AIoT: From Interconnected Sensors to Intelligent Physical Systems

AI and IoT are often thought of as separate technologies. IoT connects physical objects and captures data while AI analyzes data and identifies patterns to make decisions.

When combined, the two offer something far more interesting: systems that can understand what is happening in the physical world and act on it intelligently.

This is the basis of AIoT, or Artifical Intelligence of Things.

Why IoT Data Is Often Insufficient

IoT deployments can often capture a significant amount of data from sensors, equipment, vehicles, inventory, cameras and other connected devices.

The challenge with systems like this is that data capture is only one part of the equation.

A system may be able to capture information about:

A temperature increase in a machine.

Movements of an asset from one location to another.

Inventory fluctuations.

An abnormal operation in a vehicle.

A certain measurement crossing an acceptable threshold.

The challenge is to use such information in a meaningful way. AI can help analyze such data streams to find patterns, detect anomalies, predict future outcomes and help make decisions.

This is the basis of an AIoT architecture:

Physical World --> Sensors & Identification --> IoT Infrastructure --> Data --> AI --> Decisions --> Physical Actions

The Value in Combining AIoT

The value in the combination is that it becomes possible to perform meaningful operations in an industrial environment.

1. Predictive Maintenance

Interconnected equipment can generate data about its operating state. By using AI to find patterns in such information, it becomes possible to spot abnormal conditions and proactively investigate the cause.

2. Industrial Asset Visibility

Using identification and sensing technology, connected systems can capture information about assets and where they are located, as well as how they are being used. AI can help make sense of such data and provide insights across large-scale operations.

3. Inventory & Logistics

Warehouses and large-scale logistics environments capture huge volumes of physical data about the movement of objects. Using AIoT systems, it's possible to connect inventory, location information, equipment and logistics data to help organizations make decisions about moving inventory.

4. Workforce Safety

Sensing systems and connected devices can capture information about the industrial environment and objects in it. AI can help find patterns in such information to help make sense of situations that could pose risk to workers.

The critical difference is that such systems are not simply capturing information about the world. They're using it to transform it in to useful information about how the world can operate.

The Promise of Physical AI

This is where the interest in Physical AI becomes especially compelling. Traditional AI primarily works with information in a digital world. Physical AI works with systems that operate in the physical world.

That means it's no longer sufficient to simply design an AI model. Designers must instead think about identification, sensors, connections, data capture, AI, actions, verification and authorization in such systems.

Aperture Venture Studio focuses on the intersection between AI and IoT and the opportunities with systems that connect identification, sensing and AI processing. For engineers and developers, this area represents an intriguing opportunity to think about new ways that AI can be applied in an industrial environment. The focus is no longer on finding ways to get AI to produce some interesting output. The focus is on building systems that can reliably take observations of the physical world and use them to make meaningful decisions and take appropriate actions.

Engineering AIoT Systems

Designing AIoT systems is not simply a matter of plugging in an AI model to a set of IoT systems. There are numerous challenges. Sensor information can often be noisy. IoT devices may become disconnected. Industrial equipment often communicates across multiple standards, and some systems may have been in operation well before modern forms of AI were available.

Latency, security, reliability, data integrity and integration become important considerations when designing such systems.

An effective AIoT system will need to reliably connect multiple layers:

Sensing --> Connectivity --> Data Infrastructure --> Intelligence --> Applications --> Actions

Each of these elements will need to be reliable enough to support the rest of the system in order to provide useful insights.

The Future Is In Physical AI

Some of the most compelling applications for AI are likely to involve systems that work in concert with the physical world, rather than being limited to the digital realm. Industrial systems such as manufacturing, logistics and inventory, energy, infrastructure, transportation and workplace safety all generate vast amounts of insight about how the world operates. AI can help reason over such information to find patterns and make decisions.

IoT connects physical objects, while Physical AI takes the concepts of AIoT and extends them further to help systems that operate at the edge interact with the physical world.

For developers, opportunities are forming at the intersection of AI, IoT and data engineering. Similar opportunities are also forming with edge computing and robotics, and the convergence of these technologies with industrial software.

The future of AIoT may involve less focus on getting AI everywhere and more focus on making systems that can genuinely make the world better.

For additional background on the approach taken by Aperture Venture Studio, see Aperture Venture Studio.