Artificial Intelligence isn’t a new concept. Today, with the advancement of processors and the advent of graphic processors, AI and its applications have increased considerably around the world. Rapid technology growth and widely available high bandwidth internet has made cloud computing a central part of this AI revolution. The increase of consumer devices manufactured with robust processing power creates a need to bring the AI technology to work compatibly with the device chips. For example, face recognition technology to unlock phones “real-rime” needs to be processed on the Edge (device). This is why the Edge Computing market will continue to grow in the coming years.
AI is quickly advancing into cloud computing, but the hybrid solution provides an easy transition.
A hybrid solution includes the ability for edge processing, and real-time analysis on the cloud,
To further explain, edge processing is a system where the data generated by hardware devices–in this case IP/Remote cameras– is processed and analyzed at the local level. Device internet connection is not necessary to process the data. In most cases, the processing time is in milliseconds because critical safety decisions need to be made based on alarm and monitoring capabilities.
Cloud computing involves data transmission to a central server where the processing is done using high speed CPUs and GPUs. When the (edge) devices which collect the data are connected to the internet and they transmit data either continuously or in bursts to the central servers.A hybrid solution, can be best exemplified by Smart Home Technologies. The in-home smart devices rely on IoT devices to collect and process data from around the environment. The data is then sent to a centralized remote server, where it is processed and stored for future use. Sensitive information which can pose a security risk are processed at edge, and stored in the local devices.
The advantages for the hybrid approach is simple, it eliminates the privacy issue of transmitting millions of data and storing it in the cloud, as well as the bandwidth and latency limitations that reduce data transmission capacity. It is also a more immediate way to transition from the comfort of edge processing. Because edge technology has become essential for many industries, like autonomous cars, robotics, and surveillance systems. Current on market solutions heavily rely on edge technology.
Drivers of Edge Computing and Edge AI
Privacy – As consumers become more conscious about what’s happening with their data, it’s imperative for companies to develop and design products and apps that use Edge storage and processing. This will also enable companies to deliver more AI-enabled personalized features while also letting the use be in control of their data.
Security – There is always a risk of data hacking and leakes with Cloud technologies either during transmission or after storage. To counter the security risk there’s a movement toward multiple layers of encryption and more dynamic encryption mechanisms. With an increasing variety of AI-enabled devices such as speakers, phones, tablets, and robots, edge nodes can determine the right security mechanism for different devices.
Latency – The most obvious reason for data to be processed on the edge is latency. As services are more distributed at both the network level as well as the device level, there’s more latency concerns when sending data across networks and devices.
Load Balancing – To increase application end to end resiliency on increasingly distributed systems, there needs to be multiple endpoints of load balancing. This brings up the idea of the Chrysalis Cloud Edge Proxy to increase resiliency at the device level.
Edge AI Remains Important Today
The list of Edge AI applications is extensive. Examples include: facial recognition, real-time traffic updates to smartphones, semi-autonomous vehicles, and home surveillance.
Security cameras are a prime marker for Edge AI detection processes. Traditional surveillance cameras record videos for hours, then store for retroactive use. However, with Edge AI, (combined with Computer Vision and Machine Learning), algorithms can process videos real-time within the system itself. Subsequently, the security camera enables real-time detection and will process suspicious activities, for immediate, efficient, and less expensive service.
Another is self driving cars. Autonomous vehicles need Edge AI to increase capacity to process data and images, real-time. Detection of traffic signs, pedestrians, other vehicles, and roads direction, improving the levels of security, and ease in transportation are all taken into consideration in the live driving analysis.
As the 5G technology progresses, it means networks of greater speed and extremely low latency for mobile data transmission, making Edge AI more useful, and compelling. As consumers spend more time with mobile and smart devices, more companies and developers understand the importance of deploying Edge technology to provide a more immediate and efficient service. Industries are now equipped with enterprise-level AI-based services and user convenience.
However, this is just to emphasize that the transition to a fully run cloud industry future is possible. There is a false notion that Edge technology will replace the Cloud. But the Edge will not be the ultimate solution for all, as it can be costly and wasteful. Edge systems working in complementary with the Cloud is the way of the short term needs. Big Data will continue to be processed in the Cloud, but user-generated data that belongs only to users can be operated and processed on the Edge today.