AN AI FRAMEWORK BUILT TO POWER PRODUCTS THAT MAKE A CRITICAL DIFFERENCE FOR SERVICE MEMBERS AND FIRST RESPONDERS
Hivemind is an artificial intelligence framework that creates the capabilities that will make a critical difference in the AI era. It enables robotic systems to explore, navigate and clear complex environments with minimal human input. Hivemind is at work today powering Nova, our autonomous aerial robot.
Hivemind enables edge-level artificial intelligence. With Hivemind, robotic systems are able to plan tasks, map the world, recognize objects, decide where to go, and then travel to those places, all without operator input.
In terms of multi-robot systems, the ability to communicate enables the robots to make coordinated decisions of who should go where and when, and to assess the reason why a particular robot is better suited for the task.
For a robotic system, exploration involves the system making decisions of where it should go in order to acquire information and learn about its environment. Multi-robot exploration applies to systems in which multiple robots are working together.
As a robot explores, intelligence starts to manifest through the system’s ongoing assessment of its observations. Specifically, the robot will assess each observation’s informative value and information gain associated with it.
The concept of resiliency is rooted in psychology. When applied to robotic systems, resilient intelligence refers to the ability of the system to cope with unexpected challenges and adapt to overcome them.
Artificial intelligence is a broad field that encompasses many different aspects of what it means to create an intelligent system. Learning is a subset of intelligence. For robotic systems, learning typically refers to identifying relationships.
Machine learning is a subset of AI. Deep learning is a form of machine learning that uses neural networks with many layers that are adept at fitting complex models to large amounts of data.
Deep learning is a specific branch of machine learning that creates models using deep neural networks.
At Shield AI, we engineer robotic systems that are able to adapt their behavior in order to augment, extend, mitigate, and support the user. This enables anyone to engage with our systems, having never worked with them, and perform as if they were experts.
Two things are required to build trust in robotic systems: Human understanding of how the system is expected to operate and reliable system performance adhering to expectation.
Shield AI’s CTO Nathan Michael addresses how the future of AI depends on trust and understanding, particularly the understanding that AI is not magic, it’s mathematics.
ENABLING EVER-IMPROVING PERFORMANCE
With Hivemind, robotic systems learn from their experiences -- in the real world and in simulation -- to become smarter with each use. More experience yields more informed decisions and ever-improving performance.
Here’s how it works:
A Hivemind-powered robotic system will collect data while it is operating.
This data will include what the world around it looks like, the accuracy of its sensors, and the efficacy of its overall operation.
The robotic system will use this data to draw inferences based on its operation.
This allows the robotic system to determine how well it is performing at any given moment, and to make adjustments to optimize performance further.