Recent advances in AI efficacy, IoT device adoption, and edge computing power have all combined to unlock the power of edge AI.
This has opened up previously unimaginable possibilities for edge AI, such as assisting radiologists in identifying pathologies in the hospital, driving cars down the freeway, and pollinating plants.
Edge computing, which dates back to the 1990s when content delivery networks were created to serve web and video content from edge servers deployed close to users, is being discussed and implemented by a slew of analysts and businesses.
Almost every business today has job functions that could benefit from edge AI adoption. Edge applications, in fact, are driving the next wave of AI in ways that improve our lives at home, at work, in school, and in public transportation.
Learn about edge AI, its benefits, and how it works, as well as examples of edge AI applications and the relationship between edge computing and cloud computing.
What Is Edge Artificial Intelligence (AI)?
The deployment of AI applications in devices all over the physical world is known as edge AI. The AI computation is done near the user at the network’s edge, close to where the data is stored, rather than centrally in a cloud computing facility or private data center, hence the name “edge AI.”
Because the internet has a global reach, the network’s edge can refer to any location. It could be a store, a factory, a hospital, or everyday devices like traffic lights, self-driving cars, and cell phones.
Why Now, Edge AI?
Automation is being sought by businesses across the board in order to improve processes, efficiency, and safety.
Computer programs must be able to recognize patterns and perform tasks repeatedly and safely in order to assist them. However, the world is unstructured, and the range of tasks performed by humans encompasses an infinite number of circumstances that are impossible to fully describe in programs and rules.
Advances in edge AI have made it possible for machines and devices to operate with the “intelligence” of human cognition wherever they are. Smart AI-enabled applications learn to perform similar tasks in a variety of situations, much like humans do.
Three recent innovations demonstrate the efficacy of deploying AI models at the edge.
Maturity of Neural Networks
Neural networks and related AI infrastructure have finally progressed to the point where generalized machine learning is possible. Organizations are figuring out how to train AI models and put them into production at the edge.
Advances in Compute Infrastructure
To run AI at the edge, you’ll need a lot of distributed computing power. Recently, high-performance GPUs have been adapted to run neural networks.
Adoption of Internet of Things Devices
The proliferation of big data has been fueled by the widespread adoption of the Internet of Things. We now have the data and devices needed to deploy AI models at the edge, thanks to the sudden ability to collect data in every aspect of a business — from industrial sensors, smart cameras, robots, and more. Furthermore, 5G gives IoT a boost by providing faster, more stable, and secure connectivity.
Why Use AI at the Edge? What Are Edge AI’s Advantages?
AI algorithms are particularly useful in places occupied by end-users with real-world problems because they can understand language, sights, sounds, smells, temperature, faces, and other analog forms of unstructured information. Due to issues with latency, bandwidth, and privacy, these AI applications would be impractical or even impossible to deploy in a centralized cloud or enterprise data center.
The following are some of the advantages of edge AI.
Artificial intelligence (AI) applications are more powerful and flexible than traditional applications, which can only respond to inputs that the programmer has pre-programmed. An AI neural network, on the other hand, is trained to answer a specific type of question rather than a specific question, even if the question itself is new. Applications could not process infinitely diverse inputs such as texts, spoken words, or video without AI.
Edge technology responds to users’ needs in real-time because it analyzes data locally rather than in a faraway cloud delayed by long-distance communications.
By bringing processing power closer to the edge, applications require less internet bandwidth, lowering networking costs significantly.
AI can analyze real-world data without ever exposing it to a human, greatly enhancing privacy for anyone whose face, voice, medical image, or other personal data needs to be analyzed. Edge AI improves privacy even more by storing data locally and only uploading analysis and insights to the cloud. Even if some of the data is uploaded for training purposes, user identities can be protected by anonymizing them. Edge AI simplifies the challenges of data regulatory compliance by preserving privacy.
Edge AI is more robust due to decentralization and offline capabilities, as data processing does not require internet access. As a result, mission-critical, production-grade AI applications have higher availability and reliability.
As AI models learn more data, they become more accurate. When an edge AI application comes across data it can’t process accurately or confidently, it usually uploads it so the AI can retrain and learn from it. As a result, the longer a model is produced at the edge, the more accurate it will be.
How Does Edge AI Technology Work?
Machines must functionally replicate human intelligence in order to see, detect objects, drive cars, understand speech, speak, walk, and perform other human-like tasks.
To replicate human cognition, AI uses a data structure called a deep neural network. These DNNs are taught to respond to specific types of questions by being shown numerous examples of those questions along with correct answers.
Due to the large amount of data required to train an accurate model and the need for data scientists to collaborate on configuring the model, this training process, known as “deep learning,” is frequently performed in a data center or the cloud. Following training, the model becomes an “inference engine” capable of answering real-world questions.
The inference engine in edge AI deployments runs on a computer or device in remote locations such as factories, hospitals, cars, satellites, and homes. When the AI encounters a problem, it is common practice to upload the problematic data to the cloud for further training of the original AI model, which eventually replaces the inference engine at the edge. This feedback loop is critical for improving model performance; once deployed, edge AI models only get smarter and smarter.
What Are Some Use Cases for Edge AI?
Artificial intelligence (AI) is the most powerful technological force of our time. We’ve arrived at a point in history when artificial intelligence is revolutionizing the world’s most important industries.
Edge AI is driving new business outcomes in every sector, including manufacturing, healthcare, financial services, transportation, energy, and more.
Intelligent Forecasting in Energy
Intelligent forecasting is critical in critical industries like energy, where a disruption in supply could jeopardize the health and welfare of the general public. Edge AI models assist in the creation of complex simulations that inform more efficient generation, distribution, and management of energy resources to customers by combining historical data, weather patterns, grid health, and other information.
Predictive Maintenance and Manufacturing
Sensor data can be used to detect anomalies early and predict when a machine will fail in the manufacturing industry. Sensors on equipment scan for flaws and notify management if a machine requires repair, allowing the problem to be addressed quickly and avoid costly downtime.
Modern medical instruments at the edge are becoming AI-enabled, with devices that use ultra-low-latency surgical video streaming to allow for minimally invasive surgeries and on-demand insights.
Retailers are using smart virtual assistants to improve the digital customer experience by introducing voice ordering, which will replace text-based searches with voice commands. Shoppers can use smart speakers or other intelligent mobile devices to search for items, ask for product information, and place online orders using voice ordering.
The Future of Edge AI
There is now a robust infrastructure for generalized machine learning thanks to the commercial maturation of neural networks, the proliferation of IoT devices, advances in parallel computation, and 5G. This enables businesses to take advantage of the enormous opportunity to bring AI into their workplaces and act on real-time insights while lowering costs and increasing privacy.
We’re still in the early stages of edge AI, but the possibilities seem limitless.