, which are mainly gray and silver. The artist has used a combination of external light sources, polarising filters on the camera lense and image post production to create the colourful effect. This picture shows a “Radeon R9 Nano” by AMD.

GPU shot etched 3

A photographic rendering of medical swabs and pills in a jar seen through a refractive glass grid, overlaid with a diagram of a neural network.

This image shows two labelled swab tubes with red caps and a glass jar full of blue and white pills, floating against a grey background and refracted in different ways by a fragmented glass grid. This grid is a visual metaphor for the way that new artificial intelligence (AI) and machine learning technologies can be used to extract and analyse medical data in innovative ways. Some of the grid squares reveal graphical interpretations of the objects that exceed the capabilities of human vision, which indicates how cutting edge technologies offer ways to augment traditional human understandings of complex phenomena. A neural network diagram is overlaid, familiarising the viewer with the formal architecture of AI systems.

Medicine

Two digitally illustrated green playing cards on a white background, with the letters A and I in capitals and lowercase calligraphy over modified photographs of human mouths in profile.

These illustrated cards are the backs of an original playing card deck assembling 56 A and I words and chess diagrams, part of a larger experimentation of analogue game investigations about A.I. It explores an alternative redefinition of A.I as units of a social language before mathematical construct. Its purpose is to look at some of the principles of machine learning like interpretation, classification, prioritization, as components of human interaction, before its encoding. Hands, mouths and handwritten typefaces are chosen as imagery to visualize human sources and choices expressed through our technical architectures. The associative and generative modularity of a playing card deck opens up a more timeless representation of A.I, to consider our relationship to technology differently.

Handmade A.I

Plastic figure resembling a human who sits on a table infront of a laptop in a dark room. Long shadows disseminate a gloomy mood.

Clickworkers are the invisible workforce behind today's AI systems. Without actual humans who do the tedious labor of labeling datasets, machine learning and its many real-life applications would be impossible. This image shows 3D-printed figures who work at a computer in an anonymous, dark environment. They are anonymized, almost de-humanized.

Clickworker 3d-printed

Plastic figures resembling humans who sit at tables in front of laptops. the lack of background makes their envoronment look bleak.

Clickworkers are the invisible workforce behind today's AI systems. Without actual humans who do the tedious labor of labeling datasets, machine learning and its many real-life applications would be impossible. This image shows 3D-printed figures who work at a computer in an anonymous environment. They are anonymized, almost de-humanized.

Clickworker Abyss

A woman and a man sitting in front of a computer screen, pointing at something on the screen and talking, with a colourful stncil design on the wall behind them.

Data annotation for AI is a subjective activity which involves a lot of interpretative work and communication between the annotators and the data scientists. Sometimes, when you are labeling a training dataset for AI, you bump into a lot of edge cases: occluded objects, blurry images, groups of many objects together, etc. How you annotate them can be crucial for ensuring that the resulting AI system is robust in cases of uncertainty. This is what we call the 'long tail' of AI edge cases and where human involvement is most needed.

Data annotators discussing the correct labeling of a dataset

A black keyboard at the bottom of the picture has an open book on it, with red words in labels floating on top, with a letter A balanced on top of them. The perspective makes the composition form a kind of triangle from the keyboard to the capital A. The AI filter makes it look like a messy, with a kind of cartoon style.

This image refers to Natural Language Processing, known as NLP, a sub-field of AI enabling computers to understand language. Chatbots, voice assistants, automatic translation, text summarization, speech recognition and synthetic voices are applications that fall under NLP. To be able to communicate we need to have a common language. The symbols, letters and words are the connection between the human and the AI, using these we can communicate with each other. This forms the basis of language models (and wider machine learning), embedded within statistical processes of pattern recognition. Words are placed, as in this image, within a symbolic taxonomy. The letter is a part of a word, which is a part of a sentence, which is a part of a context, which creates meaning. Yet these systems have no real understanding. The illusion of understanding emerges from the pattern they have learned. The single letter 'A', missing its counterpart 'I' could represent the actual lack of intelligence in these systems. The image was generated by applying a neural network-based technique called 'style transfer' using the mobile app Prisma.

Letter Word Text Taxonomy

Two women, one of whom is wearing a colorful hijab and looking down smiling, are sitting in front of computers and typing.

