What does AI look like? We’re working with artists and animators to break existing stereotypes — and create a more inclusive image of AI.
Expanding our vision of what AI looks like
Streams of code. Glowing blue brains. White robots, and men in suits.
If you search online for AI, those are the kind of misleading representations you’ll find — in news stories, advertising, and personal blogs.
These stereotypes can negatively impact public perceptions of AI technologies by perpetuating long-held biases. They also often exclude global perspectives, and this lack of diversity can further amplify social inequalities.
Through our Visualising AI program, we commission artists from around the world to create more diverse and accessible representations of AI. These images are inspired by conversations with our scientists, engineers, and ethicists.
Diversifying the way we visualise emerging technologies is the first step to expanding the wider public’s vision of what AI can look like today – and tomorrow.
Image models by Linus Zoll
Large image models, or large visual models, can recognise and categorise images by learning from datasets of photos and images. These models can also be used to create generative models that can create images from text, for example.
Creative collaboration by XK Studio
Creative collaboration through tools like generative AI is opening up new opportunities for human-AI collaboration. Many tools offer new points of view, speed up processes and lead to new territories across mediums from text and audio to graphic design.
Changing what AI looks like to the world
Since launching Visualising AI, we have commissioned 13 artists to create more than 100 artworks, gaining over 125 million views, and 1 million downloads. Our imagery has been used by media outlets, research and civil society organizations.
All artworks are free to download and use.
Explore new ways of visualising AI:
The artists’ interpretation of AI
The artists we work with have complete creative freedom to explore unconventional, even challenging interpretations of AI.
Large language models by Wes Cockx
Language models can recognise and understand text by learning from massive datasets. Large language models develop more advanced capabilities with more data and generate text.
Digital assistants by Martina Stiftinger
Digital assistants, or assistive technologies, describe tools that can enhance how people work. For example, some can be generative AI tools to help brainstorm ideas or organise information to improve productivity.
Visualising emerging technologies
The artworks below illustrate key themes in AI connected to new research, technologies or real-world impact.
Each piece offers a new route into understanding a complex subject – from artificial general intelligence (AGI) and robotics to sustainability and generative AI.
AI and Society by Novoto Studio
AI and society are transforming together. Research is exploring the impact of AI on individuals and society and how they can harness the benefits through AI tools and mitigate risks through equitable development.
AGI by Domhnall Malone
Artificial General Intelligence (AGI) describes AI that would be more generally capable in human-like ways. Many AI systems are good at specific tasks, but more general AI could be a transformative tool.
Biodiversity by Nidia Dias
Biodiversity describes the breadth of life on Earth. Using AI, researchers can better understand, track and ultimately, find ways to protect plants and animals to ecosystems.
Chip Design by Champ Panupong Techawongthawon
Chip design is at the core of our phones, computers and digital lives. Producing new computer chips can take years of work, but AI-based approaches can speed up the design of more powerful and efficient circuits.
Data Labelling by Ariel Lu
Data labelling makes data usable to train AI, but the human labour behind it is often undervalued. Ethics research sheds light on the human involvement in AI and the importance of ethical data labour.
Digital Biology by Khyati Trehan
Digital biology, or computational biology, is the use of data and AI to study life. This ranges from simulating biological systems to harnessing AI to uncover patterns in the natural world.
Neuroscience by Rose Pilkington
Neuroscience and AI have created a virtuous cycle in research. Early work on neural networks were inspired by psychology and neuroscience, and similarly approaches in AI can help teach us about ways in which humans may think.
Robotics by Wes Cockx
Robotics and more broadly embodied AI, describe AI that can take a physical form in the real or simulated world. By combining AI systems with an understanding of physical dynamics and different types of agents, these technologies can be used more easily in the real world.
Video Compression by Vincent Schwenk
Video compression allows billions of people to watch videos around the world. Using AI to compress videos more efficiently, makes streaming faster and saves data and energy on a global scale.
Envisioning the future of AI
AI takes many different forms. We need a more diverse and accessible picture of how AI can impact society.
We’re excited to explore these possibilities through Visualising AI. Together with our artistic partners, we’re hoping to engage more people in shaping what AI looks like in the world.