The title of this article intentionally doesn’t quite make sense. Why? We will explain in this article.
We live in a flood of information, while the world of AI is growing. There are a lot of buzzwords around artificial intelligence that are often confused. Strangely, even many AI vendors still confuse the basic concepts.
Discover the differences between them and learn how to use them in your company.
The world is flooded with information about AI. Every other company claims to be delivering AI. But in most cases, it goes like this:
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If you feel lost in today’s AI world, you’re not alone. Everyone is talking about ChatGPT, but terms like AI, LLM, NLP, and ML are flying around everywhere. And even (self-proclaimed) AI experts often mix them up. It is like someone saying “car” and meaning the engine, another meaning the steering wheel, and a third referring to a specific BMW model. All terms are connected, but it’s not the same thing.
In this article, we will clearly explain what all these terms mean and how they relate to each other. So next time someone mentions an “AI solution” or a “language model,” you’ll know what they’re talking about. After all, artificial intelligence didn’t just appear with the arrival of ChatGPT in 2022, that’s simply when people started talking about it more. In reality, this is a field that has been evolving for decades.
The acronym AI, artificial intelligence, refers to technology that allows machines to mimic human activities and certain traits of human intelligence.
Tomáš Brychcín, CEO of SentiSquare, defines AI as follows: "A machine exhibiting some form of intelligent behaviour by doing an activity that would otherwise require to be done by a human. But there can be many such activities, and a machine can do them with varying levels of success. It is then debatable at what point is the perfomance intelligent enough to be called AI. Based on this logic, I would define AI as a machine that performs a given task similarly well to a human."
Artificial intelligence is not just about models like ChatGPT talking to you. The term "AI also includes tasks such as:
Most of these apps don't “talk”, but they are full-fledged AI systems that solve specific problems. Without often realizing it, we have been surrounded by AI for decades.
AI models are so complex that configuring them manually is impossible. Today, AI algorithms aren’t hand-coded, instead, they are trained using a method called machine learning (ML). This means that the AI learns automatically from data rather than being explicitly programmed.
Machine learning teaches AI systems by example. Every AI model we know and use today, including ChatGPT, has been trained this way.
Wikipedia defines machine learning as: “Machine learning is a subfield of artificial intelligence that deals with algorithms and techniques enabling a computer system to ‘learn’. In this context, learning means a change in the system’s internal state that improves its ability to adapt to changes in its environment.”
In simple terms: machine learning allows computers to detect patterns in data without needing explicit human instructions.
Instead of writing thousands of if/then rules like “if an email contains the words ‘complaint’ and ‘not working’, then it’s a customer issue”, you simply feed the model thousands of examples and it figures out the rules on its own.
Natural Language Processing (NLP) is a branch of AI focused on analyzing, interpreting, and understanding human language, whether written or spoken. In other words, it's a subfield of artificial intelligence dedicated to working with language.
Examples of NLP applications include:
Put simply, it's AI that understands human language. It doesn’t have to speak, but it knows what you mean.
One of the key areas of NLP is the language model (LM). A language model calculates the probability of the next word in a sequence of text.
We live in the age of the internet, which means we have access to an almost infinite amount of text and therefore examples of how words are used in context. This is how a language model learns: by seeing countless examples of which words tend to follow others.
To predict the next word correctly, the model has to understand the text. That’s why language models are used as the foundation for understanding language.
Language models have been around for years and power applications such as:
There are large and small language models. Smaller models are often more practical they require fewer computing resources and can be more easily tailored to specific use cases. Larger models, on the other hand, are more universal and can handle a wider range of tasks. But we’ll dive into that comparison in our next article.
Let's get back to the automobile analogy. Think of AI (artificial intelligence) as the entire automotive industry, it includes all kinds of vehicles (different AI models). NLP is like the category of cars. A language model is the engine that powers the car. And machine learning is the factory where the car is built and configured.
And ChatGPT? That’s like a six-meter SUV with a 16-cylinder engine. A powerful, all-purpose vehicle that can take you almost anywhere. But in some places, it might be hard to park, take corners clumsily, and you certainly won’t enjoy refueling it.
In many cases, it’s more efficient to use a lightweight and fuel-efficient hatchback, a small language model (SLM). For fast cornering, you might prefer a sporty roadster like a Mazda MX-5, a differently tuned small language model.
Modern AI systems that work with language typically combine all these components. They process human language (NLP), rely on language models (LM) to understand it, and are trained via machine learning (ML), using real-world data instead of hand-coded rules.
You've probably heard some of the following statements. Let’s put them into the right context:
That’s like asking, “Is an engine better than a car?” NLP (natural language processing) is a broad field, and LLMs (large language models) are one of its core components.
Machine learning is the foundation of modern AI, not an alternative to it. Every modern AI system, including generative models, is built using machine learning. Without ML, AI wouldn't exist.
NLP is a subcategory of AI, not a competing concept. If you want AI to understand emails, conversations, or even talk back, you're looking for NLP. It's the branch of AI that understands language.
LLMs are incredibly versatile and can handle a wide range of tasks. But remember our example of the oversized SUV, many tasks can be solved more efficiently. Small Language Models (SLMs) are a powerful alternative: they’re faster, more cost-effective, and easier to tailor.
At SentiSquare, we build small, specialized language models (SLMs). We believe the future lies in combining both large and small models, choosing the right one for the job.
If you're considering AI for your company, choose your provider wisely. A good partner won’t just resell you ChatGPT. They’ll explain what solution makes sense for your specific use case and you’ll know it by the way they talk about AI.
Our No-Code NLP platform allows companies to understand millions of customer interactions, without writing a single line of code. Instead of generic answers, you’ll get concrete insights from your data.
Our small specialized models know that “Bank A is better than Bank B” means positive sentiment in one case and negative in the other. They understand the context of your business and how your customers speak and they do it fast, with human-like accuracy.
(We’ll share more about these models in our next article!)
If you're excited about AI, we get it! There’s huge potential. Just remember: there are many forms of AI, and some of them might do the job even better without ever “speaking” a word.
So next time you hear someone mention an “AI solution,” you’ll know what questions to ask. And you might just discover that the best AI for your company doesn’t need to talk, it just needs to understand.