Artificial Intelligence

Understanding the future of embedded AI networks

6th June 2024
Paige West

Consider face-ID, which allows access to machine controls on a factory floor. It is obvious why using AI in embedded applications is appealing.

AI has several applications, such as voice control, facial recognition, and anomaly detection. Face-ID will be used as an example in this post. It’s much more intelligent, robust, and intuitive than traditional passwords and UIs for humans and machines. Plus, all the other people are doing it. Though AI's workings may seem enigmatic, what it is capable of is rapidly becoming commonplace. No one wants to use antiquated technology to evaluate things candidly.

Since AI improves the intelligence and capacities of IoT devices, the worldwide AI industry is integrating IoT more. These gadgets don't need centralised Cloud servers or a constant Internet connection to make judgments because they can process and analyse data locally. According to a recent study, the average American home currently contains 21 connected devices, based on examination of 41 million houses and 1.8 billion connected devices.

Economic opportunities are expanding since there is a growing demand for processors that are more powerful, energy-efficient, and capable of handling complex artificial intelligence algorithms. AI algorithms are becoming more sophisticated and resource-intensive. Consequently, there is an increasing demand for processors that can effectively handle these demands. High-performance CPUs, GPUs, and specialised AI accelerators are examples of processors that are in higher demand. These processors create chances for embedded AI solution providers to offer cutting-edge technology. In order to leverage the expanding market need and deliver high-performance embedded AI solutions that satisfy customers' changing needs, suppliers ought to concentrate on creating advanced processors, Edge computing capacities, energy-conserving solutions, and alliances.

The market adoption of these solutions will be expedited by the growing demand for improved technology that can provide clients with customised and adaptable experiences. AI is becoming a part of many embedded systems since personalised experiences are needed. By utilising these solutions, devices and software are able to evaluate user information, preferences, and actions in order to provide personalised recommendations, suggestions, and reactions. As such, there is an increase in user pleasure and engagement. Through enabling devices and systems to comprehend user preferences, adapt to changing circumstances, make educated judgments, and provide personalised experiences, these solutions eventually increase consumer satisfaction and encourage market expansion.

Embedded AI systems frequently operate on resource-constrained hardware with limited memory, processing power, and energy. Inadequate processing resources may limit the performance of AI systems, leading to longer inference times, lower accuracy, and a less satisfying user experience. When computational limitations make it impossible for AI models to run on embedded devices, adoption of these solutions is hindered since the models might not meet the performance requirements of the intended applications. As the market continues to grow in these areas, overcoming these challenges will enable the deployment of more powerful and effective AI applications on resource-constrained devices and accelerate the acceptance of embedded AI solutions in several domains.

Asia Pacific is expected to hold a significant market share by 2036. One major factor driving embedded AI is the retail industry's digital revolution and the fast expansion of e-commerce. It is estimated that e-commerce in Asia Pacific will generate $3.5 trillion in sales. Embedded AI technologies provide intelligent infrastructure, effective energy management, and improved public services as nations invest in creating technologically sophisticated and sustainable cities.

To sum up, the investigation of artificial intelligence in marketing could involve two distinct approaches: ‘Applied AI’ to utilise external data sources and ‘Embedded AI’ to improve in-platform functionality. Marketers need to understand how AI is essential to establishing their company's presence in digital marketing as Google continues to progress its use of AI in ad tech solutions.

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