Data annotation is a key activity required in order to prepare training data for Artificial Intelligence. Manual data annotation is especially needed when dealing with unstructured data: images, videos, and text. For example, if we want to train an AI model to recognize animals on images, we will need the help of data annotators who would draw bounding boxes or add tags to each image depending on what animal it depicts. Data annotation work is being performed by hundreds of workers around the world who use the job as a means of livelihoods.

Data annotators labeling data

An illustration in intense colors in a gloomy mood showing a collage of two mirrored cars, street signs and mathematical symbols

This picture deals with self-driving cars, environment sensing and neural networks. A computer vision system implemented in a car needs to distinguish between important street signs and other non-important elements of the environment like chimneys.

Autonomous Driving

An illustration of three „pixelated“ cupboards next to each other with open drawers, the right one is black

The cupboard as a metaphor for a classification system/algorithm with the drawers representing different categorization classes. Black cupboard: The classification system itself can be a black box, meaning that the mechanisms by which a data item is classified into a specific class are often hidden even to the developers of these systems.

Classification Cupboard

An illustration containing electronical devices that are connected by arm-like structures

This image highlights AI as an extension of human capabilities. Embedded in a variety of devices it can enrich our lives as a tool or partner that adapts to our needs.

Human-AI collaboration

Nine small images with schematic representations of differently shaped neural networks, a human hand making a different gesture is placed behind each network.

This image is made of screenshots of an educational video explaining neural networks. It can be seen as a metaphor for the idea of „explainable AI“ (XAI), a field of research that deals with the „black box“ problem of Machine Learning: Until now we often can’t really say how a neural network comes to its decisions and which are the underlying rules. Explainable AI seeks to find methods for making the AI decision making process more transparent and understandable.

Explainable AI

8 photos of the same tree taken at different times of the year in 2 rows of 4, the last photo is highly pixelated. A pattern of random white blocks run across the image from the left and become aligned on the right.

This image represents the process of machine learning in object recognition. The trees are training data and the white blocks/pattern which become more aligned indicate progress in the machine's objectives. The pixelated tree is a reminder that machines do not see like we humans see.

Trees

A photographic rendering of a succulent plant seen through a refractive glass grid, overlaid with a diagram of a neural network.

This image shows a low angle of a succulent plant against a hazy sky. The scene is refracted in different ways by a fragmented glass grid. This grid is a visual metaphor for the way that artificial intelligence (AI) and machine learning technologies can be used to extract and analyse biological data in innovative ways, and the repetitive and self-similar structure of these systems. A neural network diagram is overlaid, familiarising the viewer with the formal architecture of AI systems.

Nature

A photographic rendering of a smiling face emoji seen through a refractive glass grid, overlaid with a diagram of a neural network.

This image shows a 3D rendering of a smiling face emoji against a black background. The scene is refracted in different ways by a fragmented glass grid. This grid is a visual metaphor for the way that artificial intelligence (AI) and machine learning technologies can be used to extract and analyse social data in complex ways, and the repetitive and self-similar structure of these systems. A neural network diagram is overlaid, familiarising the viewer with the formal architecture of AI systems.

Social Media

A photographic rendering of a simulated middle-aged white woman against a black background, seen through a refractive glass grid and overlaid with a distorted diagram of a neural network.

This image shows a portrait of a simulated middle-aged white woman against a black background. The scene is refracted in different ways by a fragmented glass grid. This grid is a visual metaphor for the way that artificial intelligence (AI) and machine learning technologies can be used to simulate and reflect the human experience in unexpected ways. A distorted neural network diagram is overlaid, familiarising the viewer with the formal architecture of AI systems.

Virtual Human

A banana on a table and an image of a banana on a laptop on the same table. Each of the two bananas has a white frame around it with the word ‚Banana‘ sticked on top of it

Bounding boxes are commonly used in AI research to signify where a computer vision algorithm has detected an object in an image. Here the artist has played with this aesthetic: The bounding boxes are 3D-printed frames positioned in the physical environment around objects. In this case there is a physical banana and a photo of a banana on a screen. The object detection doesn’t make a difference between these two.

