A pipeline going down even 1% of the time means tens of millions of dollars in losses and serious health and safety concerns. And just one refinery can be 1000s of acres in size, so the scale over which inspection must happen is enormous. The availability of data to build models to run these inspections is a key bottleneck. New approaches in the areas of self-supervised learning show promise in solving this problem.
Topics Covered
(2:05) Breaking down Shell's business & key concerns
(6:49) The role of regulation
(8:21) Historical framing / what progress has already been made
(12:48) The forefront of research in CV for inspections
(16:28) Catastrophic forgetting
(23:02) Commercial patterns that may emerge to help companies without in-house data science teams, what do small companies do in this situation?
(26:06) The promise of multi-modal models
(31:12) Key limitations that prevent models from being deployed today
Closing the Loop with Andrew Camel