A Starter’s Guide To Edge Artificial Intelligence

Talking about things we love, everyone loves their morning drink – whether it's coffee, chamomile tea or orange juice. Imagine you have a smart refrigerator with a dispenser that serves custom drinks for each member of your family. Based on users’ preference, the room temperature and other factors, this refrigerator can serve piping hot coffee or a chilled glass of fruit juice – all on its own.

Heck, it even learns your taste and liking as time goes by (thanks to Artificial Intelligence). For example, if you substitute coffee for lavender tea on Sundays, the AI algorithm will pick up on that. Like most smart devices, this system is connected to the Internet – we bet you’ve heard of the Internet of Things!

Now, imagine what would happen if your Internet crashed! No more custom drinks – that’s a recipe for a ruined morning! Wouldn’t it be awesome if we could store the user data, AI algorithms and processing apparatus within the smart refrigerator itself?

Thanks to Edge AI, you can convert any machine into an offline, standalone smart device that can operate without connecting to the cloud, Internet or data centers.

If that sounds magical, read on to explore all about Edge Artificial Intelligence; its history, working, importance, benefits and future!
 

What Is Edge Artificial Intelligence?

We bet you already know what Artificial Intelligence is – however, in a nutshell, it is a field of computer science that helps develop intelligent machines that can think, learn and act like humans. The other star of Edge AI is edge computing; a distributed computing approach that processes data near the data source, instead of sending it to a centralized server or cloud.

The basic idea behind Edge AI is to make edge nodes or devices capable of independent decision-making. Artificial Intelligence helps devices become smart, while edge computing helps smart devices make quick decisions independently and offline – together, you get a perfect couple in the form of Edge AI!

Time for a flashback to find out how Edge AI came about!
 

Evolution And Origins Of Edge Artificial Intelligence

The origin of businesses exploring the idea of implementing edge computing traces to the 1990s. During this time, Content Delivery Networks were adopted to quickly serve web content to users from the closest edge servers.

The evolution of edge networks in the early 2000s led to the first commercial edge computing services being used for hosting more than content – think web applications and data. Later, businesses started deploying and scaling edge nodes to create a distributed network.

Around 2019, edge computing became a popular solution to tackle latency in processing and analyzing high data volumes. The implementation of advanced processing units within devices and sensors made local computation feasible.

The concept was soon expanded to smartphones, smart devices and network gateways. At the same time, thanks to the growth in AI innovations, it was being integrated into various applications. As most AI applications were developed using hard-coded rules, it was possible to replicate the same approach for edge nodes and devices.

In the early 2020s, businesses explored the integration of AI into edge devices for faster, optimized and more actionable decision-making at the edge. Generally, AI algorithms were trained beforehand and hard coded into the edge hardware to create what we now call Edge AI.

Now, let’s answer the burning question: how does Edge AI even work?
 

How Does Edge Artificial Intelligence Work?

Most devices today are capable of exchanging data with other devices; for instance, your smartphone connects to your Bluetooth headsets, smartwatch, smart TV, etc. All this data going back and forth significantly burdens data processing facilities. Edge computing eliminates this strain by bringing processing closer to the data source, rather than sending it to a centralized cloud computing facility or an offsite data center.

Furthermore, the use of Artificial Intelligence speeds up computations. As the information is not sent to a central processing unit, localized processing can make decisions in milliseconds – even without an internet connection. Essentially, as the edge device generates data, the onboard AI algorithm evaluates it.

Edge AI devices also make use of embedded AI algorithms that monitor the edge device’s behavior and performance by collecting device data. This allows the AI to detect problems in real time and quickly make predictions to optimize the data processing. Edge AI can be deployed on a wide range of edge devices, from central processing units and microcontrollers to advanced neural processing devices and smart gadgets.

That’s impressive, but what’s more impressive is how unique Edge AI is!
 

Edge Artificial Intelligence Is Important For Businesses

What’s the most common complaint in business circles?
 
Yup, no one likes waiting!

The ultimate goal of Edge Artificial Intelligence is simple: to bring computing closer to devices, reducing the amount of time it takes for data to be transferred back and forth. This allows us to preserve the network bandwidth and reduce latency as the volume and scale of data that must travel for processing are majorly reduced. #LessTimeWasted

Additionally, Edge AI enables real-time analytics and insights as the data doesn’t have to travel to a remote processing unit. Yes, innovations in autonomous mobility, robotics and other emerging fields are influenced significantly by Edge AI. In an era where every single millisecond counts towards improved performance, Edge AI helps businesses innovate faster and more accurately. As AI algorithms improve, Edge AI is opening new opportunities to deploy both technologies through a single interface.

However, reduced latency is not the only benefit of Edge AI – here’s more!
 

Benefits Of Edge Artificial Intelligence

Are you ready? Here are the top benefits of using Edge AI:
 

• Intelligent Applications

  AI-powered applications are more powerful and adaptable than traditional programs that can only respond to pre-programmed inputs. An AI can be trained on a certain type of situation rather than a specific situation. So, Edge AI enables applications to process infinitely various input data such as text, audio, or video with flexibility.

• Real-time Action

  As the edge devices analyze data locally rather than sending it to a faraway processing unit on the cloud, long-distance communication is avoided. This means Edge AI systems can identify and respond to users’ demands in real time.

• Lower Transmission Cost

  By bringing the computation closer to the edge (aka the data source), these applications require less bandwidth, lowering the networking costs significantly.

• Increased Data Privacy

  Edge AI can evaluate real-world data such as photos, speech, medical images and other personal information. Hence, Edge AI improves data privacy by processing information locally and uploading only the analysis and insights to the cloud. As a result, the likelihood of data being leaked or stolen in transit is eliminated.

• High Uptime

  Edge AI's decentralized and offline features make it robust as data processing does not require Internet access. Thus, mission-critical, production-grade applications can have increased availability and reliability with Edge AI. #NoNeedForInternet

• Continuous Improvements

  When an Edge AI encounters data that it cannot accurately or confidently process, it uploads it to the cloud for the AI algorithm to be retrained. As a result, the AI algorithm at the edge gets more accurate as more data is collected.

 
Yes, we agree – but perhaps not as wonderful as the future of this technology.
 

What’s The Future Of Edge Artificial Intelligence?

The market for Edge AI has not grown as fast as some experts had hoped. With the explosive adoption of AI tools, such as Generative AI and LLMs, Edge AI will also see key interest from businesses soon. According to Allied Market Research, the Edge AI market was valued at USD 9 million in 2020 but is projected to be worth nearly USD 60 million by 2030. #SixFoldGrowth

Moreover, in 2020, the technology research firm Gartner predicted that 75% of enterprise data will be generated and processed at the edge by 2025. This implies that most business data will bypass the traditional cloud and be processed locally on edge devices. If the prediction comes true, Edge AI will become a part of every business technology stack, especially for organizations working on developing applications such as Cobots, autonomous machines, smart cities, etc.

We’re eagerly awaiting the sequel - When Edge Met AI!
 

Conclusion

 
Edge AI is the “happy marriage” of two technologies that allow us to optimize, improve and automate various routine tasks. It helps businesses run AI models locally on devices which reduces latency and costs associated with transmitting data to a cloud or server for processing. Edge AI enables devices to process and respond to data in real time, leading to more efficient and cost-effective decision-making. We hope this article explains all about this technological power couple!