AIoT – The role of Artificial Intelligence in the Internet of Things

Artificial intelligence (AI) and the Industrial Internet of Things (IIoT) are two of the hottest buzzwords dominating the “Industry 4.0” conversation.

But–when people discuss the benefits of IoT, they’re often referring to the benefits offered by the artificial intelligence of things (AIoT).

True “digital transformation” comes from that critical combination of AI’s intelligence and IoT’s ability to generate, capture, and store tons of data.

What is the Artificial Intelligence of Things?

AI-powered IoT applications, dubbed, AIoT or the Artificial Intelligence of Things, represent a broad range of applications that leverage both artificial intelligence and the internet of things (IoT).

Before we dig into what that all means, let’s quickly establish the role each plays, as it will help paint a clearer picture of the symbiotic relationship between the two technologies.

• Internet of Things. The Internet of Things refers to a system that extends the internet to various objects, sensors, and devices (things) so that they can collect and share data from their environments using other devices or software programs. Essentially, IoT aims to connect machines and objects.
• Artificial Intelligence. Artificial Intelligence, or AI, describes a system capable of learning from data or performing tasks typically associated with the intelligence found in humans and animals. AI technologies include machine learning (ML), natural language processing (NLP), voice & face recognition, and deep learning. Put simply, AI brings intelligence to machines and objects.

Together, the two technologies create intelligent, connected systems, where AI functions as “the brain” to IoT’s “body.” IoT devices collect and transmit data from multiple sources–supporting the “learning” process involved in training AI to carry out automations.

AI brings machine-learning and decision-making power to IoT systems, enhancing data management and analysis, and enabling massive productivity gains.

Bringing the Power of AI to the Internet of Things

Many insist that “there is no internet of things without AI.” However, that’s not necessarily an accurate statement. Technically, IoT is about data collection. The idea is, organizations should have access to more data about their productivity, products, and processes so that they can make more informed decisions.

Earlier versions of today’s connected systems were powered by cloud computing, which offered the computing power, connectivity, and storage needed to support machine-to-machine (M2M) communications. Frameworks like Spark or Hadoop were initially used to process data, which allowed business leaders to analyze patterns, trends, and correlations between connected machines.

The problem is, IoT technology has outgrown the old arrangement. Sensors can now be applied to everything from smart meters to drones, farm equipment, and entire fleets.

In AIoT applications, AI is embedded into various components, like edge computing, chipsets, and software, and is interconnected with IoT networks. Typically, systems rely on APIs to ensure interoperability between all connected components. This allows the AI to optimize processes and extract valuable insights from the unstructured data provided by the connected IoT devices.

Why is Artificial Intelligence Required for IoT?

The short answer is, it all comes down to data. The business value of IoT comes from its ability to turn almost anything, from Mack trucks and medical devices to drones and prescription drug use, into a data point.

However, without AI baked into the system, It’s not possible for humans to put all of that data into action. According to PwC, AI is rapidly converging with IoT, to the extent that intelligence is on track to become a requirement of connected systems.

The reason for this is two-fold:

For one, AIoT enables real-time analysis and response. So, where IoT systems can collect and organize data without AI, AIoT systems take things several steps further. Because IoT platforms offer an interface for collecting data from all of these devices, those insights can easily be analyzed and put to use with AI/ML systems. AI can detect anomalies, failures, and security threats in real-time–and, in many cases, are programmed to react.

Second, AI also powers long-term analysis, allowing users to identify patterns in historical data to spot trends that occur over long periods. AI’s complex algorithms allow enterprises the ability to run predictive analytics based on a variety of possible scenarios, simplifying the problem-solving process for human users. That ability will enable organizations to analyze and respond to risks, make changes to operating parameters, and avoid unplanned downtime in real-time, allowing businesses to gain a competitive edge.

Artificial Intelligence-Driven IoT: The Impact on Various Industries

According to an IDC report, AIoT: How IoT Leaders are Breaking Away, researchers found that regardless of industry, heavy use of AI was a significant predictor for whether the IoT transformation would live up to its high expectations. 90% of heavy AI users reported far more ROI than what they had anticipated.

It’s worth mentioning, that while certain AIoT applications are designed for industry-specific use cases (i.e., predictive maintenance for manufacturing companies, smart shopping carts for retailers, AI-driven medical imaging in the healthcare space), the core benefit of AI and IoT in one application is data processing.

Applications of Artificial Intelligence in the Internet of Things

Most real-world applications of AI and IoT are used to prevent downtime, streamline operational processes, and make sense of the growing amount of data generated from IoT devices.

Here are some examples of AIoT applications being used today:

• Edge Computing. Edge computing involves processing high volumes of data at high speeds, enabling users to make decisions locally, without sending data to the cloud. Where IoT devices collect the data, it’s AI/ML that enables decision-making at the edge.
• Collaborative Robots (Cobots). Cobots represent the fastest-growing segment in industrial automation. They use a combination of IoT sensors and AI-ML models that give robots a sense of perception and environmental awareness that allows them to “make decisions” and work safely alongside human collaborators.
• Digital Twins. Digital twins are virtual replicas of physical objects that enable users to run simulations before deploying actual equipment and devices. For instance, you might use digital twins to test a new engine or wind turbine before sending the design to production.
• Autonomous Delivery Robots. While autonomous delivery robots may well fall under the “cobot” label, the use case is a bit different here. In this case, AIoT technology moves outdoors to address the last-mile delivery problem, reducing shipping costs and increasing delivery speed—a key factor in driving customer satisfaction.

What’s Next for AI and IoT?

Big data without intelligence isn’t enough.

While IoT without AI is possible, early solutions can’t keep up with the amount of data generated by the various sensors, devices, and machines that make up the industrial IoT ecosystem.

As IoT sensors and devices become more affordable, more organizations will adopt these new solutions, hoping to cut costs, drive revenue, or improve processes. So, as companies begin to add intelligence to the production line, the supply chain, and so on, it immediately becomes impossible for humans to sort through that information, much less extract anything that can be put to good use.

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