The Promise and Peril of AI at the Periphery

In our forecast from a year ago, we anticipated that edge computing would eventually overtake cloud computing. This predication was based on the understanding that cloud computing’s reliance on centralised resources had reached a saturation point, with providers already concentrating their resources. In order to expand computer capacity, decentralisation is the way ahead. In fact, this shift is happening right now, as confirmed by our recent insights.

Industry experts predict that, come 2023, the market value of edge computing will reach an astounding $1.12 trillion, evidencing the fervent enthusiasm for this technology. It’s no surprise given that edge computing is the prime solution for the mounting demands of data storage and processing that come with constant information creation. And with 5G soon to become ubiquitous, this will become even more apparent.

The rapid growth of Artificial Intelligence (AI) will undoubtedly have a profound impact on edge computing. With its immense potential to deliver heightened speed, security, and privacy, edge AI is rapidly becoming the new norm in the industry. It is critical to grasp exactly why this marks the next logical advancement in the ever-evolving tech landscape.

First and foremost, we need to establish a clear definition of “Edge AI”.

To gain a clear comprehension of the “edge” element in “edge AI”, we first need to grasp the essence of “edge” computing. Let’s imagine a scenario where you’re working from your home, utilising a cloud-based graphics editor. Even though your computer, web browser, and internet connection allow access to the editor, the actual visual processing takes place on a remote server.

This server, along with countless others like it, represents a centralised node where data from multiple sources is processed until the intended outcome is achieved. Contemporary mobile phones and autonomous vehicles are only two instances of numerous modern devices and technologies that employ this strategy. It is conceivable to visualise the entire system as a loop, with nodes relaying information to the central server and waiting for a response, with this hardware positioned at the network’s edge.

In present times, cloud computing employs an approach of providing a select few large organisations with the necessary infrastructure to analyse extensive amounts of data from various sources. However, this model is gradually becoming outdated. A host of reasons account for this, with security and privacy concerns ranking among the most pressing. When data is shared between two parties, it could be intercepted and exploited by malicious actors. Furthermore, the very concept may be viewed as an invasive practice infringing on personal privacy, as user data is stored on servers managed by the company.

There is a growing acknowledgement of the benefits of edge computing, which obviates the need for a centralised server by carrying out data processing locally on the user’s device, as explained in our post on edge computing vs. the internet of things. This entails the device itself having responsibility for data processing, only interacting with the centralised server when required. In effect, this approach has gained huge support from many people who endorse the edge computing paradigm.

The profound impact of technological advances over the past few years, including the emergence of the Internet, is self-evident, leading to vast changes in our operational methods. However, it is equally obvious that many of the fundamental principles remain unaltered. The pivotal difference now lies in the integration of artificial intelligence (AI) into devices, enhancing their data processing capabilities and enabling the analysis of increasingly intricate data sets. This delineates the core objective of modern AI and its avant-garde potential.

The distinct advantages of having edge AI algorithms are well-illustrated by a smart thermostat. Operating normally without the need for a connection to the internet or central server, the thermostat is capable of intelligently adjusting the temperature, while remembering user preferences. Upon reconnection to the network, the thermostat can transmit data to the manufacturer without any adverse effect on the user experience. This showcases the efficacy of edge AI algorithms in providing an uninterrupted, seamless service.

This offers a simplified explanation of how practical applications of Artificial Intelligence (AI) function. However, there may be instances where a more advanced AI is required.

Edge AI is an indispensable component.

Consider the scenario of commuting to work in a self-driving vehicle, when suddenly another vehicle unexpectedly appears in the path. Thanks to the local artificial intelligence (AI) technology, the car can respond swiftly and accurately to the situation, ensuring timely arrival despite the unforeseen delay. This underlines the significance of having an on-board AI in the car, as waiting for a response after sending data to a central server may prove too slow and potentially result in a missed target. A local AI is vital in emergency situations, even if the car’s software lacks emergency response capabilities.

Thanks to the present technological progress, we can now realise the complete potential of our customised tools and devices designed to meet our expectations. However, it is not solely speed that is of significance; incorporating Edge AI would also be advantageous from the perspective of security and privacy, as it diminishes the frequency of connections linking a device located at the network’s edge to the central server, enabling local storage of data.

Undoubtedly, centralised cloud computing holds distinct advantages, particularly in terms of scalability. Nonetheless, with the onset of 5G devices and the Internet of Things, the sheer volume of data is growing exponentially, and centralised systems may soon become unmanageable. Establishing the requisite connections to manage this data could prove challenging and expensive to implement in time. No doubt, network latency, the delay between request for data transmission and actual transmission, is not a concern for scenarios that require quick processing.

With the evolution of technology, more and more professionals recognise the growing importance of Edge AI in software development, machine learning, internet of things, and other digital products. Achieving this necessitates a fundamental transformation in how these stakeholders approach development, primarily centring on the end users, and not just the product objectives. This change has immense potential implications, potentially cultivating a future in which individuals and their devices engineered to assist them are optimally attuned to their specific needs and preferences.

Merely a Question of Time

The outbreak of the COVID-19 pandemic has thrown into sharp relief the need for unconventional remedies for longstanding problems. We firmly believe that incorporating Edge Artificial Intelligence could be a valuable and effective approach to explore and integrate into our routine lives. Its deployment could have a favourable impact on numerous sectors, including healthcare, security, and monitoring social distancing. Implementation of Edge AI would enhance our comprehension of the virus’s spread and treatment methods, whilst bolstering global citizen safety. Our vision is to create a future where Edge AI is widely accessible and leveraged for our collective benefit, and we are fully committed to realising this goal.

Lamenting missed opportunities is futile. Instead, we must acknowledge the potential benefits of Edge AI and commence outlining methods to incorporate it into the devices we currently manufacture. Our focus should be to identify user-centric applications that guarantee the high levels of privacy, security, and speed inherent in Edge AI. With its inevitable integration on the horizon, it is our duty to prepare for it.

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