Mastering the Art of Unsupervised Learning: Techniques and Guidance
Unsupervised learning, a cornerstone of artificial intelligence, has emerged as a powerful technique that enables machines to uncover patterns and insights from unlabelled data.
A combination of Edge Computing and Artificial Intelligence, Edge AI is a system that uses Machine learning algorithms to process data generated by a hardware device at a local level.
Edge computing is a distributed computer framework that acts on data at the source, bringing computation and data storage to the network’s edge, where data production and computation take place. AI algorithms process the data captured without the need for connectivity allowing the process to be completed within milliseconds and providing real-time feedback.
Edge AI enhances data security, calculation speed, and business continuity management. The result, it reduces operating costs and improves the functionality of AI-enabled apps. It also provides sophisticated algorithms for Internet of Things (IoT) devices, machine learning, and autonomous deep learning model application independent of cloud services.
Edge computing helps make data storage and computation more accessible to users. This is achieved by running operations on local devices like laptops, Internet of Things (IoT) devices, or dedicated edge servers. Edge processes are not affected by the latency and bandwidth issues that often hamper the performance of cloud-based operations.
Edge AI combines edge computing with artificial intelligence (AI). This involves running AI algorithms on local devices with edge computing capacity. It does not require connectivity and integration between systems, allowing users to process data on the device in real-time.
The majority of AI processes are currently performed in cloud-based centers because they require substantial computing capacity. The downside is that connectivity or network issues can result in downtime or a significant slowdown of the service. Edge AI eliminates these issues by making AI processes an integral part of edge computing devices. This enables users to save time by aggregating data and serving users, without communicating with other physical locations.
Reduced latency: Transfer of data back & forth from the cloud takes time. Edge AI reduces latency by processing data locally (at the device level).
Real-time analytics: Real-time analytics is a major advantage of Edge Computing. It brings high-performance computing capabilities to the edge, where sensors and IoT devices are located.
Higher speeds: Data is processed locally which significantly improves processing speed as compared to cloud computing.
Reduced bandwidth requirement and cost: By processing the data locally on the device itself, it reduces the cost of internet bandwidth and cloud storage.
Improved data security: Edge AI systems perform the majority of data processing locally. This greatly reduces the amount of data that is sent to the cloud and other external locations. This eliminates the exposure of sensitive data to cyber-criminals.
Scalability: Edge AI typically processes large amounts of data. If you have to process video image data from many different sources simultaneously, transferring the data to a cloud service is not required.
Improved reliability: Higher levels of security combined with greater speed produce greater reliability of the system.
Reduced power: Edge AI processes data at the device level so it saves energy costs.
Edge AI can be used to automate the assembly line, and AI to visually inspect products for defects. Inspection of devices/machines is done via AI algorithms instead of human beings performing manual inspections can save time & money.
Improve effectiveness of Industrial Internet of Things (IIoT)
AI algorithms can monitor potential defects and errors in the production chain and enable real-time adjustments to production processes.
Security camera detection processes
Traditional surveillance cameras record images for hours and store and use the data as needed. However, with Edge AI, the algorithmic process runs on the system itself in real-time, allowing the camera to detect and handle suspicious activity in real-time. This ensures more efficient and less expensive service.
Image and video analysis
For example, it can help generate automated responses to audiovisual stimuli in robots. It can also be used for real-time recognition of spaces and scenes.
Provides rapid collection and analysis of data produced by edge-based devices and sensors. This allows manufacturers to execute better control of critical assets and implement predictive maintenance protocols.
Energy (Oil and Gas)
Generally, oil and gas plants are situated in remote locations. The powerful feature of edge computing like real-time analytics with information processing is asset in itself, as it doesn’t require strong connectivity which may not be available on location.
Healthcare (Patient Monitoring)
The applications in the healthcare (patient monitoring) sector provide several distinct advantages when compared to a traditional cloud-based system. An Edge AI application allows the healthcare provider to process all patient monitoring device data locally. It also enables real-time analytics to record patient behaviours and view patient dashboards for full visibility.
The use of Edge AI is expected to grow continuously, especially for enterprise and industry applications. If you are looking into adopting this technology for your operation, AI Consulting Group has the experience and capabilities to help you design, architect, and implement your Edge AI system.