AI applications are typically discussed in terms of models, API, data pipelines, or user interfaces.
When is an AI system required to understand something that is happening in the physical world?
A machine in a factory changes it's vibration pattern, an asset moves to an unexpected location, a camera sees an anomalous occurance, or a warehouse's flow of inventory changes drastically.
In these examples, the AI model is only one component to the system.
The true engineering challenge involves tying sensors, edge hardware, data infrastructure, AI model, and physical processes together into a reliable production system.
This is the basis for AIoT.
What Is AIoT?
AIoT combines AI with The Internet of Things.
A (very) simplistic architecture for AIoT would look like:
Physical Environment
↓
Sensors / Cameras / RFID / Devices
↓
Edge Processing & Connectivity
↓
Data Ingestion
↓
Storage / Stream Processing
↓
AI / ML Models
↓
Decision & Automation Layer
↓
Human or Machine Action
Each layer introduces different engineering requirements.
The interesting aspect of this stack is that the AI Model itself is not always the most challenging component.
1. Data Starts at the Edge
Industrial environments can often be large producers of data.
Depending on the domain, this could take the form of:
Temperature
Vibration
Location
Motion
Video
Machine telemetry
Equipment status
Environmental conditions
And other forms.
The act of sending every raw signal to a cloud data warehouse may not always be optimal depending on the application.
Using edge processing lowers bandwidth requirements, reduces latency, and can allow for localized decision making.
For developers, this adds another layer to the system design challenge - deciding what computation should be done on the device, the edge gateway, or somewhere else.
2. Connecting Heterogeneous Systems
The reality of most industrial environments is that they are not designed around a unified set of technologies.
A production environment may involve legacy equipment, IoT hardware, proprietary systems, databases, industrial protocols, and many other technologies.
This presents an integration challenge that forms a large part of AIoT engineering.
It's not enough to simply gather data - data from different systems must be made:
Consistent
Time aware
Identifiable
Contextual
Accessible
Without that foundation, an AI model can offer limited value.
3. AI Needs Context
Let's say a sensor reports an abnormal temperature value.
What does this value mean?
Where was this value measured?
What was the machine doing?
What is a "normal" reading for this machine?
Has this value been seen before?
Are other sensors reporting similar values?
Has maintenance been performed?
This is why AIoT systems need more than typical ML inference - they need contextual data models.
These kinds of information can help an AI system make decisions about physical entities.
4. Real-Time Data Changes the Architecture
Most traditional AI applications are concerned with historical data.
AIoT systems, on the other hand, often need to reason about a continuous stream of data.
This introduces additional considerations such as:
Latency: How quickly does the system need to react?
Reliability: What happens if connection is lost?
Ordering: Are the events arriving in order?
Storage: What information should be stored?
Scalability: How many events will need to be processed?
A predictive-maintenance system and an analytics dashboard both might use similar data, but can have very different architectural requirements.
5. From Prediction to Action
There's an important difference between being able to predict something, and taking an action to change the outcome.
An AI model may be able to predict that a machine has higher chances of failure.
To actually make use of this prediction, another system layer is needed to:
Report the alert
Create a recommended maintenance plan
Send a message to an operator
Schedule a work order
Take other actions
Depending on the application, there may be an appropriate degree of automation.
Where safety is a concern or high value is placed on manual decisions, humans can be an important part of an AIoT system.
6. Physical AI Introduces New Testing Problems
Testing applications that operate in the physical world are significantly more complex than testing a generic API.
Developers must take into account scenarios such as:
Sensor failure
Missing data
Incorrect data
Network interruption
Physical conditions
Model uncertainty
Device failure
Conflicting sensors
A system that works properly in development can fail dramatically in production - especially in the face of unaccounted edge cases.
This is why simulation and system observability are so important.
7. Digital Twins Can Provide Another Layer
Digital twins can give additional value to AIoT systems.
By providing a virtual representation of a physical entity, twins can provide a layer of abstraction.
Twins can be used in a variety of ways such as:
Connecting the current state of a machine to a software representation
Using sensor data to update the digital representation of the machine
Applying predicted states and behaviors to a machine model
Maintaining historical information about a machine
AI can then operate not on raw sensor data, but instead a richer representation of the physical system.
Where AIoT Is Being Applied
The architecture outlined above can be applied to a variety of applications:
Industrial asset tracking
Predictive maintenance
Inventory optimization
Workforce safety
Computer vision
Robotics
Industrial automation
Environmental monitoring
Operational intelligence
Companies building businesses around these areas include Aperture Venture Studio, who are building applications for physical-world and industrial AIoT use cases.
A Practical Development Approach
When building an AIoT application, a good sequence looks like:
- Define the physical problem
Do not start with the AI Model. First, determine the physical problem to be solved.
- Identify the required signals
Determine what information is needed to solve the problem.
- Design the data pipeline
Decide how devices, edge systems, databases, and AI services will communicate.
- Establish a baseline
Measure the performance before implementing automated systems.
- Introduce AI
Use AI to improve upon the current methods.
- Add automation carefully
Move from insights to recommendations and, where applicable, actions.
- Monitor the complete system
Monitor not just model accuracy, but also device health, data quality, latency, system reliability, and results.
The Bigger Engineering Challenge
AIoT is fundamentally a systems-engineering challenge.
While the model is important, the sensors feeding it, the network carrying it, the infrastructure processing it, the software providing context, and the workflow taking actions are all of critical importance.
As AI makes its way into factories, warehouses, logistics systems, robotics, and other physical environments, developers will need to think beyond the model.
The question developers must ask is:
How can we create AI systems that can operate and understand the physical world?
This question requires AI, IoT, distributed systems, edge computing, data engineering, automation, and domain expertise to work together.
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