The relatively recent combination of AI and IoT into the artificial intelligence of things (AIoT) has added a level of actionable insight to the traditional IoT.
The internet of things, a system of interrelated computing devices and machines that can transfer data over a network without human interaction, has been used to enable new features, better functionality, and real-time status monitoring for consumers. Combining this with ever-developing AI advancements is allowing organizations to predict changes and optimize their devices. AIoT allows an algorithm to improve communication and apply predictive capabilities to give companies advantages over their competition.
What is AIoT?
AIoT is the addition of artificial intelligence to the devices within the internet of things your organization deploys. The addition of AI and machine learning allows for machines to make a variety of decisions.
“AI is utilized to deliver more capabilities for the connected device, by moving from purely programmed/algorithmic responses to dynamic decisions, empowered by machine learning,” said Tolga Tarhan, CTO at Rackspace Technology, an information technology and services company based in Texas.
Replacing programmed responses with the adaptability and flexibility of machine learning allows for more organizational readiness, since a combination of AI and IoT allows an organization to improve their decision-making and reduce latency by having their interconnected devices gather data and better understand their needs and weaknesses.
Oftentimes the intersection between the two, where AI systems use sensor data from IoT devices, occurs at the edge of the network where AI processes the data at each device. Therefore, its use cases are more likely to be found in individual devices spread throughout a home or factory.
Applications of AIoT
The applications of augmented AIoT cuts across end-user computers and personal devices to enterprise machinery. In the customer space, the combination of AI and IoT has found a home in smart appliances, security cameras and home management systems like the thermostat. By connecting these devices together and adding a level of intelligence, product users can better understand what their homes need.
Justin RichieData science director, Nerdery
Smart home surveillance cameras apply AI to decide what is worthy of being sent to the cloud. Image detection software can identify a normal setting, and only send, alert and store video that introduces something new, i.e., aperson or a tree falling, that is out of the normal scope of the picture. Video streams require large bandwidth and having cameras decipher what is significant can reduce the drag on the system as well as save time for the viewer. Smart appliances can understand when supplies (certain food or drink products) are low and alert the user via connected devices.
AIoT has also enabled the advancement of self-driving technology. By increasing the communication and intelligence within a vehicle, AIoT allows for better decision-making by the vehicle itself.
“Tesla is a great example — where self-driving technology is dependent on this type of AIoT,” Justin Richie, data science director at the digital consultancy Nerdery said. “Not only is AI leveraging computer vision, but the next stage is using the AI to help make decisions that drive the IoT.”
AIoT applications also reach over to the industrial side of the economy. Factories need numerous technical devices that require IoT in order to function properly, but the addition of predictive analytics technology means that companies can avert — or at least plan for — outages, system failures or maintenance shutdowns — something that becomes pivotal for system maintenance.
“In the industrial space, we’re seeing a reliance on predictive maintenance and predictive failure in industrial factories,” Tarhan said.
In industrial high-pressure situations, algorithms can assess when a pump is likely to fail and alert workers beforehand, giving the factory time to get ahead of a failure and apply maintenance to prevent a shutdown.
Combining AI and IoT also allows organizations to collect data from millions of IoT devices in a more organized and efficient manner. AI-based algorithms sort out and eliminate useless data for your organization and save time and costs.
Advantages of AIoT
This variety of applications and flexibility is what comes from the combination of two powerful technologies. The advantage of AIoT is in its ability to promote and provide actionable options. The internet of things can give you information on your devices but with the addition of machine learning algorithms, organizations can predict decisions and outcomes, potentially allowing IoT to make decisions for itself in the future.
The impact of this technology can be measured through its wide-ranging use case examples. Experts note that the technology’s ability to span enterprises and personal use marks it as longstanding.
“Thirty years ago, it would be inconceivable to have the same technology in your home as is used in industrial applications,” Tarhan said.
And AIoT will continue to spread into industries and expand its use case portfolio.
“Robotics will greatly benefit from this as the IoT device management grows into more AI concepts,” Richie said. “As devices begin to interact with humans and the experience grows in sophistication, AIoT will be much more involved.”
Devices have become more prominent and the demands on them continue to grow. AI and IoT are natural allies with complementary skills and their cooperative application will continue into the future.