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The case for better
Edge AI systems

Today's machines are great tools, but they are dependent on vulnerable infrastructure and limited human management attention. 


For hybrid teams to really fulfil their potential, machines must have the ability to perform dynamically and robustly at the very edge, where the danger is greatest.


Our capacity to comprehend the challenges these machines will face stems from the experience of our team members who have encountered similar situations.

Military Humvee

This understanding has allowed us to frame difficult questions, such as:


How do we build remote sensors that warn only of people who pose a threat to themselves or others and generate such a low rate of false positives that the system remains credible over time?


How do we ensure that our sensors generate warnings only when key vehicles belonging to armed military are detected and not when normal traffic passes, ensuring low rate of false negatives?


How do we enable a small UAS to navigate accurately for the entirety of a multi-hour mission, without once referring to any external systems or infrastructure?


How do we enable a small UAS to identify and react to a changing situation on the ground ensuring that it works to find and then report critical information immediately, rather than simply continuing on its way?


We've developed edge AI systems to address these critical questions, ensuring people's safety by keeping them away from harm.


Instead of acting solely as sensors, people can utilise intelligent sensor arrays, freeing them to focus on understanding and solving complex problems.

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