Dissecting 'Edge AI'
The recent development of artificial intelligence (AI), the introduction of Internet of Things (IoT) devices, and the power of edge computing are harmonizing the power of edge AI to a great extent.
This opens up new opportunities for edge AI that were previously unimaginable. Edge AI has become widely used in hospitals, including pathology identification for radiologists , highway driving , and plant pollination support .
Today, many analysts and companies are discussing and implementing edge computing . In fact, the origins of edge computing date back to the 1990s, when content delivery networks were created to serve web or video content from edge servers located close to users.
Business functions that are now handled by virtually every enterprise can benefit from the adoption of edge AI. In fact, edge applications are driving the next generation of AI to improve our lives at home, at work, at school and in the transportation system.
So, let's dive deeper into what edge AI is, its benefits, how it works, use cases, and the relationship between edge computing and cloud computing.
What is Edge AI ?
Edge AI refers to building AI applications on devices across the physical world. The reason why the name “Edge AI” was given is because AI computation is not performed at the center of cloud computing facilities or private data centers, but around users at the edge (edge) of the network close to where the data is located.
Because the Internet spans a global sphere, the edge of a network is virtually anywhere. It could be a retail store, a factory, a hospital, or devices like traffic lights, automatic machines, and telephones all around us.
Why Edge AI is Needed Now
Organizations in all industries are looking to expand automation to improve processes, efficiency and safety.
This requires computer programs to recognize patterns and execute tasks repeatedly and safely. But in today's unstructured world, humans have to deal with infinite situations that cannot be described by programs and rules alone.
The advancement of edge AI has ushered in an era where machines and devices can operate anywhere through human cognitive function 'intelligence'. AI-powered smart applications learn to perform similar tasks in other situations that are very realistic.
Efficient deployment of AI models at the edge is driven by three recent innovations:
Maturity of Neural Networks: Neural networks and their associated AI infrastructure have finally advanced to the level of generalized machine learning. Organizations are learning how to successfully train AI models and deploy them to the edge.
Advances in computing infrastructure : Running AI at the edge requires powerful distributed computing power. Recent advances in highly parallel GPUs have been tuned to run neural networks.
Introduction of IoT devices : With the widespread use of IoT, big data is exploding. The sudden availability of data collection from every aspect of a business—industrial sensors, smart cameras, and robots—has created data and devices that now require AI models to be placed at the edge. Moreover, 5G is bringing faster, more reliable and more secure connectivity to the IoT.
Why Put AI at the Edge and the Benefits of Edge AI
Because AI algorithms can understand language, sight, sound, smell, temperature, face, and other unstructured information in analog form, it is especially useful where end users have real problems to solve. However, deploying AI applications in a centralized cloud or enterprise data center is impractical or impossible due to latency, bandwidth, and privacy concerns.
Edge AI benefits include:
Intelligence : AI applications are more powerful and flexible than traditional applications that can only respond to inputs expected by programmers. AI neural networks are not trained to answer specific questions, but rather to answer specific types of questions, even if the questions themselves are new. Without AI capabilities, applications would not be able to handle the infinite variety of inputs such as text, spoken language, and video.
Useful information that can be provided in real time : Edge technology can respond to user needs in real time because it analyzes data close to the user rather than in the distant cloud, which can be delayed by long-distance communication.
Reduced Costs : By performing data processing at the edge, applications require less internet bandwidth, significantly reducing networking costs.
Enhanced Privacy : AI, which can analyze real-life information without exposing it to others, enhances the privacy of those who need to provide appearance, voice, medical images, and other personal information for information analysis. Edge AI stores the data locally and only uploads the analysis and derived information to the cloud, further
enhancing privacy. :Even if some information is uploaded for training purposes, it can be anonymized to protect your identity. This makes edge AI simple when it comes to data compliance.
High Availability : Decentralization and offline capabilities make edge AI more robust as data processing does not require internet access. This increases the availability and reliability of mission-critical, industrial-grade AI applications.
Continuous improvement : The more the AI model learns from the data, the more accurate it becomes. When an edge AI application encounters data that it cannot process accurately or with confidence, it typically uploads it so the AI can retrain and learn. Therefore, the longer the model is produced at the edge, the higher the accuracy of the model.
How edge AI technology works
If machines are to see and detect objects, drive a car, understand or speak horses, walk, or mimic human technology, they will need to functionally replicate human intelligence.
AI uses data structures called deep neural networks (DNNs) to replicate human cognitive abilities. These deep neural networks are trained to answer specific types of questions by showing many examples of those types of questions along with their correct answers.
This training process, known as “ deep learning ,” is often run in data centers or in the cloud because the amount of data required to train an accurate model is vast and data science must collaborate to build the model. After training, the model becomes a “inference engine” that can answer real-world questions.
In edge AI deployments, inference engines operate on computers or devices in remote locations such as factories, hospitals, cars, satellites, and homes. When an AI runs into a problem, that problematic data is usually uploaded to the cloud to further train the original AI model. It will at some point replace the inference engine at the edge. Edge AI models get smarter once deployed, thanks to this feedback loop, which plays an important role in improving model performance.
Edge AI Use Cases
AI is the most powerful technology of our time. Now AI is revolutionizing the world's largest industries.
In manufacturing, healthcare, financial services, transportation and energy, edge AI is driving new business outcomes. An example is:
Intelligent forecasting in the energy sector : In an industry as important as energy, where uneven supply can threaten people's health and welfare, intelligent forecasting is very important. Edge AI models combine historical data, weather patterns, grid conditions, and other information to help create complex simulations that can provide customers with information to more efficiently create, distribute and manage energy resources.
Predictive maintenance in the manufacturing sector : Sensor data can be used to detect anomalies early and predict when a system will fail. Machine-mounted sensors detect faults and manage alerts when a machine needs repair, enabling early resolution of issues, avoiding costly downtime.
AI -powered devices in healthcare : Edge AI Modern medical devices become AI-powered devices using ultra-low-latency surgical video streaming, reducing surgical operations and providing the information you need right away.
Smart Virtual Assistants in Retail : Retailers are working to improve the digital customer experience by introducing voice ordering to replace text-based search with voice commands. Voice ordering allows shoppers to easily search for items, request product information, and order online using smart speakers or other intelligent mobile devices.
The role of cloud computing in edge computing
AI applications can run in data centers, such as public clouds, or on-site, near users, at the edge of the network. Cloud computing and edge computing each offer advantages that can be combined when building edge AI.
The cloud offers benefits related to infrastructure cost, scalability, high utilization, resilience to server failures, and collaboration. Edge computing reduces response times and bandwidth costs, and provides resilience to network failures.
There are several ways cloud computing is helping edge AI deployments.
The cloud can run the model during the training period.
The cloud retrains the model with the data at the edge, so the model continues to run.
The cloud can run AI inference engines that complement models in the field where computational power above response time is important. For example, a voice assistant can respond when his or her name is called, but send the request back to the cloud for analysis.
The cloud provides the latest versions of AI models and applications.
The same edge AI often runs on multiple devices in the field with software installed in the cloud.
The future of edge AI
Thanks to the growing use of neural networks in commercial applications, the proliferation of IoT devices, and advances in parallel computing and 5G, a robust infrastructure for generalized machine learning is now in place. This not only saves money, protects privacy, but also opens up tremendous opportunities for businesses to use AI in their workplaces and act on the information they receive in real time.
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