AI (Artificial Intelligence), is constantly evolving, introducing new opportunities to businesses and consumers alike. Edge AI, which combines the power of advanced AI models, IoT devices, and edge computing, is beginning to gain momentum.
Promising opportunities to enhance real-time insights, boost privacy, reduce costs, and enable scalable AI strategies, edge AI holds a lot of potential for businesses. As we dive deeper into a world powered by ever-more sophisticated intelligent systems, edge computing could be the key to enhancing artificial intelligence.
Here, we’ll explain edge AI, how it works, its benefits, challenges, and emerging use cases in the industry. Buckle in for a complete guide to the technology that is poised to revolutionize AI accessibility.
What is Edge AI?
Edge AI combines artificial intelligence with edge computing practices. It allows companies and users to execute AI tasks directly on interconnected devices without relying exclusively on cloud servers.
It’s a revolutionary concept in edge computing, which has helped companies reduce latency and accelerate efficiency in computing tasks for years, while minimizing the security risks of cloud data transmission. With edge AI, data is processed directly on the network edge (even without an internet connection), facilitating rapid data management for AI applications.
Today, countless AI innovations and solutions from companies like NVIDIA and IBM rely on edge AI. Self-driving cars, wearable devices, smart home appliances, and security cameras all use this technology.
The Evolution of Edge AI
Although edge computing and artificial intelligence aren’t new concepts, more organizations have begun combining these technologies in recent years. As companies in every industry look to increase automation and improve efficiency, advances in edge AI have introduced opportunities for machines and devices to operate with almost human-level cognition.
Primarily, three innovations have contributed to the explosion of edge AI:
- Neural network evolution: Neural networks used to power advanced AI systems have been developed to enable generalized machine learning. This means companies can successfully train intelligent models and deploy them on the edge of their networks.
- The rise of IoT: Widespread adoption of devices connected to the “Internet of Things” has led to an explosion of opportunities for companies. Businesses can now collect data from industrial sensors, robots, and more to power AI models at the edge. Plus, with 5G connectivity, IoT systems can share data faster than ever before.
- Compute infrastructure advancements: Advanced computational power is necessary to run AI on the edge. Fortunately, advances in powerful GPUs, such as those offered by NVIDIA, can now run cutting-edge neural networks.
Edge AI vs Distributed AI Systems
Distributed AI (DAI) and edge AI are closely connected. Distributed artificial intelligence uses multiple networked devices in unison to improve performance and solve problems. They’re designed to mimic the human decision-making process and learn from experience.
Thanks to edge AI, which enables localized decision-making, organizations don’t need to constantly transmit data to a central location to complete tasks. However, there’s still usually a requirement to transmit data to the cloud to train and deploy AI pipelines. Deploying these systems across various locations and applications can cause resource management and scale issues.
Distributed AI addresses these challenges by integrating intelligent data collection strategies into AI lifecycles and adapting and monitoring pipelines over time. DAI enables algorithms to autonomously process data across various systems, domains, and devices on the edge.
Edge AI vs Cloud AI Systems
Cloud computing and edge computing can both benefit each other when companies deploy AI solutions. The cloud offers an opportunity to reduce infrastructure costs, improve scalability, and streamline collaboration. Edge computing helps to offer faster response times, and lower bandwidth costs, as well as resilience from network failure.
However, most organizations will use edge and cloud computing systems for different purposes when implementing AI. Cloud computing systems offer greater computational capabilities and enhanced storage capacity than edge solutions. That makes them ideal for training and deploying more intricate and advanced AI models, like LLMs, and conversational AI systems.
Edge AI offers a better overall experience when companies need access to real-time prediction and data processing capabilities. It reduces latency, and reliance on network bandwidth, allowing for faster results, and cost savings. For instance, in a self-driving vehicle, the technology can rapidly process information and deliver direct feedback to drivers.
In many cases, companies will use the cloud to run models during training and retraining periods and rely on edge AI for fast-paced and secure machine learning applications.
How Does Edge AI Work? The Basics
Understanding the inner workings of edge AI solutions requires knowledge of AI, machine learning, and edge computing. For AI solutions like intelligent speakers, autonomous vehicle systems, and other tools to understand speech and images and emulate human skills, they need to replicate human intelligence. This means giving systems a kind of “thought process.”
Edge AI uses deep neural networks (a unique type of data structure) to replicate human cognition. Deep neural networks are trained to respond to specific queries and tasks through a process called “deep learning.” Because these systems rely on vast amounts of data, the training process usually takes place in a data center or the cloud.
After training, the model is deployed as an interface engine that runs on a computer or device in a specific environment, like a car, hospital, or factory. The models progressively improve their performance over time. If they encounter an issue, they transfer data about the problem to the cloud to enhance the training of the original model. The model is then trained further, and the inference engine used at the edge is gradually updated.
The Benefits of Edge AI
As AI algorithms and models become more advanced, capable of understanding language, images, sounds, and more, they’re becoming more valuable in places occupied by end-users with real-world problems.
Deploying AI applications that can respond to local data would be impractical in an enterprise data center or centralized cloud environment, due to issues with latency, bandwidth, and even security or data privacy.
Edge AI is becoming a valuable alternative in today’s world. The market is expected to reach a value of $269.82 billion by 2032.
The biggest benefits include:
Reduced Latency
Transferring data to the cloud and back takes time, even with a strong internet connection. Although the actual latency in this data transfer can only be around 100 milliseconds, even that delay can cause significant issues.
