They can also deliver real-time results to insurers, repairers, and consumers regarding:
And then, of course, there are self-driving cars, which rely on a continuous, uninterrupted stream of data and instructions to be autonomous.
However, it’s important to not only focus on the measured outcomes of this technology: cost, efficiency gains, speed and safety—but it’s critical to focus on how the AI accomplishes its automation of certain tasks, particularly in the automotive space where safety is key.
The human-in-the-loop approach re-frames and prioritises the human-computer interaction, in a design to build smarter systems that incorporate useful, meaningful human interaction.
It is good practice (and a necessity in the automotive industry) for A.I. and machine learning systems to always be designed with a human-in-the-loop component that can intervene when necessary—whether that be flagging something in the claims cycle or overriding a self-driving car.
At Solera, we’re building intelligent systems for humans to enable us to accomplish more in a smarter way. The human augments the technology with their expertise (or repair science, in our case), creating an unbeatable blend of human and technology.
This repair science is where future-proofing comes in. Solera continues to train our solutions as new cars come out with slightly different shapes, slightly different angles. There will inevitably be things no one has seen before—and that’s why we continue to teach the machine.
The “human in the loop” is never simply a checklist item where someone is given minimal oversight of the algorithms. One of the things we pride ourselves on at Solera is our team’s extensive and collective experience in the industry, which provides a true understanding of how the intelligent designs work in the automotive space and helps validate the output.
