Artificial Intelligence. Machine Learning. Large Language Models. Computer Vision. You’ve heard them all – and sometimes all in the same sentence. They’re everywhere, from investor decks to trade show booths, usually accompanied by big promises. But what do they actually mean in practice? And more importantly, what role do they play in the Internet of Things (IoT)?
At ObjectSpectrum, we work at the intersection of these technologies every day. Let’s break down the differences and talk about how each really fits into IoT.
Artificial Intelligence: The Big Tent
Artificial Intelligence (AI) is the umbrella term. It covers any system designed to mimic human-like intelligence – problem solving, decision making, or pattern recognition. But here’s where things get messy: most of the AI you hear about today is actually powered by specific techniques under that umbrella, such as Machine Learning (ML), Large Language Models (LLMs), or Computer Vision (CV).
That’s why when people say “AI,” it often sounds like they’re talking about LLMs (think ChatGPT). But LLMs are just one type of AI, not the whole picture.
Machine Learning: The Workhorse of IoT
Machine Learning is one of the most common forms of AI in IoT. Instead of following a fixed set of rules, ML systems learn from data. They spot patterns, improve over time, and adapt without needing to be reprogrammed constantly.
Take predictive maintenance in manufacturing. IoT sensors capture vibration, temperature, and pressure data. ML models use that history to predict equipment failures before they happen. The more data the system sees, the more accurate it becomes.
In IoT, ML is often the behind-the-scenes intelligence that makes “smart” devices actually smart.
Large Language Models: The Conversational Layer
LLMs are another form of AI – but their specialty isn’t raw sensor data, it’s language. They’re trained on massive datasets of text, which allows them to understand, generate, and interact in human language.
In IoT, LLMs can bridge the gap between complex data and human users. Imagine an operations manager asking, “How’s production looking this week?” Instead of combing through dashboards, an LLM can respond in plain English, pulling insights from IoT systems.
But LLMs aren’t great at everything. They require huge compute resources, making them less suitable for small, embedded devices. And while they excel at making IoT data accessible to humans, they’re not designed for tasks like predicting machine failures or counting cars on a roadway. That’s where ML or CV come in.
Computer Vision: The Eye of IoT
Computer Vision (CV) deserves its own spotlight. It’s the branch of AI focused on interpreting images and video. In IoT, that often means using a camera as a sensor.
But here’s the catch: in many cases, you don’t actually want the video feed. What you want are the insights extracted from it. CV can count people entering a building, spot cracks in a pipeline, or read license plates. Instead of sending gigabytes of video data, the device sends back just the key information – saving bandwidth and improving efficiency.
This kind of preprocessing is especially powerful on embedded or edge devices. A low-cost, low-power sensor equipped with CV or ML can do a lot of the heavy lifting locally, reducing how much data has to travel back to the cloud. LLMs, with their high compute demands, aren’t built for this kind of deployment – at least not today.
Dashboards vs. Intelligence
Data visualization is critical. “Pretty charts on dashboards” can go a long way toward making massive amounts of IoT data digestible. But the real magic happens when AI highlights the interesting parts.
Think of it as the difference between scrolling through a thousand camera feeds versus getting an alert that says, “This piece of equipment is showing early failure signs,” or “This traffic camera just counted 200 cars in the last ten minutes – double the norm.” AI helps surface the signal in the noise.
How They Work Together (and Alone)
It’s tempting to think of AI, ML, LLMs, and CV as always working hand in hand. Sometimes they do – but just as often, one technology does the job on its own.
- ML alone can power predictive maintenance without needing an LLM in the mix.
- CV alone can count vehicles without ML or LLM support.
- LLMs alone can make IoT systems conversational and user-friendly, without touching raw sensor data.
When they do combine, the results can be powerful: CV spotting defects, ML predicting failure rates, and an LLM explaining the findings in plain English. But IoT isn’t about one-size-fits-all. It’s about choosing the right tool for the right job.
Real-World Examples
Here’s what this looks like in practice:
- Healthcare: ML models predict health risks from wearable data, while CV detects irregularities in medical imaging. LLMs provide doctors with plain-language summaries.
- Manufacturing: ML identifies patterns that indicate machine wear. CV inspects parts for defects. LLMs translate factory performance into easy-to-understand reports for managers.
- Energy: ML predicts consumption patterns, AI algorithms optimize grid distribution, and dashboards show clear trends – highlighted by intelligence that points out anomalies.
- Transportation: CV counts vehicles and reads license plates, ML predicts maintenance needs, and LLMs help planners simulate “what if” scenarios in natural language.
Challenges Along the Way
Of course, there are hurdles:
- Data Quality: AI is only as good at the data feeding it.
- Scalability: Processing IoT’s enormous data volumes requires serious infrastructure.
- Security: More intelligence means more attack surfaces.
- Integration: Blending LLMs, ML, and CV into existing systems isn’t always straightforward.
These are the problems we tackle daily – turning challenges into opportunities for smarter IoT solutions.
The Road Ahead
IoT is only getting bigger. More devices, more data, more opportunities. ML and CV will continue to expand their reach on edge devices, enabling smarter, low-power sensors that can process information locally. LLMs will keep making data more accessible to humans, turning complex IoT systems into natural conversations.
The real story isn’t AI vs. ML vs. LLM vs. CV – it’s knowing when to use each, and when to combine them. The companies that get this right will be the ones shaping the future of connected systems.
How We Put It All Together
AI isn’t one thing, it’s a toolbox. Machine Learning, Large Language Models, and Computer Vision are three of its most powerful tools. In IoT, sometimes one is enough. Sometimes you need all three. But either way, the outcome is the same: smarter decisions, faster responses, and real value from your data.
At ObjectSpectrum, we don’t just build IoT systems – we build intelligent IoT systems. Because in a world drowning in data, intelligence isn’t optional. It’s essential.
Contact us today to see how we can make your products work better for your clients.