Why Edge Computing and AI are the perfect combination

Chris Sampson

Today, we already know how to build artificial intelligence (AI) to improve an extensive range of processes and applications. Still, AI hasn't been adopted in many areas due to the complexity and cost of implementation.

The pioneers of AI as we know it today are mostly internet companies. As a result, almost all at-scale implementations of AI exist in the cloud. Those that run in real-time typically rely on a good internet connection to facilitate connection to the cloud, which does all the heavy lifting.

Edge AI systems are changing this rapidly. Edge AI addresses the factors limiting AI adoption in some areas and has several advantages over Cloud AI.

Edge AI offers a higher level of security for both data, devices, and networks.

Data security
While encryption is a great defence, it isn't bulletproof. Whenever data is sent over a network and stored, a level of risk is absorbed.

For applications where sensitive data is analysed, edge computing can significantly reduce the risk in handling and processing this data. Edge device processing can eliminate the need to send sensitive data over a network and retain it somewhere.

Data-sensitive applications can use AI to process data directly on the local device allowing the extraction of metadata without any identifiable data being sent over the network or existing anywhere but on the device itself temporarily.

Network Security
Not only is Edge AI great for data security, but it also has a positive impact on overall network security. In environments where a consistent internet connection presents an unacceptable security risk, edge processing offers the ability to do advanced AI in real-time without compromising the network. Fewer connections mean fewer attack vectors.

Device security
In data sensitive or mission-critical applications, it's generally preferred that devices run on isolated networks with minimal external access. Traditionally it has been challenging to implement AI in these systems since connecting a device to the cloud presents an unacceptable security risk.

For these applications, using edge processing can mean a device doesn't need to have an external connection during regular operation or perhaps at all. Disconnecting the device from the internet limits exposure time, significantly reducing the risk of a device being compromised externally or being used as a vector to access a connected network.

Edge AI is much more reliable and consistent than cloud-based AI because reliance on internet connections is eliminated.

Connection stability and bandwidth
While it's true that the internet is more available and easier to access than ever, it isn't flawless.

WIFI isn't always as reliable as we'd like it to be, particularly on networks with many devices. The alternate option of cables can be a headache a best and simply not possible in some circumstances.

This is particularly relevant in commercial and industrial settings where system reliability is paramount. Edge AI provides a way to reliably deliver AI to mission-critical applications without being dependent on internet stability. In many applications, this is the difference between AI being adopted or not.

AI provides the ability to compress data like never before. By extracting the meaningful metadata from raw data using AI, we don't need to store or transport the raw data. For example, in computer vision applications, this can mean an incredible compression rate, transferring kilobytes of information rather than gigabytes. Combining this AI tech with edge processing capabilities unlocks a range of new applications.

Eliminating the need to transport high-fidelity data means network traffic volumes from devices can be significantly reduced or potentially eliminated. This enables AI adoption in areas where networks are either unreliable or have limited bandwidth, such as rural and remote.

Superior latency
In most applications where AI is used today, a slight delay in processing doesn't impact the overall function or user experience. Nonetheless, consumers expect technology to be fast, and industry builds a competitive advantage on being the fastest.

Edge AI is the fastest way to deliver AI to a process or application. By eliminating the need to transmit data across networks and into the web, latency is reduced, and AI response times are far more consistent.

The reliably low latency of Edge AI unlocks the adoption of AI into processes that demand high performance.

AI and Edge Computing are the perfect combination for many previously untapped use cases. Providing enhanced performance, security, and reliability compared to Cloud AI, expect Edge AI to be a key component in the next wave of AI-powered applications.

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Chris is Tiliter's CTO and one of our co-founders. He shares his expertise on AI, deep learning and edge computing.

Chris is Tiliter's CTO and one of our co-founders. He shares his expertise on AI, deep learning and edge computing.

Chris Sampson

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