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IoT And AI: A Powerful Evolution For Future Generations!

The Internet of Things, combined with artificial intelligence, will be an evolution in the future of humanity. Here is why you should pay…

Opinion

The next revolutionary period of the world is right now. A period that will be as significant as the Renaissance and the Industrial Revolution.

Artificial Intelligence and the Internet of Things are two such prospects people believe will modernize the world into something that will blow our minds in the next few decades. These two pillars have a gigantic scope in the future.

Both Artificial Intelligence and the Internet of Things are not the rising trends, but they also contribute towards a new emerging technology that holds the power to modernize the perspective of the world in the upcoming years.

In this article, we will aim to understand the basic idea of what we can expect from AI and IoT by analyzing both these concepts individually. We will also discuss a few ways on how to start utilizing these integrated technologies together to develop innovative and creative projects. And finally, we will analyze a way to make the code more compact and effective for deployment in embedded devices.

What is IoT?

The Internet of things (IoT) describes the network of physical objects – "things" – that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the Internet.

The definition of the Internet of things has evolved due to the convergence of multiple technologies, real-time analytics, machine learning, commodity sensors, and embedded systems. Traditional fields of embedded systems, wireless sensor networks, control systems, automation (including home and building automation), and others all contribute to enabling the Internet of things.

In the consumer market, IoT technology is most synonymous with products pertaining to the concept of the "smart home", including devices and appliances (such as lighting fixtures, thermostats, home security systems and cameras, and other home appliances) that support one or more common ecosystems, and can be controlled via devices associated with that ecosystem, such as smartphones and smart speakers.

What is Artificial Intelligence and AIOT?

The subject of artificial intelligence is humungous similar to the massive milky way galaxy. Artificial Intelligence (AI) is a broad field with many sub-categories such as natural language processing (NLP), artificial neural networks, computer vision, machine learning, deep learning, robotics, so on and so forth. The formal definition of AI is –

"The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages."

According to a study by McKinsey Global Institute, AI is estimated to create an additional 13 trillion US dollars of value annually by the year 2030. Even today the artificial intelligence technologies are generating a tremendous amount of revenue, but it is mostly in the software field.

However, by 2030, the revenue that will be generated will be outside the software industry, especially in sectors such as retail, travel, transportation, automotive, materials, manufacturing, and so on.

The combination of artificial intelligence combined with the internet of things forms a new, interesting, and unique branch of study called the Artificial Internet of Things or the AIOT in short. IoT enabled with AI is capable of creating intelligent machines that can simulate smart behavior while supporting decision-making ability with little or no human interference.

With the integration of artificial intelligence in embedded IoT devices like raspberry pi and Nvidia Jetson Nano (Among many others) are capable of developing some masterpieces, which will be highly profitable and beneficial to society as a whole. Some examples of virtual assistants like Alexa, Siri, or Google AI show the high-level intellect and Future possibilities.

So, if you are curious and want to know how you can get started, then my suggestion would be to move up the order with these devices.


How can you get started?

In this section of the article, we will discuss in detail related to the various ways you can get started with AIoT. This guide can be especially useful for beginners who are trying to get started with the beauty of utilizing AI in embedded devices to create spectacular real-life projects.

So, without further ado, let us start analyzing three of these devices that can offer you a great starting point for developing IoT and AI projects.

1. Arduino

The Arduino is a development board that consists of an ATmega Micro-controller. It is one of the best ways to get started with robotics and IoT (Internet of Things) projects.

Arduino is an open-source hardware and software company that can be used by hobbyists, tinkerers, and professionals to build amazing innovative projects. From my experience, Arduino is definitely one of the best ways to get started with your robotics dream, as it is comparatively easier to use than other micro-controllers.

The Arduino comes in many forms and sizes, namely Arduino Nano, Arduino Uno, and Arduino mega. The Nano is a smaller sized board which can be used for more simple and unique projects. The Uno is a medium-sized board perfect to start experimenting and trying out hobbyist level projects. The mega is a bigger development board which can be used for slightly more complex projects and scenarios.

In my opinion, the Arduino is the best way to get started with any type of IoT projects. With some basic projects like sensor controlling and learning device management with Arduino is highly beneficial for pursuing more creative ideas in this field.

It is simple to start learning as it is mostly a mix of programming languages like C and C++, and it has mainly two code blocks to worry about as a beginner, namely, setup and loop function blocks. Hence, my starting suggestion would be to try out the Arduino Uno board before proceeding to more complex embedded devices.

