We carry out meaningful applied research: transforming breakthrough discoveries in deep supervised and semi-supervised learning into products that help people recover from accidents and disasters more quickly.
With the right approach and training, this can be carried out by an AI like a human, at scale.
To get there we are addressing complex processes that, today, experts with decades of experience take hours to complete.
Our research focuses on creating representations that capture the subtlety of real damaged vehicles and properties.
Building robust training methods to deal with data that is noisy, heterogeneous and of varying dimensionality.
Refining domain adaptation techniques to handle differences in geographies and repair standards.
Combining visual output with fine-grained metadata to utilise key non-visual information.
Conditioning learning on structured expert knowledge to create models that are accountable and interpretable.
We are building AI-first products that will transform traditional industries by streamlining outdated processes and delivering unprecedented customer value.
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