Why Artificial Intelligence at the Edge?

Everyday it seems as if there’s a new artificial intelligence (AI) solution disrupting the market. From generative AI to new search engines, AI is paving the path towards a highly efficient future, with new ways of approaching AI and machine learning being developed by innovators everywhere. So why then is deploying artificial intelligence at the edge paramount to the growth of AI?

As the name suggests, AI at the edge, or edge AI, refers to deploying artificial intelligence solutions and models directly onto local edge devices. These devices include sensors, routers, wearable devices, and autonomous vehicles, with “edge” in edge devices and edge computing referencing the literal network’s edge, one that’s near data sources and users.

Edge computing has been a mainstay within technological practices because of its close proximity to data sources, resulting in efficient data storage and real-time data processing without relying on cloud infrastructure and internet connection. And with the data-intensive requirements of AI and ML, reducing latency and increasing processing speed can drastically improve operations and training.

With a majority of our everyday devices being a part of an edge device network, incorporating artificial intelligence at the edge can evolve our device interactions and create a technological tomorrow.

Why Edge Artificial Intelligence Is Relevant Now More Than Ever

Edge artificial intelligence isn’t just another buzzword, AI developers and organizations alike have been looking towards the edge to increase their efficiency for the longest time. Automation has been instrumental in achieving organization-wide operational efficiency, with artificial intelligence revolutionizing automation through pattern recognition and task execution. The timing was perfect for edge AI, with its adoption growing rapidly and more and more companies cement edge AI as a driving force of enterprise innovation. And with data being created at record-breaking speeds, it only makes sense to deploy AI innovations at the edge.

Edge AI has come a long way since the creation of artificial intelligence, with its maturity opening the doors for solution growth everywhere. Nvidia credits the rise of edge AI popularity and usage to three innovations:

1. Neural Network Maturity: With more and more resources being poured into neural networks, AI solutions, and deep learning models, organizations have successfully matured and fine-tuned these systems, making them appropriate for use at the edge.
2. Compute infrastructure growth: Infrastructure has grown significantly in the past couple of years, with distributed infrastructure allowing for stronger neural network usage on edge networks
3. IoT device adoption: Internet of Things (IoT) devices usage has experienced explosive growth over the past decade, showing no signs of slowing down. With more data now than ever because of these devices, neural networks and AI solutions at the edge have a place to grow and operate without fail.

The stage is set for edge AI, with organizations all over the world looking to leave their mark in this rapidly growing space. 

What are Edge AI Benefits?

Deploying AI solutions close to data sources creates a ton of potential benefits not only for users, but also for AI training. While AI isn’t a complete replacement for human intelligence, deploying these solutions at the edge enhances the user experience and organizational efficiency, with the benefits of edge AI including:

• Lowered latency: deploying near data sources can increase response intervals by decreasing information travel to a local data center, with reduced latency increasing speed
• Decreased bandwidth demands: because edge AI processes data on a local level, less bandwidth is required to handle large data transfers over the internet
• Real-time analytics: the use of edge devices enables real time data processing, removing system integration and location communication from the strenuous process
• Enhanced availability: edge AI is highly decentralized and capable of operating offline, making edge AI solutions highly available in data processing
• Increased scalability: rather than requiring physical equipment to scale, edge AI depends on cloud platforms and other edge capabilities that can expand on equipment, making scaling as simple as clicking a button
• Stronger data privacy: removes the threat of vulnerabilities conducted through transfers, as well as reduces the risks of mishandling and forgetting about compliance
• More opportunities for growth: operating at the edge introduces more data to AI solutions, with training opportunities become available as more and more complex datasets are added

Edge AI Use Cases

The use of artificial intelligence has been normalized and encouraged throughout many different industries. From streamlining marketing processes to generating powerful code for developer teams, AI has been proven time and time again that it’s extremely useful in any industry. In the case of edge AI, this utility is upheld, showing to create value in industry and disciplines such as:

Healthcare

Edge AI and healthcare go hand in hand for a multitude of reasons.

Within healthcare buildings, providers use edge AI for faster data processing, enabling faster insight extraction as well as stronger data filtering and organization capabilities. And with the amount of data this industry generates alone, strong handling capabilities make all the difference. This also extends to emergency vehicles and emergency rooms, allowing paramedics and health care professionals to gather patient information and operation strategies on the spot.

Outside of healthcare walls, edge AI is used to power wearable devices such as smart watches and heart rate monitors. These devices communicate directly with healthcare professionals when something goes wrong and captures data necessary for treatment and preventative care. Edge AI also powers telehealth efforts, bringing healthcare to patients if they aren’t able to access dedicated facilities.

Smart Homes

Edge AI devices can be found throughout the modern home. From smart doorbells and app-controlled lightbulbs, to intuitive refrigerators and even smart vacuum robots, edge AI is used to change how we live and interact with technology. Within this connected edge ecosystem, users can utilize apps to communicate with the devices that enhance their quality of life. In turn, these devices gather sensor data and learn to automate these user preferences, making adjustments seamless while protecting valuable information. 

Retail

The retail experience has evolved significantly since the pandemic pushed eCommerce to rise in popularity. As a result of drastic changes, retailers have been innovating and pushing for ways to make the brick and mortar shopping experience more customer friendly. The technology pushing for this evolution include smart shopping carts, inventory management sensors, smart device integrations, and even dedicated stores that offer ‘pick and go’ shopping. Powering these technologies is edge AI, connecting user information and retailer information to create an all-encompassing shopping experience.

Manufacturing

Organizations around the world have been embracing edge AI for their manufacturing operations to boost efficiency and productivity while increasing operator safety. 

The most important use of edge AI in manufacturing disciplines is within predictive maintenance. With edge AI, sensors can be used to identify machine failures and other anomalies before they happen, alerting for maintenance before disaster strikes. Not only does this protect the lives of operators, but also allow for downtime and cost reductions.

These are just a few of the uses where edge AI shines!

How Lyrid Supports Edge AI Deployments

Deploying artificial intelligence solutions at the edge offers tons of benefits to users seeking to do so. From reduced latency and decreased bandwidth requirements, to heightened scalability and increased privacy and availability, shipping edge AI solutions have quickly become one of the main drivers of innovation for enterprises everywhere. 

Despite the sudden push for edge AI being questionable in validity, the maturation of AI systems and neural networks, increase in IoT device adoption, and growth in compute infrastructure have created the perfect foundation for edge AI to blossom. And with applicable uses in almost every industry, edge AI is becoming the new norm.

While edge AI provides tons of values to users across different disciplines, the power needed to host such solutions and networks is certainly formidable. An overlooked aspect of edge AI is the role cloud computing plays, offering that power for AI solutions to operate and train. Lyrid offers infrastructure for AI projects to grow. From LLMs and computer vision models, to full-fledged AI solutions, Lyrid’s infrastructure provides AI scalability and performance within a single pane of glass.

Lyrid enables AI teams to reduce their latency, increase efficiency and cost savings, and stay competitive in the ever-growing AI landscape. Looking to deploy AI at the edge? Book a meeting with one of our product specialists for more information on our AI dedication!