Smarter Cities: Will Autonomous AI Surveillance And IoT Now Automate Law Enforcement?
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Picture a CCTV camera installed onto the side of a building or onto a telegraph pole in a busy urban space. The camera has no pre-set configuration or coded rules, it simply observes its environment, studying and classifying patterns of life, continually learning. The camera will detect anomalies in the behavior and movement of people and vehicles and objects, in environmental conditions, without ever having been instructed as to what such an anomaly might look like. Every object will be detected and classified. Metadata will be captured under strict privacy rules. No imagery will be stored or streamed unless it relates to an incident or an anomaly. Unless it’s marked.
Now, connect that camera to others, to local clusters of cameras on one level, to entire networks of cameras on another, and the depth of machine learning is staggering. Sensors share data, they compare results, they train one another. They work as a system, quietly and unobtrusively, to learn and protect their environments. Within the multi-trillion-dollar Smart City market, the ongoing fusion of cloud and edge is progressing us towards this level of distributed intelligence, towards autonomous surveillance, whether we're ready or not.
The Fusion Of Cloud And Edge
Just as the cloud completes its mopping up and analysis of all the data known to humankind, here comes the IoT. This race to the edge will network billions of intelligent devices and automate our world. The IoT "is growing at a breath-taking pace," says Intel. "From 2 billion objects in 2006 to a projected 200 billion by 2020." The IoT underpins the many varied applications of artificial intelligence that Accenture predicts "could double annual economic growth rates [by] 2035," leading to "an economic boost of $14 trillion in additional gross value added." IoT in all its guises will drive the annual growth in data transmission from 25% to 50%. It will also shift processing from the cloud to the edge. There is too much data, it’s too indiscriminate, too centralized, and it takes too long to access. According to Accenture’s 2018 Technology Vision Report, this ‘internet of thinking’ extends intelligence from cloud to edge. “To fully enable real-time intelligence, businesses must shift event-driven analysis and decision processing closer to points of interaction and data generation. Delivering intelligence in the physical world means moving closer to the edge of networks.”
Despite the Cloud Vs Edge debate that has emerged in some quarters, what we will see in practice is a fusion of Cloud and Edge, shaped by the imperatives of AI. Professor Stephen Hawking said of AI that “every aspect of our lives will be transformed. In short, success in creating AI could be the biggest event in the history of our civilization.” Real-time video is fundamental to much of this, whether steering vehicles, fighting battles or smartening cities. And intelligent video analytics means high resolution, which means serious bandwidth and latency. This has been a major driver of the fusion of cloud and edge. "Computing will become an increasingly movable feast," says the Economist. "Processing will occur wherever it is best placed for any given application."
Executed properly, autonomous surveillance necessitates the combination of cloud and edge computing, it requires an end-to-end AI chain that can apply levels of processing flexibly based on need and equipment, as well as an intelligently distributed architecture where captured data and reference datasets can be shared and synchronized in real time.
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