Edge Intelligence, commonly abbreviated as EI, is an advanced technology subset that integrates artificial intelligence (AI), edge computing, and Internet of Things (IoT) to facilitate real-time informed decision-making at the device or 'edge' location. In this dynamic, data processing and decision-making occur near the data source, significantly improving speed, reliability, and security while reducing the data load transmitted to central data centers.

• Real-Time Analysis: Edge Intelligence grants the ability to analyze data in real-time due to its closer proximity to the data source. This facilitates timely, informed decisions, empowering smart systems to adapt faster to evolving circumstances.

• Increased Security: With data processing at the edge, there’s a significant reduction in the volume of data transmitted, reducing the exposure to potential cyber threats noticeably.

• Reduced Latency: An inherent characteristic of Edge Intelligence is minimized latency, owing to the closer proximity between data generation, processing, and application. For applications where time is of the essence, this proves critically advantageous.

• Superior Scalability: The decentralized nature of edge computing underpins Edge Intelligence's capacity to scale easily, suiting the evolving needs of rapidly expanding IoT networks.

• Enhanced Privacy: Processing data close to its source implies less personal or sensitive data is shared; thus, Edge Intelligence contributes positively to data privacy.

Various sectors utilize Edge Intelligence, exploiting its quick availability, cost efficiency, security, scalability, and potential reliability derived from its testing in an assortment of environments.

Successful Edge Intelligence implementation necessitates thorough strategic planning and evaluation of organizational requirements. Considerations include identifying pertinent edge applications, discerning the critical data elements to be processed at the edge, determining the appropriate technology and tools, and the selection of compatible edge devices. Furthermore, it will require evaluating potential vendors to establish their capability to provide reliable support services for edge computing, in addition to ensuring data security and privacy.

Settings that would dramatically benefit from Edge Intelligence include telemedicine for real-time remote diagnosis, transport systems and autonomous vehicles, hazardous working conditions that need real-time surveillance, and sectors reliant on predictive maintenance like manufacturing. Considering Edge Intelligence essentially symbolizes a shift from a datacenter-focused model to a more distributed one, a well-structured strategic plan complemented by meticulous execution is instrumental for successful implementation and maximizing benefits.

Edge Intelligence comes with a series of inherent advantages:

• Fast Decision-Making: The capacity to process data instantly at the device location facilitates quick decision-making—a boon for real-time applications.

• Heightened Security: With less data transmitted, the likelihood of data breaches declines, making edge intelligence a safer option.

• Reduced Latency: Edge Intelligence enables real-time data processing and analytical capabilities, reducing latency considerably and thereby beneficial for time-critical applications.

• Economically Feasible: Edge Intelligence decreases the immense burden of data transmission and central storage on cloud services. It reduces the financial commitments related to data transfer and storage, making it economically viable over time.

• Improved Privacy: With less data sent and saved, personal and sensitive information remains protected, leading to enhanced privacy.

Despite its merits, a few critical disadvantages to be considered are as follows:

• Efficiency Concerns: While capable of real-time data processing, Edge Intelligence devices may not be able to perform complex computations due to their resource restrictions.

• Device Security: While data transmission risks are reduced, individual edge devices might be susceptible to physical security threats; these need thorough attention.

• Infrastructure Challenges: Implementing Edge Intelligence requires considerable modifications to existing technology infrastructure.

• Cost implications: The potentially high costs establishing an edge-computing network might deter organizations with budget constraints.

• Scalability Issues: Managing multiple edge devices effectively could pose a challenge as the network size expands.

• Dependence on Network Infrastructure: Edge Intelligence's success depends enormously on the quality of network infrastructure. Any network failures may significantly impact performance.