AI Models: What They Are and How They Work

You probably hear the term “AI model” a lot these days. It’s one of those phrases that people use without always knowing what it means. But if you want to understand AI—really understand it, not just nod along in conversations—you have to understand what an AI model actually is. The good news is, it’s simpler than it sounds.

What is an AI Model?

An AI model is just a kind of program. It’s not magic, and it’s not alive. It’s a set of instructions that tells a computer how to turn input data into output data. The “AI” part means the program can improve itself over time, but at the core, an AI model is just a way of mapping inputs to outputs. You give it data, it processes that data, and then it gives you something back.

An AI model is a program that has been trained on a set of data to recognize certain patterns or make certain decisions without further human intervention. Artificial intelligence models apply different algorithms to relevant data inputs to achieve the tasks, or output, they’ve been programmed for. Simply put, an AI model is defined by its ability to autonomously make decisions or predictions, rather than simulate human intelligence.

Among the first successful AI models were checkers- and chess-playing programs in the early 1950s: the models enabled the programs to make moves in direct response to the human opponent, rather than follow a pre-scripted series of moves. Different types of AI models are better suited for specific tasks, or domains, for which their particular decision-making logic is most useful or relevant.

Machine Learning Models

The most common type of AI model people talk about now are machine learning models. The big idea behind machine learning is that instead of programming rules for the computer to follow, you give the computer lots of examples and let it figure out the rules by itself. If you show it a bunch of pictures of cats and tell it, “these are cats,” the model starts to develop a sense of what makes a cat look like a cat. This is called training, and once a model is trained, it can make predictions on new data. You show it a picture it’s never seen before, and it can tell you whether or not it’s a cat.

If you understand this process—training on lots of data to develop a function—you understand the core of how AI models work. The nuances come from different types of models and the problems they’re trying to solve.

Algorithms vs. Models

It’s important to understand the difference between algorithms and models, as the terms are often used interchangeably but mean different things. Algorithms are procedures, often described in mathematical language or pseudocode, to be applied to a dataset to achieve a certain function or purpose. Models are the output of an algorithm that has been applied to a dataset. In simple terms, an AI model is used to make predictions or decisions and an algorithm is the logic by which that AI model operates.

Types of AI Models

Regression Models

The most basic kind of model is a simple regression model. Imagine you have a bunch of points on a graph, and you want to draw a line that best fits all those points. That’s a regression model. It finds the best line that explains your data.

Neural Networks

A more complex type of model is a neural network, which, instead of drawing one line, tries to create a multi-layered web of relationships. Neural networks are especially good at picking up subtle patterns in data, which is why they’re used in everything from speech recognition to generating art.

Neural networks get their name because they work a bit like the human brain. They have “neurons” organized in layers, and data moves from one layer to the next, getting transformed along the way. This layered structure is how neural networks are able to capture very complex relationships in data. The model can adjust the weight of each connection to minimize the error in its predictions. It’s like a child learning to recognize a dog by being corrected over and over, gradually adjusting their understanding until they get it right most of the time.

Decision Trees

Another popular type of AI model is a decision tree. Imagine you’re trying to classify different types of fruit. You start with questions like, “Is it round?” If yes, go down one branch; if no, go down another. This branching process repeats until you reach a decision, like “it’s an apple” or “it’s a banana.” Decision trees are intuitive, but they’re not always the best for complex problems, because they tend to overfit—which means they can be too specific to the data they trained on, and not generalize well to new data.

Transformer Models

Then there are more advanced models, like transformers. If you’ve heard of GPT-4, CLAUDE or any other large language model, those are transformer-based models. They’re built to process data sequences, like text, and can generate surprisingly coherent output because they’ve trained on an enormous amount of text data. Transformers are powering many of the AI advancements you see today. They can answer questions, write essays, or even generate entire conversations—basically, they’re pattern-recognition machines taken to an extreme level.

How Do AI Models Learn?

Now, when people say AI models are “learning,” they don’t mean it in the same way humans do. An AI model doesn’t understand anything. It doesn’t “know” what a cat is, or even what an image is. It has a complex mathematical representation of what it means for an image to resemble one of the millions of examples it’s seen. When you see a model perform well, it’s not because it understands. It’s just very good at matching patterns.

Types of Machine Learning

Machine learning models can be broadly categorized into three types based on how they learn:

• Supervised Learning: This is the most common type, where a human expert provides labeled training data. For instance, if you’re training a model to recognize cats and dogs, you label images as either “cat” or “dog.” The model learns from these examples and uses the labels to predict new images. It’s like teaching a child by explicitly pointing things out.
• Unsupervised Learning: Unlike supervised learning, unsupervised learning doesn’t require labeled data. Instead, it tries to find patterns within the dataset. For example, clustering customer data based on behavior. The model isn’t told what to look for—it figures it out by itself. This is useful when you don’t know exactly what you’re looking for in the data.
• Reinforcement Learning: In this method, a model learns by trial and error. It gets rewarded when it makes the right decisions, much like how you’d train a pet. This technique is used for things like self-driving cars or playing complex games, where the model learns from continuous feedback and improves itself over time.

Deep learning is a further evolved subset of machine learning, specifically part of unsupervised learning, where models use neural networks with multiple layers (hence “deep”) to learn complex patterns in the data.

Why Are AI Models So Prevalent Now?

So why do we hear so much about AI models now? Two reasons: data and computation. Training an AI model takes a lot of both. Ten years ago, we didn’t have as much labeled data, and computers weren’t as powerful. Now we have data coming from all over—images, videos, text, social media—and we have the computational power to process it all. That’s what has made AI so much better recently. The models themselves aren’t necessarily more sophisticated than they were a decade ago, but the scale of data and processing power available to them has gone up by orders of magnitude.

AI Models as Tools

It’s easy to get lost in the complexity of AI models, but at the core, they’re just tools. Like any tool, they’re only as good as the problem they’re used to solve. What makes them interesting isn’t that they’re new—they’re surprisingly good at a certain kind of problem, which involves recognizing patterns in huge amounts of data. Whether it’s recommending a movie, summarizing an article, or finding a tumor in an X-ray, AI models are really doing the same basic thing: mapping inputs to outputs, based on what they’ve seen before.

Should Your Business Use AI?

If you’re working in a startup or a small business, you might be wondering whether you need an AI model of your own. The answer depends on whether you have a specific problem that an AI model is well-suited to solve—like predicting what your customers want or automating repetitive tasks. You don’t have to build these models from scratch; there are companies like Linkt.ai that make it easier to implement AI solutions, so you can focus on what you do best.

Start with a clear problem, find a good source of data, and then see what AI can do. Remember, AI models aren’t some mystical force—they’re just a set of tools. The real magic comes from knowing how to use them well.