Deep Learning is great at perception (seeing a cat). Symbolic AI (traditional logic) is great at reasoning (if cat then meow). For years, these camps were divided. Now, we are seeing their convergence.
Why does it matter?
In critical infrastructure (my background SCADA), we cannot trust a "Black Box" neural network. We need explainability. Neuro-symbolic AI allows us to inject logical rules into the learning process.
Example: Safety Systems
We can train a neural network to optimize power grid flow, but constrain it with hard symbolic rules: "Never exceed 50Hz frequency deviation." If the neural net proposes an action that violates this physics rule, the symbolic layer rejects it. This guarantees safety while allowing optimization.
The Future
This is the key to "Reliable AI" in industrial automation. It moves us from "AI as a toy" to "AI as a trusted operator."
About the Author
Nay Linn Aung is a Senior Technical Product Owner specializing in the convergence of OT and IT.