The Promise and Peril of AI at the Periphery

At this time last year, we predicted that edge computing would eventually supplant cloud computing. This was based on the premise that cloud computing is reliant on the centralisation of resources, and the service providers had already concentrated the majority of these resources. With limited possibilities for growth, expanding computer capacity necessitates moving it to the edge. In reality, this transition is taking place currently.

By 2023, it is anticipated that the edge computing industry will have a market value of $1.12 trillion, which attests to the high expectations for this technology. This is entirely logical given that edge computing is the only answer to the increasing demand for data storage and processing, given the constant production of new information. Once 5G is widely available across the globe, this will be even more apparent.

Edge computing is likely to be significantly impacted by the unstoppable development of Artificial Intelligence (AI). With its potential to offer enhanced privacy, security, and speed, edge AI is increasingly being adopted as the industry standard. It is important to fully understand why this is the natural next step in an era of ever-evolving technology.

To begin, let’s define “Edge AI.”

Gaining a clear understanding of what is meant by “edge” in the term “edge AI” requires a thorough understanding of “edge” computing. To illustrate this concept, consider a situation where you are working from home and using a cloud-based graphics editor. While your computer, web browser, and internet connection enable you to access the editor, the actual visual processing is taking place on a server.

This server, alongside numerous others of a similar ilk, is a centralised node where data from numerous sources is processed in order to achieve the desired outcome. Our mobile phones and autonomous vehicles are just two examples of the numerous modern devices and technology which employ this approach in contemporary times. It is possible to conceptualise the entire system as a loop, with nodes sending information to the centre and awaiting a response. This hardware would be situated on the periphery of the network.

Nowadays, cloud computing follows an approach of providing a limited number of larger organisations with the necessary infrastructure to analyse substantial amounts of data from a variety of sources. However, this model is gradually becoming less relevant. There are several reasons behind this, with security and confidentiality worries being among the most pressing. If data is shared between two parties, it could be intercepted and taken advantage of by criminals. Furthermore, the concept may be perceived as an infringement on individual privacy since user data is located on servers managed by the company.

There is a growing appreciation of the advantages of edge computing, which eliminates the need for a centralised server by processing data locally on the user’s device. This means that the user’s device is responsible for managing data processing, only communicating with a centralised server when it is necessary to do so. Consequently, this approach has resulted in a large number of people becoming supporters of the edge computing paradigm.

It is clear to see that the advancements in technology over recent years, particularly the emergence of the Internet, have led to a marked difference in the way we operate. However, it is equally evident that many of the fundamental principles remain the same. The key distinction here is the incorporation of artificial intelligence (AI) into devices, allowing them to process data more effectively and assess increasingly complicated data sets. This is the crux of what we mean when we refer to modern AI and its cutting-edge potential.

A smart thermostat is an excellent example of the advantages of having edge AI algorithms. Without the need for a connection to the internet or a central server, the thermostat can still operate normally. It is able to intelligently adjust the temperature and remember user preferences. Upon reconnection to a network, the thermostat can then submit data to the maker without any detriment to the user experience. This demonstrates the power of edge AI algorithms in providing an uninterrupted service.

It is possible to consider this as a rudimentary explanation of how Artificial Intelligence (AI) works in practical applications. In other words, there could be occasions where an especially sophisticated AI is necessary.

Edge AI is essential.

Imagine yourself travelling to work in a self-driving vehicle. Suddenly, another vehicle unexpectedly appears in its path. Thanks to the local artificial intelligence (AI) technology, the car is able to quickly and accurately respond to the situation, meaning you arrive on time, despite the unexpected delay. This illustrates the importance of having a local AI in the car, as having to send data to a central server and waiting for a response could have been too slow and potentially caused you to miss your target. Having a local AI is essential in emergency situations, even if the car’s software does not include an emergency response feature.

Given the current advancements in technology, we would now be able to experience the full potential of our tools and gadgets, which have been customised to meet our expectations. However, it is not only speed that should be considered; the use of Edge AI would also be beneficial from a security and privacy perspective as it would reduce the amount of connections between a device located at the edge of the network and the central server, thereby allowing for data to be stored locally.

The advantages of centralised cloud computing, particularly in terms of scalability, are undeniable. However, it may soon become unmanageable due to the sheer volume of data that is increasing exponentially with the emergence of 5G devices and the Internet of Things. Establishing the necessary connections to manage this data could prove to be costly and difficult to achieve in time. On the other hand, network latency, the delay between the request for a data transfer and the actual transmission, should not be a concern in practical scenarios where quick processing is needed.

As the world of technology evolves, an increasing number of professionals are recognising the significance of Edge AI in the development of software, machine learning, internet of things, and other digital products. This requires a fundamental shift in the way these professionals approach development, which has thus far been mostly focused on the objectives of the product itself, rather than on the end user. The potential implications of this are immense, as it could lead to a future where individuals and the gadgets designed to assist them are better tailored to their personal needs and preferences.

Simply a Matter of Time

The emergence of the COVID-19 pandemic has showcased the need for novel solutions to address traditional problems. We believe that Edge Artificial Intelligence could be an effective and beneficial technology to explore and integrate into our lives. Its implementation would have a positive impact on multiple sectors, such as healthcare, security and social distancing monitoring. By implementing Edge AI, we would have a better understanding of the spread of the virus and how to treat it. It could also be used to improve the safety of citizens around the world. We envision a future where Edge AI is widely accessible and utilized to our benefit, and we will strive to make this a reality.

It is pointless to regret the opportunities that have been missed. The best course of action is to recognise the potential of Edge AI and start devising ways to integrate it into the devices we are currently producing. We must strive to identify user-friendly applications that can guarantee the privacy, security, and speed that Edge AI offers. It is clear that its introduction is imminent, so it is our responsibility to be prepared for it.

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