Embedding Artificial Intelligence In Enterprise

Artificial intelligence (AI) and embedded systems have recently made tremendous advancements that have totally changed how we view the future. Technologies like artificial intelligence, machine learning, embedded systems, and the Internet of Things (IoT) and their combinations are opening up hitherto unexplored vistas in fields ranging from industrial automation to human implants and deep space exploration. The fiercely competitive global markets, which force corporations to innovate and push the envelope to stay ahead of the competition, are partly to blame for this astounding technical advancement.

This article will explain the integration of cutting-edge technologies, such as ES and ML, to create cutting-edge AI solutions and how organizations can profit from them.

Artificial intelligence built-in

One must thoroughly understand embedded systems and artificial intelligence to understand embedded AI.

Electronic Systems

These autonomous systems have been specifically created to perform certain functions using their hardware and embedded software. They can be freestanding or components of a larger assembly.

Artificial Intelligence (AI) 

A computer-controlled cyber-physical system can carry out tasks usually done by humans. It requires mental prowess on par with human intelligence, including difficult cognitive feats, drive, and self-awareness.

Embedded AI could be described as such

The ability of embedded systems or resource-constrained devices, typically isolated, to carry out operations that call for human intellectual capacities is called embedded AI. Embedded AI refers to using AI models and algorithms at the device level to enable independent functionality without outside assistance.

The History of Embedded AI

Embedded systems (ES) and artificial intelligence (AI) have long existed. Their trajectories, however, have been noticeably different. AI struggled to live up to its early promises throughout the latter decades of the 20th century and the first decades of the 21st century. Applications and usability were restricted to very few fields. This was mainly because there weren't enough data scientists and engineers who were experts in this sector, there wasn't enough affordable, high-volume manufacture of the necessary electronic hardware components, and there wasn't enough bandwidth to feed massive data to AI algorithms. On the other hand, ES technology grew steadily and eventually thrived in the twenty-first century. At the forefront of modern technological advancement are AI and ES.

Let's explore philosophy from this angle!

The classic quote "Our need will be the real creator," attributed to one of history's greatest philosophers, Plato, can be translated as "necessity is the mother of invention." Recent research that emphasizes innovation drivers confirms this. There are many problems worldwide, and there has never been tougher competition in international business marketplaces. It is now standard practice to generate new information, use innovation, and use cutting-edge technology. The escalating demand for embedded systems and the anticipated growth in artificial intelligence applications over the next few years have caused the two industries to meet, spawning the exciting new field of Embedded AI. The increased attention paid to privacy, security, and resilience, as well as improved functionality and responsiveness, have all contributed to this convergence. Development engineers and scientists are still required to utilize these advantages at their best, making this a desirable job choice for individuals preparing for their professional journey.

Which is better, AI or embedded?

Why would asking which is better when comparing embedded and AI be unfair? The ironic response might be "to catch your attention," but the real solution lies in differentiating the two and emphasizing how they work best together. An AI model can make better decisions because it learns from the provided data. In addition, embedded systems are tangible objects that use sensors to produce data or information that may be fed to AI algorithms. The better the results, the more trained models there are. As a result, embedded AI becomes a potent solution, particularly for limited devices.

Are AI and embedded systems (ES) related?

As explained in the earlier sections, there is a connection between AI and ES since ES can produce data that AI algorithms can use to continue continuous electronics; autonomous vehicles and industrial sectors all use embedded AI, which has several advantages for customers and enterprises, such as low latency, reduced energy use, and autonomy.

Machine learning embedded

A careful separation between machine learning and AI is necessary since both focus on implementing the best possible business solutions. Applications using machine learning (ML) or ML models are resource-intensive and require powerful computer resources. Because of this, they are frequently run on generally unrestricted devices like PCs or cloud servers, where data processing proceeds without any problems. Nevertheless, it is now possible to install machine learning frameworks or apps directly on embedded devices thanks to recent developments in data science, algorithms, and CPU power. This idea is known as TinyML apps or Embedded Machine Learning (E-ML). Embedded machine learning successfully overcomes challenges, including bandwidth interruptions, data transmission security breaches, and high battery consumption, by moving computing to the edge, where sensors collect data. This is especially important for deep learning since it promotes autonomy and intelligence at the edge and makes it possible to use neural networks, other ML frameworks, signal processing services, model creation, gesture recognition, and other ML techniques.

Applications of Embedded AI for Businesses

Now let's get down to business. The ability of a technology to support societal and/or commercial development determines its success. The same is true for embedded machine learning or artificial intelligence.

Worth Knowing: Current Embedded AI Information

• From 2021 to 2026, the global market for embedded AI is anticipated to develop at a 5.4% CAGR, reaching about USD 38.87 billion.

• The market for AI chipsets was valued at USD 12.04 billion in 2020, and predictions indicate that it might reach USD 125.67 billion by 2028, representing a CAGR rise of 34.08% for the time period under consideration.

• Healthcare, banking and finance, automotive, manufacturing, cyber-security, smart cities, and consumer electronics are the industries most commonly embracing embedded AI.

• Natural Language Processing, machine learning, computer vision, context-aware computing, neural networks, and TensorFlow Lite are key technologies advancing this movement.

• The main forces behind the development of embedded AI are the need for autonomous machines with the ability to reflect on their actions, the rising need for dependable and effective intelligence solutions at the edge, and the desire to minimize human involvement.

• The key obstacles are the lack of highly skilled human resources in this field, expected employment losses, and skepticism from powerful people.

Why should your company incorporate embedded AI?

In any industry, embedded AI or edge AI provides firms various benefits over traditional solutions. We've highlighted a handful below:

Economics

For example, while traditional cloud-based solutions are getting more affordable, they still have high expenses. High costs are associated with transferring data from the device to the cloud and with extra processes after the data arrives there. As the device can analyze data and has the necessary computational capacity to train AI models, deploying embedded AI solutions reduces the need for cloud communication and significantly saves costs.

Bandwidth

AI algorithms require a large amount of data for model training and analysis, requiring a large amount of bandwidth for data transfer to the cloud or data centers. Devices become independent with Edge AI or Embedded AI, requiring little to no bandwidth for flawless functionality.

Privacy

At the edge, sensors and recording equipment produce sensitive data, which raises privacy issues. The risk of privacy violations increases when this sensitive material is transmitted over several internet levels. The likelihood of a violation is considerably decreased by processing data locally and eliminating data transmission, improving the device's privacy control.

Latency

Embedded AI deployment significantly reduces system latency by performing computation locally rather than sending sensor data to a remote location. This is essential for real-world services and applications that need real-time AI solutions. When facing barriers or signal processing system responses, quick responses are crucial for autonomous cars. Rapid reaction time becomes essential.

Reliability

Local data processing devices are less likely to malfunction, which minimizes downtime. This is an essential necessity for sensitive gadgets and specialized tools that users rely heavily on. Embedded AI solutions perform better in this area than traditional AI computer systems.

How can you use embedded AI in your company?

Contacting us and having one of our professionals walk you through the AI development process step by step is the simplest course of action. We have been able to design the best solutions and processes thanks to years of research and development, and we use them to help our clients succeed and meet their business needs. You can benefit from our four pillars of cooperation, which span the phases of need analysis, development, and complete deployment: discovery workshops, user-experience design at the interface, a strong software architecture, and customized solutions that match your company's needs. Our services provide a wide range of cutting-edge tech solutions to help your organization grow, whether hardware or software, ML models, embedded or other devices, neural networks, or deep learning.