This blog explains what Edge AI is, why it is important and where you can learn more on the subject.
What is Edge AI?
Edge AI means that AI algorithms are processed locally on a hardware device. The algorithms are using data (sensor data or signals) that are created on the device.
A device using Edge AI does not need to be connected in order to work properly, it can process data and take decisions independently without a connection.
In order to use Edge AI, you need a device comprising a microprocessor and sensors.
Example: An elderly person wearing a watch that can detect falls is a solution based on Edge AI. The Edge AI system use accelerometer data in real time as input to the AI algorithm that will detect when the person is falling. The watch will only connect to the cloud when it has detected a fall. One of the key properties in the example above is to have a long battery life. If the system would rely on processing in the cloud it would need bluetooth connection enabled all the time and the battery would be drained in no time.
Johan Malm, PhD and AI researcher at Imagimob, has written a very good blog about the more technical aspects of Edge AI, Edge AI for techies. Read it here.
Why is Edge AI important?
Edge AI will allow real time operations including data creation, decision and action where milliseconds matter. Real time operations is important for self-driving cars, robots and many other areas.
Reducing power consumption and thus improving battery life is superimportant for wearable devices.
Edge AI will reduce costs for data communication, because less data will be transmitted.
By processing data locally, you can avoid the problem with streaming and storing a lot of data to the cloud that makes you vulnerable from a privacy perspective.
Great articles about Edge AI
Ben Dickson is an experienced software engineer and tech blogger. He contributes regularly to major tech websites such as the Next Web, PCMag.com, VentureBeat, International Business Times UK and The Huffington Post. Read his article explaining why Edge Ai is important.
Nathan Cranford is a writer at RCR Wireless News since 2017. His previous work has been published by a myriad of news outlets, including COEUS Magazine, dailyRx News, Texas Writers Journal and VETTA Magazine. Read his article on how to take AI from the cloud to the edge.
S. Somasegar is the managing director of Madrona Venture Group, a venture capital firm that teams with technology entrepreneurs to nurture ideas from startup to market success. Read his article with his predictions for AI and Machine Learning in 2018.
Imagimob white papers on Edge AI
Imagimob regularly produces white papers on Edge AI. Find them here.