Oxford, United Kingdom

University of Oxford

Oxford pairs deep vision research with the field's most-cited work on long-run risk and governance.

AIPolicyRoboticsOfficial site

2 key findings1 timeline milestones2 sources to follow

Key findings

2014
VGGNet — depth with 3×3 convolutions
Karen Simonyan and Andrew Zisserman
Showed that stacking small uniform filters to 16–19 layers beat wider, shallower designs.
Why it matters here · Made architectural depth the default lever, and VGG features are still a standard perceptual loss.
2014
Superintelligence
Nick Bostrom, Future of Humanity Institute
Formalised the alignment and control problem for systems that exceed human capability.
Why it matters here · Directly shaped the safety teams, evaluations and policy commitments that frontier labs in this catalog now publish.

Milestones on the 1943 → today timeline credited to this institution.

Research lab · Continuous

VGGNet and a long line of vision architectures and datasets.

Program · Continuous

Empirical research on AI's labour, information and governance effects.