April 12, 2024

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Edge AI: Driving Next-Gen AI Application In 2024

5 min read

AI is reshaping the world and how, and 2024 is no different. The integration of AI into different applications and software has become significant with time and a vital part of predictive algorithms.

This technology is an amalgamation of edge computing and artificial intelligence.

It makes tech integration seamless. As a part of the process, you can deploy the apps directly or on servers close to devices, known as Edge AI. You ought to know about the intricacies of Edge AI and how it’ll impact the technology.

What is Edge AI?

Edge AI can implement artificial intelligence technology in the edge computing environment. The integration allows different computations to be done where the actual data is collected rather than in an offsite data center.

Simply put, the technology lets the device make quick and smart decisions faster without being connected to the cloud. Most importantly, it brings the data storage closer to the device location.

Ideally, AI algorithms process this data without an internet connection. It can process data within milliseconds, along with real-time feedback. Apart from a quick data process, the technology allows the responses to be delivered in no time.

Let’s understand how it’s working.

How does the technology work?

It is no rocket science to understand how the technology works. In Edge AI, one portion of the data stored is taken out of the data center and moved close to the data source.

Following this, the Machine Learning algorithms process the data and help analyze the area where this data is generated. This could be a retail store or a smart city.

The point is, you don’t need internet for the process, and the real-time decision takes place in milliseconds. The final output of the process is sent to the data center.

Through Edge AI, the data is closest to the user’s computer, touch point, or IoT device. For instance, when you communicate something to Google, your recording is sent to Edge Network, which sends text through artificial intelligence, and your response is processed.

Moving forward, like any other technology, Edge AI has its share of advantages and disadvantages.

Let’s dive into it.

Advantages of Edge AI:

Edge AI is adept at reducing the bandwidth. As it reduces the data quantity set through the internet, it helps you save money. The best part about the technology is its increased response.

Unlike other devices that wait for data processing and feedback, local data processing with Edge is comparatively quicker. Ideally, it enables quick decision-making and is action-oriented.

Now, coming to the privacy part, Edge AI supports secrecy enhancement. The technology assures that your sensitive data is never compromised.

Apart from helping data owners exercise greater control over shared data, it helps filter out redundant and unnecessary information. Intrinsically, only the critical data is sent to the cloud. The precision of different AI models advances with exposure to the added data, which is also an advantage.

Disadvantages of Edge AI:

A major setback of Edge AI is it lacks the computing power of cloud-based AI. That said, edge-enabled devices can perform specified AI tasks. Also, it may perform on device interference using smaller models.

Edge AI affects storage and cost. Building a new storehouse leads to an increase in the cost of components. In addition, traditional IT infrastructure may need upgradation to manage the edge devices, which again increases the cost.

One of the main disadvantages of Edge AI is data loss. In the process, most devices can discard relevant data, and if significant data is thrown away, it leads to significant losses. So, you must plan data implementation to avoid such situations. Moreover, discarded data can lead to inconsistent analysis.

 Where can you deploy Edge AI?

Embracing new technology impacts business scalability and provides productive solutions by enabling you to process data in real time.

The healthcare sector can deploy Edge AI to radically change treatment plans by improving the monitoring and diagnosis of different diseases. Moreover, you can enhance the retail experience by using Edge AI technology. It can provide customized services and accessibility to end users.

Most cities are using Edge AI technology. For instance, smart cities in New York and Singapore use it to manage traffic and reduce energy consumption.

That said, Edge AI has many possibilities if deployed efficiently.

Next-gen apps of Edge AI:

A step further, brands can use the technology in Web3, Metaverse, and Autonomous vehicles.

Do you know Amazon is already using the technology in its Echo devices? Microsoft is also leading the game with its Azure IoT applications and platforms. Additionally, IBM is using the technology in its Watson IoT platform, which utilizes the technology to make better decisions. Another powerful brand, Bosch, uses Edge AI to automate products. Even Intel plans to integrate with Edge AI for its IoT devices and data centers.

It is quite evident that all big brands have integrated Edge AI to build next-gen applications.

Difference between Edge AI and Cloud AI:

Edge AI and Cloud AI are not the same and the differences are worth taking note of.

Firstly, the processing power of both technologies is different. Edge computing may be tricky to replace and upgrade and is less impactful than cloud AI.

Secondly, the difference is latency. Cloud AI is faster than Edge AI but still not ready for real-time apps. Connectivity is far better in the cloud than in edge computing.

Besides, safety-critical operations cannot stop when there is no internet. Due to onsite data storage, Edge AI provides better security and privacy. Ideally, it may suit the requirements of device authentication.

Future of Edge AI:

The future of Edge AI looks bright as most brands are looking to deploy the technology. Besides, with processors becoming powerful and improving network access, the technology will provide many dynamic opportunities across different sectors.

Companies can leverage Edge AI better with the availability of infra for machine learning and increasing use of IoT-enabled applications. Other than real-time insights, you can leverage the technology for increased privacy.

In the future, edge AI technology may need vendors, which may increase complexity. At times, the technology can be prone to sustainability issues.

Wrapping up

Edge AI has created a revolution in the world of AI, and it can be witnessed in real-world applications. Most brands have already integrated with the technology and the rest are in the process. In 2024, Edge AI will open innovations in robotics, security, communication, healthcare, and many more.

All in all, Edge AI will drive digital transformation in business practices, which will impact many industries. Yet, it completely depends on the company as to what it wishes to do with the technology.

However, integrating with the Edge AI technology will surely give your business a paradigm shift.

AI report :

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