Four common applications of AIoT

We are rapidly entering an era where smart machines and big data collection capabilities become an important part of global industries. Today, the shadow of AIoT (artificial intelligence for the Internet of Things) applications can be seen in most high-end residential areas and large enterprises. Although not as widespread as advertised in the media, adoption is growing exponentially in developed countries.

The Internet of Things, as an umbrella term, represents a network of connected objects. When we combine artificial intelligence algorithms and machine learning capabilities, we can expect IoT smart devices to learn, self-improve, and adapt to change based on their experience with themselves and other systems.

AIoT, or Artificial Intelligence of the Internet of Things, is the integration between IoT infrastructure and artificial intelligence technology. The convergence between IoT and artificial intelligence brings enhanced human-computer interaction, data management and analysis, and a new customer experience for IoT users.

The following are the most common AIoT applications today

• Self-driving cars

Self-driving cars Probably the most obvious and widespread application of AIoT today. Self-driving car manufacturers like Tesla use their cars currently on the road to collect millions of data points while driving manually and autonomously. They use this data to map roads, optimize the vehicle's autonomous driving system, and enrich its data pool to develop better vehicles in the future.

• Manufacturing

Today, the biggest adopter of IoT and AIoT is smart factories to use artificial intelligence to improve their performance and efficiency . Most factories in highly industrialized countries already use embedded sensors to collect various data in the manufacturing process.

Industrial and manufacturing robots are becoming increasingly smarter through the use of AIoT, which allows factory robots to learn patterns and predict disruptions, delays and damage before they occur. Predictive maintenance may be the best way to save money for any manufacturer.

• Retail

Data collected by facial recognition cameras and other sensors can help map the paths customers take as they move around a store. Data such as this can help managers predict the occurrence of staffing shortages and provide insights into in-store customer behavior to improve the retail experience and even plan store layouts to make the most of customer visits.

• Smart Thermostat

This is where modern applications of AIoT meet the smart home. Automation and AI-powered temperature regulation can maximize savings on energy bills, and it can also adjust the home's temperature over time based on the weather outside, the homeowner's work schedule and temperature preferences.

In a fully realized AIoT future, the Earth will become a vast interconnected network composed of countless data collection systems that will autonomously improve each other by learning patterns and making connections. We look forward to this day coming soon!

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