Edge artificial intelligence refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
Simply stated, edge AI, or "AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.
Edge AI is growing in popularity as industries discover new ways to harness its power to optimize workflows, automate business processes and unlock new opportunities for innovation, all while addressing concerns such as latency, security, and cost reduction.
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Thanks to edge AI, localized decision-making eliminates the need to constantly transmit data to a central location and wait for it to facilitate the automation of our business operations. Nevertheless, there is still a need to transmit data to the cloud for the purpose of retraining these AI pipelines and deploying them. Deploying this pattern across numerous locations and a diverse range of applications presents specific challenges such as data gravity, heterogeneity, scale, and resource constraints. Distributed AI can address these challenges that edge AI faces by integrating intelligent data collection, automating the data and AI life cycles, adapting and monitoring spokes, and optimizing data and AI pipelines.
Distributed artificial intelligence (DAI) is responsible for distributing, coordinating, and forecasting task, objective, or decision performance within a multi-agent environment. DAI scales applications to a large number of spokes and enables AI algorithms to autonomously process across multiple systems, domains, and devices on the edge.
Presently, cloud computing and APIs are used to train and deploy machine learning models. Subsequently, edge AI conducts machine learning tasks such as predictive analytics, speech recognition and anomaly detection in close proximity to the user, distinguishing itself from the common cloud services in various ways. Instead of applications being developed and run entirely on the cloud, edge AI systems process and analyze data closer to the point where it was created. Machine Learning algorithms are able to run on the edge and information can be processed right onboard IoT devices, rather than in a private data center or in a cloud computing facility.
Edge AI presents itself as a better option whenever real-time prediction and data processing are required. Consider the most recent advancements in self-driving vehicle technology. To ensure the secure navigation of these cars and their avoidance of potential dangers, they must rapidly detect and respond to a range of factors such as traffic signals, erratic drivers, lane changes, pedestrians, curbs, and numerous other variables. Edge AI’s ability to locally process this information within the vehicle mitigates the potential risk of connectivity problems that might arise from sending data to a remote server through cloud-based AI. In scenarios of this nature, where quick data responses could determine life or death outcomes, the vehicle's ability to react swiftly is absolutely crucial.
Conversely, cloud AI refers to the deployment of AI algorithms and models on cloud servers. This method offers increased data storage and processing power capabilities, facilitating the training and deployment of more advanced AI models.
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Cloud AI can provide greater computational capabilities and storage capacity compared to edge AI, facilitating the training and deployment of more intricate and advanced AI models. Edge AI comes with a boundary on the processing capacity due to the limitation of the device’s size.
Latency directly affects productivity, collaboration, application performance and user experience. The higher the latency (and the slower response times) the more these areas suffer. Edge AI provides reduced latency by processing data directly on the device, whereas cloud AI involves sending data to distant servers, leading to increased latency.
Bandwidth refers to the public data transfer of inbound and outbound network traffic around the globe. Edge AI calls for lower bandwidth due to local data processing on the device, whereas cloud AI involves data transmission to distant servers, demanding higher network bandwidth.
Edge architecture offers enhanced privacy by processing sensitive data directly on the device, whereas cloud AI entails transmitting data to external servers, potentially exposing sensitive information to third-party servers.
In 2022, the global edge AI market was valued at USD 14,787.5 million and is expected to grow to USD 66.47 million by the year 2023, according to a report conducted by Grand View Research, Inc (link resides outside ibm.com). This rapid expansion of edge computing is driven by the rise in demand for IoT-based edge computing services, alongside edge AI’s other inherent advantages. The primary benefits of edge AI include:
Through complete on-device processing, users can experience rapid response intervals without any delays caused by the need for information to travel back from a distant server.
As edge AI processes data on a local level, it minimizes the amount of data transmitted over the internet, leading to the preservation of internet bandwidth. When less bandwidth is used, the data connection can handle a larger volume of simultaneous data transmission and reception.
Users can perform real-time data processing on devices without the need for system connectivity and integration, enabling them to save time by consolidating data without needing to communicate with other physical locations. However, edge AI might encounter limitations in managing the extensive volume and diversity of data demanded by certain AI applications and may need to be integrated with cloud computing to harness its resources and capacities.
Privacy is increased because data is not transferred over to another network, where it may be vulnerable to cyberattacks. Through processing information locally on the device, edge AI reduces the risk for the mishandling of data. In industries subject to data sovereignty regulations, edge AI can aid in maintaining compliance by locally processing and storing data within designated jurisdictions. On the other hand, any centralized database has the potential to become an enticing target for potential attackers, meaning edge AI is not completely immune to security risks.
