What Is Edge AI and How Does It Work? – NVIDIA Blog

Recent strides in the efficacy of AI, the adoption of IoT devices and the power of edge computing have come together to unlock the power of edge AI.

This has opened new opportunities for edge AI that were previously unimaginable from helping radiologists identify pathologies in the hospital, to driving cars down the freeway, to helping us pollinate plants.

Countless analysts and businesses are talking about and implementing edge computing, which traces its origins to the 1990s, when content delivery networks were created to serve web and video content from edge servers deployed close to users.

Today, almost every business has job functions that can benefit from the adoption of edge AI. In fact, edge applications are driving the next wave of AI in ways that improve our lives at home, at work, in school and in transit.

Learn more about what edge AI is, its benefits and how it works, examples of edge AI use cases, and the relationship between edge computing and cloud computing.

Edge AI is the deployment of AI applications in devices throughout the physical world. Its called edge AI because the AI computation is done near the user at the edge of the network, close to where the data is located, rather than centrally in a cloud computing facility or private data center.

Since the internet has global reach, the edge of the network can connote any location. It can be a retail store, factory, hospital or devices all around us, like traffic lights, autonomous machines and phones.

Organizations from every industry are looking to increase automation to improve processes, efficiency and safety.

To help them, computer programs need to recognize patterns and execute tasks repeatedly and safely. But the world is unstructured and the range of tasks that humans perform covers infinite circumstances that are impossible to fully describe in programs and rules.

Advances in edge AI have opened opportunities for machines and devices, wherever they may be, to operate with the intelligence of human cognition. AI-enabled smart applications learn to perform similar tasks under different circumstances, much like real life.

The efficacy of deploying AI models at the edge arises from three recent innovations.

Since AI algorithms are capable of understanding language, sights, sounds, smells, temperature, faces and other analog forms of unstructured information, theyre particularly useful in places occupied by end users with real-world problems. These AI applications would be impractical or even impossible to deploy in a centralized cloud or enterprise data center due to issues related to latency, bandwidth and privacy.

The benefits of edge AI include:

For machines to see, perform object detection, drive cars, understand speech, speak, walk or otherwise emulate human skills, they need to functionally replicate human intelligence.

AI employs a data structure called a deep neural network to replicate human cognition. These DNNs are trained to answer specific types of questions by being shown many examples of that type of question along with correct answers.

This training process, known as deep learning, often runs in a data center or the cloud due to the vast amount of data required to train an accurate model, and the need for data scientists to collaborate on configuring the model. After training, the model graduates to become an inference engine that can answer real-world questions.

In edge AI deployments, the inference engine runs on some kind of computer or device in far-flung locations such as factories, hospitals, cars, satellites and homes. When the AI stumbles on a problem, the troublesome data is commonly uploaded to the cloud for further training of the original AI model, which at some point replaces the inference engine at the edge. This feedback loop plays a significant role in boosting model performance; once edge AI models are deployed, they only get smarter and smarter.

AI is the most powerful technology force of our time. Were now at a time where AI is revolutionizing the worlds largest industries.

Across manufacturing, healthcare, financial services, transportation, energy and more, edge AI is driving new business outcomes in every sector, including:

AI applications can run in a data center like those in public clouds, or out in the field at the networks edge, near the user. Cloud computing and edge computing each offer benefits that can be combined when deploying edge AI.

The cloud offers benefits related to infrastructure cost, scalability, high utilization, resilience from server failure, and collaboration. Edge computing offers faster response times, lower bandwidth costs and resilience from network failure.

There are several ways in which cloud computing can support an edge AI deployment:

Learn more about the best practices for hybrid edge architectures.

Thanks to the commercial maturation of neural networks, proliferation of IoT devices, advances in parallel computation and 5G, there is now robust infrastructure for generalized machine learning. This is allowing enterprises to capitalize on the colossal opportunity to bring AI into their places of business and act upon real-time insights, all while decreasing costs and increasing privacy.

We are only in the early innings of edge AI, and still the possible applications seem endless.

Learn how your organization can deploy edge AI by checking out the top considerations for deploying AI at the edge.

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What Is Edge AI and How Does It Work? - NVIDIA Blog