Real Cases of AIoT Devices

When artificial intelligence is combined with the Internet of Things, we get something called Artificial Intelligence Internet of Things (AIoT). The main reason for combining artificial intelligence with the Internet of Things is that although the purpose of Internet of Things devices is to collect all data and transmit it to the usual cloud or any other space, in these spaces, artificial intelligence is considered to be the brain of AIoT. It will actually make a decision or simulate the action or response of a machine.

1. Traffic management

Traffic is an annoying problem in urban areas, and people always need effective traffic management to avoid congestion. Traffic management done by humans can sometimes lead to chaos. However, AIoT provides a clever solution to this problem. Now, the use of drones can effectively manage real-time traffic. The drone can monitor a large area and transmit traffic data, and then use AI to analyze it to make the final decision such as human intervention, to adjust the traffic lights.

More read: Intelligent Transportation in 2021

2. Self-driving cars

The self-driving car is another case of IoT devices embedded with artificial intelligence. Tesla’s self-driving car is a great example. With the help of installed sensors and artificial intelligence, the car can make human-like decisions by determining the conditions of the surrounding environment. For example, they can make effective decisions by determining the best speed, weather, and road conditions.

More read: Introduction of Giant Self-driving Companies Tesla

3. Smart home

The integration of the Internet of Things and artificial intelligence has also led to the emergence of the concept of the smart home. When a smart home connects all devices through the Internet of Things, these devices have the ability to make intelligent decisions with the help of AI. Smart homes can make our lives easier by giving us the ability to remotely control equipment. For example, we can decide in advance when turning on the TV or calling the fire department in the event of a fire. Even if we are not at home, we can turn the appliances on or off as needed.

More read: What the Future Home Looks Like?

4. Human body sensor

Maintaining health is a major challenge people face today. Due to busy work, it is difficult for a large number of people to see a doctor from time to time for regular check-ups, but this problem can also be solved by wearable devices such as fitness trackers, which help track blood sugar, heartbeat, cholesterol levels, etc. Thus it contributes to health management. Construction companies can also use these sensors to detect the posture of workers to avoid any injuries while at work.

More read: How Data Labeling Service Empowers Security Industry?

5. Manufacturing robots

The manufacturing industry also uses robots in the manufacturing process. These robots are just another form of AIoT device. They help improve the manufacturing process by saving time and cost. One example is eyeglass. Manufacturers can use robots to make high-precision lenses.

More read: How Data Labeling Services Empower Manufacturing Industry 2021?

6. Intelligent buildings

Another intersection of the Internet of Things and artificial intelligence is smart office buildings. Not only residences but also the entire building can be installed with AIoT to improve efficiency and management costs. For example, some companies have installed a network of AIoT devices in their buildings. These devices can detect the presence of people and adjust the temperature accordingly, or shut down the device without human interaction, thereby improving energy efficiency and ultimately reducing costs.

Customized Labeled Dataset

From the perspective of the research direction of artificial intelligence technology, whether in the field of traditional machine learning or deep learning, supervised learning based on training data is still a major model training method. Especially in the field of deep learning, more labeled data is needed to improve the effectiveness of the model.

At present, the demand for the highest quality AI training data in various industries is urgent. AI is implemented in various fields, such as education, law, intelligent driving, banking, and finance, etc. Each field has requirements for subdivision and specialization.

With the acceleration of the commercialization of AI and the application of AI technologies such as assisted driving and customer service chatbot in all walks of life, the expectation of data quality in the special scenarios is getting higher and higher. High-quality labeled data would be one of the core competitiveness of AI companies.

If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create the unique one.

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End

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source: https://baijiahao.baidu.com/s?id=1713944602772076382&wfr=spider&for=pc