While Amazon Go stores have led the trend for autonomous shopping environments, the use of computer-vision enabled self-service kiosks for grab-and-go checkout is growing rapidly overall.
This includes a trend for new “experiential” shopping centers, for which customers expect to give up some privacy when they walk in the door in exchange for a better experience.
Due to privacy concerns, the initial focus will be primarily around assessing shopper demographics (e.g., age, gender) and location but increasingly we’ll see personalized shopping experiences based on individual identity with proper opt-in (often triggered through customer loyalty programs). The ability to apply AI to “see” events in the physical world enables immense opportunity for innovation in areas such as object recognition, safety and security, quality control, predictive maintenance and compliance monitoring.Ĭonsidering retail - computer vision solutions will usher in a new wave of personalized services in brick and mortar stores that provide associates with real-time insights on current customers in addition to better informing longer term marketing decisions. Hands down, computer vision is the killer app for edge AI today due to the bandwidth associated with streaming video. In a perfect world all processing would be centralized, however this breaks down in practice and the need for AI (and ML) at the edge will only continue to grow with the explosion of devices and data. What do you see as the most promising use cases for edge AI?Īs highlighted in Part One, the reasons for deploying AI at the edge include balancing needs across the vectors of scalability, latency, bandwidth, autonomy, security and privacy.
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