Edge AI expands systems using cloud-based platforms and inherent edge capabilities on original equipment manufacturer (OEM) technologies, encompassing both software and hardware. These OEM companies have begun to integrate native edge capabilities into their equipment, thereby simplifying the process of scaling the system. This expansion also enables local networks to maintain functionality even in situations where nodes upstream or downstream experience downtime.
Expenses associated with AI services hosted on the cloud can be high. Edge AI offers the option of utilizing costly cloud resources as a repository for post-processing data accumulation, intended for subsequent analysis rather than immediate field operations. This reduces the workloads of cloud computers and networks. The utilization of CPU, GPU and memory experiences a large reduction as their workloads are distributed among edge devices, distinguishing edge AI as the more cost-effective option between the two.
When cloud computing handles all the computations for a service, the centralized location bears a significant workload. Networks endure high traffic to transmit data to the central source. As machines execute tasks, the networks become active once more, transmitting data back to the user. Edge devices remove this continuous back-and-forth data transfer. As a result, both networks and machines experience reduced stress when they are relieved from the burden of handling every aspect.
Moreover, the autonomous traits of edge AI eliminate the need for continuous supervision by data scientists. Although human interpretation will consistently play a pivotal role in determining the ultimate value of data and the outcomes that it yields, edge AI platforms assume some of this responsibility, ultimately leading to cost savings for businesses.
Edge AI utilizes neural networks and deep learning to train models to accurately recognize, classify, and describe objects within the given data. This training process usually utilizes a centralized data center or the cloud to process the substantial volume of data necessary for model training.
After deployment, edge AI models progressively improve over time. Should the AI encounter an issue, the problematic data is often transferred to the cloud for additional training of the initial AI model, which eventually replaces the inference engine at the edge. This feedback loop significantly contributes to enhancing model performance.
Presently, common examples of edge AI include smartphones, wearable health-monitoring accessories (e.g., smart watches), real-time traffic updates on autonomous vehicles, connected devices and smart appliances. Various industries are also increasingly implementing edge AI applications in order to cut down costs, automate processes, improve decision-making, and optimize operations.
Healthcare providers are undergoing a substantial transformation through the practical implementation of edge AI and the introduction of state-of-the-art devices. When combined with further edge advancements, this technology is poised to build smarter healthcare systems, all the while safeguarding patient privacy and lowering response times.
Utilizing AI models embedded locally, wearable health monitors evaluate metrics such as heart rate, blood pressure, glucose levels, and respiration. Wearable edge AI devices can also detect when a patient falls suddenly and alert caretakers, a feature already included in common smartwatches on the market.
Through equipping emergency vehicles with swift data processing capabilities, paramedics can extract insights from health monitoring devices and consult with physicians to determine effective patient stabilization strategies. Simultaneously, emergency room staff can prepare to address patients' unique care requirements. Integrating edge AI in such circumstances will help facilitate the real-time exchange of critical health information.
Worldwide manufacturers have initiated the integration of edge AI technology to revolutionize their manufacturing operations, thereby enhancing efficiency and productivity in the process.
Sensor data can be leveraged to proactively identify anomalies and forecast machine failures, also known as predictive maintenance. Equipment sensors locate imperfections and promptly notify management about crucial repairs, enabling timely resolution and preventing operational downtime.
Edge AI can also be applied to other areas of need in this industry, such as quality control, worker safety, yield optimization, supply chain analytics and floor optimization.
It’s no secret that businesses have experienced a massive trend with the rise in popularity of eCommerce and online shopping. Traditional brick-and-mortar retail stores have been forced to innovate in order to create a seamless shopping experience and engage customers. With this shift, new technologies have emerged, such as “pick-and-go” stores, smart shopping carts with sensors, and smart check-outs. These solutions utilize edge AI technology in order to elevate and expedite the customers’ conventional in-store experience.
The contemporary landscape is saturated with "smart" devices such as doorbells, thermostats, refrigerators, entertainment systems and controlled lightbulbs. These smart homes contain device ecosystems that utilize edge AI to enhance the quality of residents' lives. Whether a resident needs to identify someone at their door or control their house temperature through their device, edge technology can rapidly process data on-site without the need to transmit information to a centralized remote server. This helps maintain the resident's privacy and reduces the risk of unauthorized access to personal data.
Speed is of utmost importance for security video analytics. Numerous computer vision systems lack the proper speed required for real-time analysis, and instead of locally processing the captured images or videos from security cameras, these systems transmit them to a cloud-based machine equipped with high-performance processing capabilities. Without processing the data locally, these cloud-based systems encounter hindrances due to latency issues, characterized by delays in data uploading and processing.
Edge AI’s computer vision applications and object detection capabilities on smart security devices identifies suspicious activity, notifies users, and triggers alarms. These capabilities provide residents with a stronger sense of safety and peace of mind.
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