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How AI at the Edge Could Revolutionize Artificial Intelligence

June 16, 2022 - Lou Farrell

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Artificial intelligence (AI) has already reshaped modern life and business. As widespread as it is, though, this technology is far from reaching its peak. AI at the edge is the next significant step forward, promising to take AI’s disruptive potential to new heights.

As AI has become more common, its challenges have become more evident alongside its advantages. Combining this technology with edge computing could help it overcome these obstacles and bring its benefits to new use cases or expand on existing ones. This movement is already visible in some smaller applications, but it will likely grow to transform AI as a whole.

What Is AI at the Edge?

AI at the edge refers to running AI applications across edge computing networks. Instead of deploying an algorithm entirely on the cloud or a single physical device, edge AI combines the advantages of both. It leverages the tens of billions of IoT connections in the world to distribute AI processes while keeping them near their end uses.

Cloud computing has become the standard for many AI applications, given the sheer volume and distribution of their data. AI at the edge takes that further by bringing these processes closer to the data’s source. It’s still technically processing on the cloud, but that cloud is a network of nearby devices instead of far-away data centers.

Edge computing already uses these distributed networks to run conventional computing processes, so running more complex AI algorithms on them is the logical progression. Similarly, artifical intellgence already capitalizes on the flexibility of the cloud, so it’s natural to transition it to the edge as cloud environments move in that direction.

Advantages of AI at the Edge

Running AI at the edge has multiple advantages over entirely on-device or traditional cloud systems. Lower latency and faster speeds are some of the most notable of these benefits. It enables sub-millisecond latency in some circumstances since the data has less distance to cover before and after processing.

Sending information to a remote data center and back takes time, especially at the volumes that some AI applications handle. Edge AI mitigates this by sending data to nearby connected devices instead. Some may still travel to conventional data centers for their processing power, but there’s still far less information making that trip.

This reduced reliance on remote data centers also reduces networking costs. With less information to send as far, companies will require less bandwidth, which, in turn, reduces their internet expenses.

Since it involves more local data processing, it can improve security, too. Remote data centers will hold and process less sensitive data, reducing the risk of exposure during transit and mitigating third-party data breaches. Considering 20% of global CIOs rank security concerns as a leading barrier to AI, that could help boost AI adoption.

The edge’s simultaneous distributed nature and local scope also open the door to new AI applications. Some processes require more speed and reliability than traditional data centers enable but more processing power than what individual devices can handle. Placing AI at the edge provides the ideal middle ground for those applications.

Applications of Edge AI

Many of AI’s loftiest goals today would be easier to achieve with edge AI. Self-driving cars, for example, require complex decision-making but extremely fast speeds and reliable connections. Edge AI is the key to achieving that balance. Driverless vehicles could distribute computing across nearby connected devices, including other cars, to navigate without the high latency of conventional cloud computing.

Similarly, this technology could help smart cities offer more to their citizens. Connected infrastructure could gather traffic data and run it through AI algorithms across nearby devices. The resulting insights could then inform drivers of optimal routes, open parking spaces or other recommendations.

Edge AI would also unlock the potential of Industry 4.0. Smart manufacturing needs to leverage real-time data to ensure automated workflows function properly and enable predictive maintenance. This would take that real-time data and process it close to its source, leading to faster, more reliable insights.

Edge AI could bring similar benefits to hospitals. IoT tracking systems could provide real-time insight into resource availability and location that AI algorithms then analyze to inform ideal management decisions. Hospital staff could see recommendations for resource allocation or orders in real-time as conditions change.

Amazon Go stores are an excellent example of existing small-scale AI at the edge. These systems synchronize computer vision and sensor data to determine which shoppers pick up which products and charge them accordingly as they walk out. Deploying artificial intelligence across the various edge devices in the store helps make accurate readings from the data and prevents lag.

What Challenges Remain?

As promising as this technology is, some obstacles remain between today’s systems and widespread implementation. Hardware limitations are among the most significant. While distributed computing reduces on-device processing demands, edge devices still need more computing resources than many today have.

Edge AI devices don’t need extensive power, but providing enough in a small package is challenging. If technological advancement follows Moore’s Law, this won’t be an issue for long, but it hinders deployment today. Similarly, AI at the edge may require standardized hardware across devices to enable seamless interaction.

Data quality is another issue, with 87% of AI users citing concerns about it impacting their projects’ success. If edge devices don’t gather accurate or relevant information, it’ll skew the results, regardless of how fast they may produce them.

Finally, while it provides some security benefits, it also raises concerns of its own. Connected devices will need to feature stronger built-in protections, and networks will need advanced security protocols to ensure no one intercepts data transferring between devices.

Edge AI Is the Next Big Thing for Artificial Intelligence

AI at the edge may still be a few years away from wide-scale deployment. However, as technical, management and regulatory factors improve, this movement could come to redefine AI as we know it. It’ll take it to new places, expand its current uses and maximize its benefits.

Edge AI could help artificial intelligence achieve some of the technology’s loftiest promises. Business and everyday life would improve as a result.

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Author

Lou Farrell

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