AI Fundamentals: How AI Works — ABXK.AI

Regression: How a Model Predicts a Number

Sun, 18 Oct 2026 00:00:00 +0000

How many units will we sell next month? How long will this delivery take? What will this apartment cost? Whenever the answer is a number, the machine learning tool for the job is regression. It is the oldest method in this learning path, far older than computers, and it is still everywhere: in sales forecasts, in pricing, in the planning tools of almost every company.

This is part 1 of Level 1 in our learning path AI Fundamentals. Instead of the usual example with apartment prices, we use a question from the AI world itself: can you predict how much computing power the training of an AI model took from the number of its parameters? You will learn what a regression line is, how to read its slope, what R² tells you and why the points that miss the line are often the most interesting ones.

The AI Universe: How AI, Machine Learning, Deep Learning and Generative AI Fit Together

Sun, 18 Oct 2026 00:00:00 +0000

Artificial intelligence, machine learning, deep learning, generative AI, agents: the words are often used as if they meant the same thing. They do not. They are rings of one map, each one inside the next, like the layers of an onion. Once you know the map, you can place any AI system you read about, from a spam filter to a robot, and you know which questions to ask about it.

Classification: Accuracy, Precision and Recall Explained

Sun, 18 Oct 2026 00:00:00 +0000

Spam or not spam, fraud or normal payment, which department should get this ticket: most decisions that companies hand to machine learning are classification, a choice between a few fixed categories. A classifier is easy to build. Knowing whether it is good is harder, because a single number such as “96% correct” can hide exactly the errors that matter.

This is part 2 of Level 1 in our learning path AI Fundamentals. We use the digit recognizer from Level 0, a logistic regression trained on handwritten digits, and look at its results the way a professional would: with accuracy, a confusion matrix, precision and recall, and the threshold that decides between them.

How Machines Learn: An Experiment with Handwritten Digits

Sun, 18 Oct 2026 00:00:00 +0000

“The model learns from data” is easy to say. What does it mean in practice? How many examples does a model need, what happens if nobody tells it the right answers, and how does a machine learn by trial and error? Instead of repeating textbook claims, we ran the experiments ourselves, on a public dataset that anyone can use, with code that gives the same numbers on every run.

This is part 2 of Level 0 in our learning path AI Fundamentals. You will learn the three ways machines learn, supervised, unsupervised and reinforcement learning, and see what each of them achieved in our experiment. You do not need to run any code to follow along.

Decision Trees and Overfitting: When a Model Learns by Heart

Sun, 18 Oct 2026 00:00:00 +0000

A decision tree works like a game of twenty questions. “Is the pixel in the middle dark? If yes: is the pixel at the top left dark too?” After a few such questions, the tree reaches an answer. Trees are popular in business because anyone can follow their logic, and they are the building block of some of the most successful methods for table data. They also show the most important trap in machine learning more clearly than any other model: overfitting.

Training and Inference: When AI Learns and When It Answers

Sun, 18 Oct 2026 00:00:00 +0000

Every AI model lives two lives. First it learns: in training, it sees large amounts of data and adjusts its internal numbers, again and again, until its predictions are good. Then it works: in inference, the finished model receives a new input, such as your question, and produces an output without learning anything new. The two phases differ in almost everything: who pays, how long they take, what hardware they need and what can go wrong.

Parameters, Data and Compute: Why AI Models Grew So Fast

Sun, 18 Oct 2026 00:00:00 +0000

When a new AI model is announced, three numbers usually come with it: how many parameters it has, how much data it was trained on and how much computing power its training took. These three numbers explain most of the progress of the last fifteen years. The ideas behind today’s models are not much newer than the transformer of 2017; what changed is the scale.

This is part 4 of Level 0 in our learning path AI Fundamentals. You will learn what each of the three numbers means, how fast each has grown according to Epoch AI’s data on notable AI models and why bigger has meant better, so far.

Which Algorithm Wins? Six Classic Methods Compared

Sun, 18 Oct 2026 00:00:00 +0000

Machine learning offers dozens of methods for the same task, and every one of them has fans. Which one is best? The honest answer is: it depends on the data, and you only find out by testing. Testing fairly, however, is harder than it looks, because a single test can make a method look better or worse than it is by chance.

This is part 4 of Level 1 in our learning path AI Fundamentals. We ran six classic methods on the same handwritten digits and compared them with cross-validation, the standard way to test fairly. You will learn what each method does in one sentence, how cross-validation works, which method won on our data and why that does not make it the best method in general.

Clustering and PCA: Finding Structure Without Labels

Sun, 18 Oct 2026 00:00:00 +0000

Most data in a company has no labels. Nobody has marked which customers belong together, which machine readings are normal or which documents cover the same topic. Unsupervised learning works with exactly this kind of data. It does not learn to predict a known answer; it looks for structure: groups of similar items, or a simpler way to describe the data.

This is part 5 of Level 1 in our learning path AI Fundamentals. We return to the handwritten digits, this time without telling the methods which digit each image shows. You will learn how k-means clustering finds groups, why the hardest question is how many groups to look for, and how principal component analysis (PCA) compresses each image from 64 numbers to a handful, plus what that does to the groups.

Neural Networks in Plain Words: Neurons, Layers and Weights

Sun, 18 Oct 2026 00:00:00 +0000

Neural networks are behind almost every recent AI breakthrough, from image recognition to large language models. The name sounds like biology, but the idea is simple arithmetic: many small calculations, connected in layers, with numbers that the network adjusts while it learns. To show what that means, we built and trained a small network ourselves and changed its size step by step.

This is part 5, the last part of Level 0 in our learning path AI Fundamentals. You will learn what a single artificial neuron computes, how neurons form layers, what the weights are and what happened to the accuracy of our network when we gave it more or fewer neurons.