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What is Edge AI? How does it work?

The effectiveness of artificial intelligence (AI), the adoption of IoT devices, and the performance of edge computing have all made significant recent advances, unlocking the potential of AI at the edge.

From helping radiologists diagnose illnesses in hospitals, to driving cars on highways, to helping us pollinate plants, edge AI is opening up possibilities we never had before.

Countless analysts and businesses are talking about and deploying edge computing. The technology's origins date back to the 1990s, when content delivery networks were created to deliver web and video content from edge servers close to users.

Almost every business today has a job function that could benefit from AI at the edge. In fact, edge applications are driving a new wave of AI development that will improve our homes, jobs, schools, and transportation.

What is Edge AI?

Edge AI refers to the deployment of AI applications in physical world devices. The technology is called AI at the edge because it performs AI computing at the edge of the network, close to users and data, rather than centralized in cloud computing facilities or private data centers.

Because the Internet spans the globe, the edge of the network can touch anywhere, such as retail stores, factories, hospitals, or devices around us such as traffic lights, autonomous machines, and phones.

AI at the edge: Why adopt the technology now?

Organizations in every industry are seeking to improve processes, efficiency and safety through increased automation.

To help them, computer programs need to be able to recognize patterns and perform tasks repeatedly and safely. But the world we live in is unstructured, and the tasks performed by humans cover countless situations, so they cannot be fully described by programs and rules.

The development of AI technologies at the edge opens up new opportunities for machines and devices. We can use human intelligence to control machines and devices wherever they are located. AI smart applications can learn how to perform similar tasks in different situations, much like real life.

The effectiveness of AI models at the edge stems from three recent innovations:

Maturity of Neural Networks: Neural networks and the associated AI infrastructure have finally evolved to the point where general-purpose machine learning is possible. Organizations are learning how to successfully train AI models and deploy them in production at the edge.

Advances in Computing Infrastructure: Running AI at the edge requires massive distributed computing power. State-of-the-art highly parallel GPUs are already being used to run neural networks.

Adoption of IoT devices: The widespread adoption of IoT has fueled the explosion of big data. With the sudden ability to ingest data from all aspects of the enterprise from industrial sensors, smart cameras, robots, and more, we now have the data and devices needed to deploy AI models at the edge. In addition, 5G is advancing the Internet of Things by providing faster, more stable and more secure connections.

Why deploy AI at the edge? What are the advantages of edge AI?

AI algorithms capable of understanding speech, sights, sounds, smells, temperatures, faces, and other analog forms of unstructured information are especially useful for end users with real-world problems. Given the latency, bandwidth, and privacy concerns, these AI applications would not, or even be possible, to be deployed in centralized clouds or corporate data centers.

Benefits of AI at the edge include:

Intelligent: AI applications are more powerful and flexible than traditional applications, which can only respond to the input predicted by the programmer; AI neural networks can be trained to answer specific types of questions (even if the question itself is a new one). problem), rather than a specific problem. Without AI, it would be impossible for an app to process endless inputs such as text, speech, or video.

Real-time insights: Since edge technologies analyze data locally rather than in a distant cloud, there is no delay due to long-distance communication and the ability to respond to user needs in real time.

Lower costs: By bringing processing power closer to the edge, applications require less Internet bandwidth, significantly reducing network costs.

Enhanced Privacy: AI can analyze real-world information without revealing it, greatly enhancing an individual's privacy, even when analyzing an individual's appearance, voice, medical images, or any other personal information. Edge AI keeps this data locally and only uploads analysis and insights to the cloud, further enhancing privacy. Even if a portion of the data is uploaded for training, the data can be anonymized to protect user identities. By preserving privacy, edge AI can simplify data compliance challenges.

High Availability: Since data can be processed without a network connection, edge AI becomes more powerful with decentralization and offline capabilities, which improves the availability and reliability of critical production-grade AI applications.

Continuous Improvement: The more data you use to train your AI model, the more accurate your AI model will be. When an edge AI application encounters data it can't handle accurately, it typically uploads that data and then uses that data to retrain and learn from it. Therefore, the longer a model has been used in marginal production, the more accurate that model will be.

How does edge AI work?

In order for machines to see, detect objects, drive a car, understand language, speak, walk, or otherwise mimic human skills, they need human intelligence.

AI employs a data structure known as a deep neural network to replicate human cognitive abilities. These neural networks are trained to answer specific types of questions by showing them lots of examples of that type of questions and the correct answers.

Because training an accurate model requires a lot of data and requires data scientists to jointly configure the model, this training process, known as deep learning, is generally run in the data center or cloud. The trained model becomes an inference engine that can answer real-world questions.

In deploying edge AI, the inference engine runs on a computer or device in remote locations such as factories, hospitals, cars, satellites, and homes. When AI encounters a problem, the data causing the problem is typically uploaded to the cloud for further training of the original AI models, which will at some point replace the edge inference engine. This feedback loop plays an important role in improving model performance—as edge AI models are deployed, they become increasingly smarter.

What are the use cases for edge AI?

AI is the most powerful technological force of our time. Now, AI is revolutionizing the world's largest industries.

AI at the edge is driving new business outcomes in manufacturing, healthcare, financial services, transportation, energy, and more, including:

Intelligent Forecasting in the Energy Industry: In critical industries such as energy, where supply disruptions can threaten the health and well-being of the population, intelligent forecasting is critical. Edge AI models help these industries combine historical data, weather patterns, supply network health, and more to create complex simulations that provide customers with more efficient production, delivery, and management of energy resources.

Predictive maintenance in the manufacturing industry: The manufacturing industry uses sensor data to detect anomalies at an early stage and predict when machines will fail. Sensors on equipment can detect defects and alert managers when machines need repair, so problems can be addressed early and costly downtime avoided.

AI instruments in the medical industry: Modern medical instruments at the edge are becoming AI-enabled, with devices using ultra-low-latency surgical video transmission technology to help doctors perform minimally invasive procedures and provide insight into needs.

Smart virtual assistants in the retail industry: Retailers want to introduce voice ordering capabilities, replacing text searches with voice commands, thereby improving the quality of the online shopping experience for customers. With voice ordering, customers can easily search for items, ask for product information and order online using a smart speaker or other smart mobile device.

What role does cloud computing play in edge computing?

AI applications can run in data centers, such as those in public clouds, or at the edge of the network, close to users. By deploying edge AI, you can enjoy the respective advantages of cloud computing and edge computing at the same time.

The cloud offers benefits in terms of infrastructure cost, scalability, high utilization, resilience to server failure, and collaboration. Edge computing can speed up response times, reduce bandwidth costs, and increase resilience to network failures.

Cloud computing can support edge AI deployment in several ways:

The cloud can run the model during model training.

While the model is being retrained using data from the edge, the cloud can also continue to run the model.

When high computing power is more important than response time, the cloud can run AI inference engines to supplement the computing power of on-site models. For example, a voice assistant can respond to its own name, but will upload complex requests to the cloud for analysis.

The cloud can deliver the latest versions of AI models and applications.

With software in the cloud, the same edge AI can often run on multiple devices in the field.

Future Trends of Edge AI

With the maturity of commercial neural networks, the ubiquity of IoT devices, and advances in parallel computing and 5G, we are now able to achieve general-purpose machine learning with a powerful infrastructure. Businesses should seize the opportunity to apply AI to their premises and act on real-time insights while reducing costs and enhancing privacy.

AI at the edge is still in its early stages, and its applications are boundless.

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