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Edge AI: The Benefits of Hive Minds Versus the Central Master Mind
Edge computing refers to devices with processors and sensors that are able to process data locally as it is captured. Edge AI means that the local devices use AI algorithms as part of their processing.
Edge AI means that devices need not be centrally connected in order for them to process data and to independently make decisions. Often use cases involve continuous monitoring with broadcasts being sent out only when an anomaly or event triggers a need for additional attention.
For example, Edge AI use cases include:
- Surveillance and Monitoring – Cameras can captured video and phones, process the data and only communicate data when an event triggers a request for additional processing.
- Audio Event Detection – The sound gunshot, breaking glass, or a crying baby, could trigger other systems to take action.
- Health Monitoring – Wearable devices can track information about heart rate and activities alert wearers of potential health problems.
- Autonomous Vehicles, Robots and Drones – Cameras can recognize pedestrians and other vehicles, obstacles, road signs and either make immediate decisions or signal other systems for assistance.
Dan Woods, CEO of Evolved Media, said that “in general, while we think of AI in the cloud as a huge brain, AI at the edge will be a hive mind of many smaller brains working together in self-replicating and self-organizing ways. AI at the edge will behave as we humans do – each learning from our environment to make locally optimal decisions, on the fly, from local data only. “
