How to create meaning from data in AI: Applying cognitive layers to computer vision in heavy industry

By Dr Nathan Kirchner, Founder and Chief Evangelist, Presien

We live in an age where data is generated at an astonishing pace, from the devices we use to the environments around us. But the real challenge lies not in collecting data—it’s in turning it into meaningful insights that can drive smarter, safer, and more efficient decisions.

That’s where cognitive layers come in. Cognitive layers refer to a framework or methodology in artificial intelligence (AI) that adds a higher level of interpretation and reasoning to raw data. These layers mimic how humans process information, enabling machines to understand not just what is happening but also why it is happening.

By combining innovative sensory tools with the power of context and cognitive understanding, real asset owners, operators and developers have the potential to transform information into actionable intelligence, unlocking a new era of operational awareness across their portfolios.

The overlooked power of nonverbal data

Every day, much of what we understand comes not from words but context—facial expressions, gestures, and subtle environmental cues. Similarly, in operational settings, data often comes in nonverbal forms. In high-risk sectors like heavy industry, this can look like the tilt of a machine, a sudden temperature change, or a movement pattern. These clues, while easy to miss, hold immense potential when properly interpreted.

In my experience, traditional systems focus on straightforward metrics. By embracing nonverbal data alongside relevant technologies, we’ve seen customers quickly unlock more profound insights that reflect the complexity of their real-world environments.

At Presien, we’ve delivered this clarity to customers with Blindsight, our award-winning flagship AI vision product. Designed for high-risk industries like construction and mining, Blindsight uses advanced sensors and machine vision to monitor surroundings, detect potential hazards, and alert workers before problems escalate.

Turning data into insight: Making sense of the noise

Data by itself isn’t valuable until it’s understood. The process of transforming raw numbers into actionable insights involves three key steps:

  1. Data: Collecting facts—such as sensor readings from a machine.
  2. Information: Placing those facts into context, like identifying that a machine is overheating.
  3. Insight: Drawing conclusions, such as predicting that the overheating will lead to a breakdown without immediate action.

Presien’s systems excel at this transformation, taking the constant flow of environmental data and converting it into timely, meaningful alerts. For asset managers and developers, this isn’t just about having data on hand; it’s about enabling proactive decisions that reduce risks and costs.

Cognitive layers: Adding intelligence to insights

What if machines could think like humans, noticing not just what’s happening but why it’s happening? This is the promise of cognitive layering, a method that allows AI to interpret events in context, just as people do. For instance, in a mining operation, Blindsight might detect subtle vibrations in the ground. Rather than just flagging the vibration, a cognitive layer could interpret it as a precursor to a larger issue, like a structural weakness, and offer actionable recommendations.

This approach transforms safety systems from reactive tools into proactive ones, enabling industries to anticipate and prevent problems before they occur.

It’s not just heavy industry taking advantage of cognitive layering. In healthcare, cognitive layers enable AI to analyse nonverbal cues like changes in a patient’s vital signs or facial expressions, helping doctors identify early signs of conditions such as sepsis or neurological disorders. Similarly, in logistics, AI systems use cognitive layers to interpret patterns in shipping delays, weather conditions, and traffic flows, ensuring goods are rerouted efficiently to avoid disruptions.

Understanding the bigger picture: Task-based context

It’s not enough to know what’s happening—understanding the purpose behind tasks is critical for effective decision-making. By embedding task-based grounded inference into AI systems, technologies like Blindsight can view operations holistically.

For example, it could assess not just the position of a construction crane but also its role in the broader project, the risks in its surroundings, and its alignment with overall objectives. This deeper understanding enables systems to offer more relevant, actionable insights that enhance both safety and efficiency.

A smarter, safer future

The potential for AI to revolutionise operations is immense, particularly when cognitive layers are integrated into perception capabilities and decision-making processes.

By leveraging its power to build a highly capable perception layer atop raw sensing data, AI can extract richer, more nuanced information, transforming streams of signals into actionable insights. This enhanced perception allows systems to interpret nonverbal data, apply advanced contextual understanding, and ground insights directly in real-world tasks.

For industries managing complex, high-risk environments, such capabilities aren’t just innovative—they’re essential. By creating AI systems that can perceive, reason, and act with clarity, businesses are better equipped to navigate the challenges of a data-driven world. This approach unlocks unprecedented opportunities for safety, efficiency, and performance, setting the stage for smarter and more adaptive operations.

To learn more about Presien’s Blindsight technology, visit https://www.presien.com/.

About Dr Nathan Kirchner

Dr Nathan Kirchner, Founder and Presien’s Chief Evangelist, is a leading voice in robotics and AI, championing the use of data-driven technologies to enhance safety and efficiency across various industries. As an expert in grounded inference and cognitive layering, Dr Kirchner’s contributions continue to inspire advancements in AI’s ability to interpret and act on real-world data in nuanced, context-aware ways. His work aligns with Presien’s mission to create advanced, perspicacioussystems that not only monitor but also intelligently interpret operational environments, setting the stage for a safer and more insightful future.

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