AI explained

In 2025, AI is now everywhere — in marketing decks, in software demos, and yes, in infrastructure inspections. But it’s important to understand that all AI is NOT created equal, and not every solution that claims to use it actually delivers useful outcomes. Let’s break it down to what’s real, what’s not, and how to know what matters when it comes to using AI in visual data workflows. No buzzwords, just practical understanding for organizations working in and around drone inspections, utilities, and infrastructure.

What We Have Learned About AI in Visual Inspections

This series of articles will focus on AI in the field of visual infrastructure-inspections and aim to provide an understanding of these technologies, their abilities, shortcomings, and possibilities.

Understanding AI Without the Buzzwords

Let’s be honest: explaining AI “simply” isn’t simple — but that’s what we’re here to do. This article lays out the foundational concepts behind AI in the context of visual inspections, without the fluff, and without dumbing it down.

We’re not positioning Scopito as an AI company. But we do believe that anyone working with inspection data needs a clear, realistic understanding of what AI actually does, what it doesn’t do, and what’s required to make it work in the real world. Whether you’re just trying to grasp the basics or you’re navigating vendor conversations, this breakdown is designed to give you a solid starting point — grounded in what’s technically accurate and operationally useful.

If you have any questions as to the basics of how AI works, what you can do with the technology, or just don’t think we explained AI simply enough, please write to us.

What Is AI, Really?

At its core, Artificial Intelligence (AI) refers to machines performing tasks that would normally require human intelligence — things like pattern recognition, decision-making, or visual analysis. But not all AI is created equal.

There are three broad categories that help frame the conversation:

Artificial Narrow Intelligence (ANI)

This is the type of AI we actually have today — and it’s what powers almost everything labeled “AI” in software and inspections. It doesn’t think, it doesn’t understand — but it can process large amounts of data and identify patterns with impressive consistency.

From email filters and chatbots to image recognition models used in visual inspections, these tools are task-specific, data-driven, and limited to the domains they’re trained for. If a system is helping you find faulty components in drone images, that’s ANI.

Artificial General Intelligence (AGI)

AGI is still theoretical. It would match or exceed human capabilities across a broad range of tasks — learning new things, adapting to new environments, and reasoning across different domains.

We’re not there yet. Not close. And certainly not in the visual inspection space.

Artificial Super Intelligence (ASI)

This is the sci-fi version — the hypothetical AI that’s smarter than humans, fully self-aware, and capable of independent reasoning. It’s a popular debate topic among futurists and philosophers, but has no practical bearing on today’s infrastructure inspection challenges.

How Machine Learning Fits In

When people talk about “AI,” they’re usually referring to a specific subset of it: Machine Learning (ML). This is where most real-world applications live, especially in industries like infrastructure inspections.

At its core, Machine Learning means teaching a system to make predictions based on historical data — not by hard-coding rules, but by exposing it to examples and letting it learn the patterns.

Think of it like this:
Traditional programming tells the system exactly what to do.
Machine Learning gives it thousands of examples and lets it figure out the logic on its own.

That’s why your email’s SPAM filter gets better over time. It’s not following a rigid rulebook — it’s learning from flagged patterns.

In the visual inspection world, that might look like feeding the system thousands of labeled drone images to teach it what rust looks like on a transmission tower — or how vegetation encroachment typically presents near power lines. ML models don’t generalize well beyond their training, and they require good data to learn anything useful. But when properly trained and validated, they can become powerful tools for surfacing issues at scale — faster and more consistently than a human reviewer could.

What Deep Learning Actually Does

If Machine Learning is the engine behind AI, Deep Learning is the part doing most of the heavy lifting in visual data workflows.

Deep Learning models are built to process large volumes of labeled images and learn how to identify patterns — like a loose bolt, a cracked insulator, or overgrown vegetation near a transmission line. These models don’t just memorize examples — they learn from them. The more diverse, accurate, and well-labeled the data, the better they perform.

Most visual inspection models use what’s called a Neural Network — a layered structure loosely inspired by how the human brain works. Each “neuron” processes a small piece of information, and when stacked together in layers, they can detect increasingly complex patterns in visual data.

This is how a system trained on thousands of inspection photos can learn to identify not just a utility pole, but the specific hardware on it — and eventually, the early signs of failure.

But here’s the key point: Deep Learning doesn’t “understand” what it sees. It’s not thinking. It’s pattern-matching, at scale. And when deployed correctly, it can drastically reduce the time and human effort required to review large inspection datasets.

Computer Vision: Teaching Machines to See (Sort Of)

Computer Vision (CV) is the field of AI focused on helping machines interpret visual data — and in inspection workflows, it’s where Deep Learning models get put to work.

Modern CV systems rely heavily on Convolutional Neural Networks (CNNs) — specialized Deep Learning models trained to detect patterns in images. Instead of manually programming every possible defect or object, we train these systems on thousands of labeled images, and they learn to identify things like rust, cracks, missing components, or encroaching vegetation.

In practical terms:
Computer Vision is how your inspection platform can highlight anomalies across thousands of drone images without a human clicking through every frame.

It’s not magic, and it’s not perfect. These models don’t actually understand what they’re looking at — they’re recognizing statistical patterns. But when built on good data and used in the right context, they help teams process inspections faster and more consistently than manual review alone.

As Fei Fei Li, pioneer of ImageNet, put it:

“Understanding vision and building visual systems is really understanding intelligence. And by see, I mean to understand, not just to record pixels.”

We’re not quite at understanding. But Computer Vision is helping us take the next best step: turning pixels into actionable data.

Wrapping Up: So, Where Does This Leave Us?

AI isn’t a magic box — it’s a collection of tools and models that rely on data, context, and the right application to be effective. In the world of visual inspections, that means understanding the building blocks: Machine Learning, Deep Learning, and Computer Vision.

If you’re working with inspection data — whether you’re on the utility side or delivering services — having a grounded understanding of what AI can do today (and just as importantly, what it can’t) is essential to making the right decisions. This article is the first in a multi-part series meant to unpack those details.

In the next installment in this series, we’ll look at the current state of AI in visual infrastructure inspections: what’s actually being done, what’s still experimental, and where the biggest challenges lie.

Because before we talk about the future, we need a clear view of where we are right now.

Original article by Ditte R. Lønstrup. Updated in 2025 by: Gayle Godkin, Scopito’s Marketing Representative, who specializes in the commercial drone inspection space and helps communicate the value of visual inspection data to infrastructure industries.

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