Edge AI eliminates this problem, diminishing latency significantly. For instance, NVIDIA GPU processors and analytics software built into Porsche cars allow the company to consistently access and process vast amounts of data on how the car is operating. When issues emerge, the automation system can rapidly “take the wheel” and protect the driver.
Decreased Bandwidth
Because edge AI processes data on a “local” level, it reduces the amount of data that needs to be transmitted via the internet. You don’t need to use nearly as much internet bandwidth to run an AI application. Using less bandwidth means that your data connection can handle more simultaneous data reception and transmission.
Improved Real-time Analytics
With edge AI, users can process data on devices instantly, without the need for an internet connection, system connectivity, and integration. This means analytical information can be surfaced almost in real-time. That’s crucial for time critical situations, such as when companies need to detect if a machine on an assembly line is about to malfunction.
Notably, though, edge apps can be limited in their ability to manage extensive volumes of data, which is sometimes why companies still rely on cloud systems.
Greater Data Privacy and Security
Securing data is becoming increasingly difficult in today’s AI landscape, particularly in the enterprise. Edge AI can analyze real-world information immediately without ever exposing it to another human being, transferring it to another network, or sending it to the cloud.
This reduces the risk of data being mishandled, eliminates issues with data sovereignty regulations, and can even reduce the risk of criminals gaining access to data in transit. However, companies that store information to train their AI systems in the cloud will still need protections for those centralized databases.
Scalability and Availability
Edge AI offers an incredible opportunity to expand systems using cloud-based platforms and advanced technologies. They empower organizations to scale their AI strategies with exceptional speed, minimize downtime, and increase efficiency.
With edge AI, companies can reduce the bottlenecks in cloud data processing, and even improve accessibility to advanced tools. Decentralization and offline solutions mean that users don’t even need internet access to process data on the move.
Reduced Costs
Running cloud services and advanced models in the cloud (like LLMs) can be expensive. Utilizing edge AI can help companies reduce the costs associated with cloud resources and data storage.
When cloud computing handles all of the power required for an AI service, expenses grow, as a result of high traffic and computing requirements. Edge devices help to balance the load with CPU, GPU, and memory solutions for budget-conscious companies.
Continuous Improvement
AI models grow more efficient, powerful, and accurate as they’re exposed to new data. When an artificial intelligence model is exposed to data it can’t process or understand, it can upload that information into the cloud, for retraining purposes.
This means that tools can be constantly improved and optimized over time, leading to better outcomes for users.
Edge AI Use Cases for Different Industries
Just as the use cases for artificial intelligence in general are expanding across all industries, the opportunities for using edge AI in different sectors are increasing too. Just some potential use cases for different industries include:
Healthcare:
Wearable devices, combined with edge AI solutions can help care providers consistently monitor heart rate, blood pressure, glucose levels and more. These devices can even alert caretakers when patient vitals fall, ensuring they can respond quickly to potential health threats.
Some modern medical instruments can even provide physicians and surgeons with guidance and support during procedures in real-time.
Manufacturing:
In the manufacturing industry, sensor data can provide insights into anomalies and even forecast machine failures in advance. This enables the use of predictive maintenance strategies that significantly reduce downtime and improve efficiency.
Edge AI can even optimize worker safety and supply chains with real-time insights into what’s happening in different environments.
Retail:
Various forms of AI are making their way into the retail landscape. With edge AI, retailers can allow customers to search for product information on kiosks and devices with their voice rather than typing words into a system. They can even enable access to smart checkouts that can instantly sense products and process transactions.
Smart homes:
Edge AI solutions are particularly common in the smart home landscape. They power everything from intelligent doorbells to thermostats and entertainment systems. Edge technology allows these devices to rapidly process data to transmit information to a user. This helps to improve the efficiency of smart devices and protect users’ privacy.
Security and Surveillance
Speed is essential in the security landscape. However, many computer vision systems used to detect threats in an environment struggle to process images and information without sending data to the cloud. Edge AI allows smart security devices to identify suspicious activity and trigger alarms with exceptional speed.
Energy
In the energy sector, where problems with supply can threaten the welfare of entire populations, edge AI is extremely valuable. The right systems can combine historical data with weather patterns and grid health information to create complex simulations.
Energy providers can then use these simulations to optimize energy generation, distribution, and resource management.
The Challenges with Edge AI
Just like any form of AI, edge AI still has challenges to overcome related to development, security, and even ethical usage. Adding AI to existing legacy infrastructure can be extremely complicated, particularly for larger companies with fixed-function systems. Training and fine-tuning those models also requires significant investment and expertise.
Beyond this, companies often struggle to address hardware diversity issues. Edge-native applications can span various connectivity protocols, operating systems, and nodes, leading to cost constraints, computing needs, and compliance concerns.
Plus, while edge AI can improve security, there are still risks to consider, particularly in IoT security and protecting centralized cloud networks. Innovative companies are beginning to address these issues, but deploying the tech at scale is still complex.
Looking to the Future
Edge AI is still in its early stages – but it’s a concept that’s growing at an incredible rate. Technology innovations and trends will continue to push investment in this tech forward. Already, 5G networks are enhancing IoT device capabilities, and strengthening the performance of new applications.
What’s more, IoT solutions are becoming more effective at collecting all kinds of data from various systems and solutions, giving AI tools more information to work with. With advanced machine learning algorithms and systems, companies in the future will be able to create even more powerful models that work seamlessly on the “edge”.
Going forward, these trends, the increasing access companies have to AI resources and powerful computing systems, and the growing demand for efficient AI systems will power numerous transformations.