Ceci n'est pas une banane

A banana, a plant and a flask on a monochrome surface, each one surrounded by a thin white frame with letters attached that spell the name of the objects

Bounding boxes are commonly used in AI research to signify where a computer vision algorithm has detected an object in an image. Here the artist has played with this aesthetic: The bounding boxes are 3D-printed frames positioned in the physical environment around objects. Sometimes the objects stick out of their frame.

Banana / Plant / Flask

A window of three images. On the right is a photo of a big tree in a green field in a field of grass and a bright blue sky. The two on the left are simplifications created based on a decision tree algorithm. The work illustrates a popular type of machine learning model: the decision tree. Decision trees work by splitting the population into ever smaller segments. I try to give people an intuitive understanding of the algorithm. I also want to show that models are simplifications of reality, but can still be useful, or in this case visually pleasing. To create this I trained a model to predict pixel colour values, based on an original photograph of a tree.

The work illustrates a popular type of machine learning model: the decision tree. Decision trees work by splitting the population into ever smaller segments. I try to give people an intuitive understanding of the algorithm. I also want to show that models are simplifications of reality, but can still be useful, or in this case visually pleasing. To create this I trained a model to predict pixel colour values, based on an original photograph.

Decision Tree

Decision Tree reversed

A window of three images. On the right is a photo of a man in a dark t-shirt and light trousers walking in a narrow street. The curving street is lined with brightly coloured terraced houses facing straight onto the pavement, with green trees in large pots on the other side. The images left and in the middle consist of multiple colored semi transparent polygons, made from the key colours of the mans clothes, the houses and the plants. These polygons were randomly initiated, then randomly mutated. However, only if the mutation made the image look more like the original it was kept, otherwise the image was reverted to the previous step. The image on the right is the original, the image on the left is an early stage of the evolution process, the image on the right is a late stage of the evolution process.

The work illustrates a popular type of algorithm called a genetic evolution. The image consists of multiple colored semi transparent polygons. These polygons were randomly initiated, then randomly mutated. However, only if the mutation made the image look more like the original it was kept, otherwise the image was reverted to the previous step. The image on the right is the original, the image on the left is an early stage of the evolution process, the image on the right is a late stage of the evolution process. Inspired by Roger Johansson's work on genetic programming evolutino of mona lisa.

Man reversed

A yellow window with three images. On the right is an under water photo of a single bright blue and yellow tropical fish. The two on the left are simplifications created based on a decsion tree algorithm. The work illustrates a popular type of machine learning model: the decision tree. Decision trees work by splitting the population into ever smaller segments. I try to give people an intuitive understanding of the algorithm. I also want to show that models are simplifications of reality, but can still be useful, or in this case visually pleasing.

Fish reversed

Fish

A grey window with three images. On the right is a zoomed in photo of a red sports car with a white background. The two on the left are simplifications created based on a decsion tree algorithm. The work illustrates a popular type of machine learning model: the decision tree. Decision trees work by splitting the population into ever smaller segments. I try to give people an intuitive understanding of the algorithm. I also want to show that models are simplifications of reality, but can still be useful, or in this case visually pleasing.

Images of AI library

AI Architecture - Anton Grabolle

Mirror D - Comuzi

Strawberries and milk - Catherine Breslin & Rens Dimmendaal

Safety Precautions - Yasmin Dwiputri & Data Hazards Project

Medicines (flipped) - Rens Dimmendaal & Banjong Raksaphakdee

Little data houses (square) - Joahna Kuiper

People on phones (portrait) - Jamillah Knowles & Reset.Tech Australia

Medicines - Rens Dimmendaal & Banjong Raksaphakdee

AI Lands - XK Studio & Google DeepMind

AI silicon clouds collage - Catherine Breslin & Tania Duarte

Chipping Silicon - Catherine Breslin & Team and Adobe Firefly

Ai is... Banner - Rick Payne and team

People and Ivory Tower AI 2 - Jamillah Knowles & We and AI

Generative Image models - Linus Zoll & Google DeepMind

Little data houses - Joahna Kuiper

Social media content - Jamillah Knowles & Reset.Tech Australia

AI large language models - Wes Cockx & Google DeepMind

AI for Biodiversity - Nidia Dias & Google DeepMind

Surveillance View B - Comuzi

Likes (tall) - Comuzi