Visualising AI—Release 1,2,3

Visualising AI—Release 1,2,3 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.

Bias amplification by Ariel Lu

Role
Project co-lead

Artists

Ariel Lu

Champ Panupong Techawongthawon
Domhnall Malone

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 Visualising AI, 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.

Learning to Learn

Neuroscience by Rose Pilkington

Neural networks by Novoto Studio

Deep learning by Vincent Schwenk

Reinforcement learning by Vincent Schwenk

Neuroscience by Novoto Studio

Applications of AI

Large language models by Wes Cockx

Chip design by Champ Panupong Techawongthawon

AI for biology by Nidia Dias

Digital biology by Khyati Trehan

Fusion by Khyati Trehan

Generative image models by Linus Zoll

Digital assistance by Martina Stiftinger

AI for education by Martina Stiftinger

AI for biodiversity by Nidia Dias

Large models by Wes Cockx

Energy efficiency by Linus Zoll

Large language models by Tim West

Video compression by Vincent Schwenk

Artificial General Intelligence

Creativity and AI by XK Studio

AGI by Nidia Dias

AGI by XK Studio

AGI by Wes Cockx

Safety and responsibility

AI and society by Novoto Studio

Bioethics by Khyati Trehan

Safety by Khyati Trehan

Data labellers by Ariel Lu

Accountability by Champ Panupong Techawongthawon

Bias amplification by Ariel Lu

Brand and identity

More work