2. Raspberry Pi

The Raspberry Pi is a single-board computer, which is a fantastic way to get started with computing and programming. The Raspberry Pi offers lots of opportunities to create extremely cool projects in branches like computer vision, gaming, IoT projects, and so much more.

With a camera attachment, the raspberry pi can even be used for tasks like object detection, face recognition, and surveillance operations. If you are a beginner who wants to get started with programming and coding, then the Raspberry Pi is the cheapest and best approach. It can also be used by intermediate-level hobbyists or experts for more advanced projects.

The best part about programming with the Raspberry Pi and an operating system like the Raspbian OS is that you can use a variety of programming languages, including python. The Thony Editor comes as a pre-installed program in the OS, and you can code your python projects here.

All the programs that are coded in the Raspberry Pi, including Machine Learning and deep learning programs, can be deployed easily. External attachments like cameras, audio devices, etc., can also be added to the Raspberry Pi and be controlled to perform real-time computer vision tasks such as Video Surveillance, face recognition, etc.

3. Nvidia Jetson Nano

The NVIDIA Jetson Nano is one of the best tools for artificial intelligence related operations in robotics. It is slightly more expensive than the Raspberry Pi, but the Jetson Nano also has higher computation power. According to NVIDIA:

NVIDIA Jetson Nano enables the development of millions of new small, low-power AI systems. It opens new worlds of embedded IoT applications, including entry-level Network Video Recorders (NVRs), home robots, and intelligent gateways with full analytics capabilities.

The NVIDIA developer kit allows users to run many neural network operations, including image classification, segmentation, object detection, and speech processing, among many more.

Although the Nvidia Jetson Nano is a bit more expensive than the Raspberry Pi, it has comparatively better features and is also a great starting point for developing intermediate and advanced level projects. It is powerful and can perform a wide array of tasks, as mentioned previously.

These are some of the best devices to get started with AIOT, in my opinion. There are obviously so many other awesome options and embedded device choices to develop effective models as well.

The final significant topic I want to touch upon in this article is a crucial one. The next section of the article will cover the Post-training Quantization of your machine learning and deep learning models to run complex programs with lesser GPU capacities on embedded devices.


Post-Training Quantization:

A model that runs effectively on your system might not be able to run the same program/model effectively on a lower-end device. This could be due to the hardware constraints of the target device. Here, is where post-training quantization can help improve in the optimization of the algorithms and models for the target device.

Post-training quantization is a conversion technique that can reduce model size while also improving CPU and hardware accelerator latency, with little degradation in model accuracy. You can quantize an already trained float TensorFlow model when you convert it to the TensorFlow Lite format using the TensorFlow Lite Converter.

The TensorFlow Lite Converter is very useful on devices such as the Raspberry Pi for optimization of object detection models, face recognition models, etc. The object detection projects can be optimized using TensorFlow Lite up to a great effect on android or ios devices as well. You can check out this cool project for this topic here.

While exploring these models, if you do want them to be converted to real-life use cases where it can reach as well as benefit a large number of people, then post-training analysis and post-training quantization of models becomes extremely important to improve the efficiency, quality, and compactness to deploy the projects to a wider audience.

The post-training quantization also enables us to achieve almost identical accuracy on the quantized model similar to the accuracy obtained in the original model. This simplifies and makes our life a lot easier!


Conclusion:

Artificial Intelligence, combined with the Internet of Things, as discussed in this article, is extremely powerful, and we can develop and create unique projects with innovative ideas. The scope of the IoT and AI is tremendous, and these technologies have the power to change the landscape of the future.

The impact of AIoT in the upcoming years will be mind-blowing and the new inventions to be made in the future excites me. Let me know what you guys feel about the emerging AIoT trend and what cool projects you are aiming to build.

If you have any queries related to the topics discussed in various sections of this article, then feel free to let me know. If you want me to write another article that goes more in-depth on how you can get started with each of the devices mentioned or any other recommendations, then feel free to comment below.

Check out some of my other articles that you might enjoy reading!

Beginners Roadmap To Master Data Science

3 Ways To Utilize The Power Of Artificial Intelligence For Your Marketing Today!

10 Awesome Real-World Applications Of Data Science And AI

5 Essential Steps For Every Deep Learning Model!

Understanding ReLU: The Most Popular Activation Function in 5 Minutes!

Thank you all for sticking on till the end. I hope you guys enjoyed reading this article. I wish you all have a wonderful day ahead!


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