Why Distinguishing AI Terms Matters
Why It Is Important to Get the Terms Right
In the public eye, three concepts – algorithms, machine learning, and artificial intelligence – are often used as synonyms or in a random order. The same tool might be called an «algorithm» in one text, a «machine learning model» in another, and simply «AI» in a third. This isn't just terminological sloppiness; it masks a superficial understanding of how systems that are increasingly becoming part of professional and everyday life actually work.
In previous knowledge base materials, we have already explored why the term «artificial intelligence» can be misleading – in the article «Why the Term “Artificial Intelligence” Is Misleading» – and why modern systems do not possess reason or consciousness in the usual sense of those words – we discussed this in the article «AI Is Neither Mind Nor Consciousness» This article allows us to take a step back and build a logical hierarchy: what an algorithm is in principle, how machine learning differs from it, and how what we call AI relates to both concepts.
The goal of this article is not to provide an exhaustive technical description, but to systematize the terms used, including those in non-technical circles.
What Is an Algorithm in Computing
The Algorithm: The Starting Point
An algorithm is a precise sequence of actions leading from input data to a result. This concept is not specific only to computers and does not belong exclusively to the field of AI. An algorithm is a fundamental concept that existed long before the advent of computing machines.
When a chef follows a recipe, they are executing an algorithm: they take specific ingredients, perform operations in a given order, and ultimately get a predictable result. If at some step the recipe does not provide for a necessary action, the chef will stop: the logic is written in advance and in full. When an accountant calculates tax using a fixed formula, they are also acting according to an algorithm. When a GPS navigator calculates a route according to set rules, that is also an algorithm. In all cases, there is input data, a set of clearly defined operations, and a final result.
In programming, an algorithm is a formalized description of what a program should do. The programmer writes the rules, and the computer executes them. For every situation, there is an explicit answer: «if event A happens, perform action B».This approach is called classic, or «rule-based»./p>
Such a method is effective where the task is clearly defined and lends itself to formalization: sorting a list, calculating a sum, checking a file format, or routing a network packet. But as soon as the task becomes complex or uncertain – for example, when you need to recognize an object in a photograph or understand the meaning of a text – the classic approach begins to fail. Not because the algorithms are bad, but because the task cannot be reduced to a fixed set of rules that can be written by hand.
How Machine Learning Differs from Traditional Programming
Machine Learning: When Rules Are Not Set Manually
Machine learning is a field in which a program does not receive ready-made instructions but independently identifies patterns in data. This is the fundamental difference from traditional programming.
Let's go back to the chef analogy. The classic approach is when the cook receives a finished recipe with exact proportions and steps. Machine learning is when they are shown thousands of dishes labeled «tasty» or «not tasty», and they begin to grasp for themselves which combinations of ingredients and techniques yield a good result. The rules are not explicitly written anywhere – they are formed from experience.
In a classic scenario, the programmer describes the logic: «if there are horizontal lines and rounded shapes in a certain ratio on the image, it is likely a cat».In machine learning, the system is set up differently: it is presented with thousands or millions of examples with already known answers (for example, images labeled «cat» or «not cat»), and it adjusts its internal parameters to provide the correct answers on new data as accurately as possible.
At the same time, it is important to understand: machine learning does not replace algorithms. The learning process itself is an algorithm. The way the model's parameters are adjusted when an error occurs is an algorithm. The mathematical operations that make up a neural network are also algorithms. The difference is not in the disappearance of algorithms, but in the fact that part of the logic – those very recognition rules – is no longer set by a human but is formed during the learning process. At the same time, even the most complex neural networks remain «narrow»: they handle a specific task well within the framework of the data they were trained on – we will talk more about this in the article «Narrow AI, General AI, and the Illusions of the Future»
This significantly expands the range of solvable tasks. Speech recognition, text translation, image classification, recommendation systems – in these areas, machine learning has provided a qualitative breakthrough precisely because the rules in them are difficult to formalize manually.
Machine learning is not magic and it is not thinking. It is a statistical process of selecting model parameters that allows for the reproduction of patterns present in the training data. The system does not understand the essence of its actions – it optimizes calculations.
Defining Artificial Intelligence and Common Misconceptions
Artificial Intelligence: A Broader and Less Precise Term
«Artificial Intelligence» is a term used in two different senses, which is the main source of confusion.
In an academic context, AI is a research field that brings together many different approaches to creating systems capable of solving tasks that traditionally require human intelligence: logical inference systems, expert systems, planners, search methods – and machine learning as one of the tools.
In mass and business discourse, «AI» today most often refers specifically to systems based on machine learning, in particular, neural networks and language models. In itself, this is not an error; however, it leads to the perception of the term as something monolithic and fundamentally new in its nature.
In practice, the term «AI» is often used incorrectly in several typical situations.
Firstly, when it is used to name any data processing algorithm. A sorting system, a keyword-based spam filter, or a simple recommendation based on purchase history might be called «AI» for marketing purposes, even though there is no learning process involved and we are talking about standard programming.
Secondly, when the term implies the presence of reason, intent, or understanding. A language model generating coherent text does not realize its meaning. A system that beats a human at chess does not know that it is playing. The word «intelligence» in the name merely reflects a historical tradition rather than describing the real properties of the system.
Thirdly, when «AI» is used as a complete synonym for machine learning. This unjustifiably narrows the first concept and endows the second with excessive significance. Machine learning is a powerful tool within the broad field of AI, but not all AI is based on learning, and not every application of machine learning is appropriate to discuss in categories of «intelligence»./p>
The Relationship Between Algorithms ML and AI
Where the Lines Are Drawn and How the Concepts Relate
The logical hierarchy can be conveniently presented in the following order.
An algorithm is the broadest and most fundamental concept. Any computing system (including machine learning and AI) is built on algorithms. An algorithm is not the opposite of AI and is not something outdated – it is the basic building block.
Machine learning is a method within programming in which part of the logic is formed automatically based on data. This is a methodological difference, not a difference in the nature of systems. Machine learning uses algorithms and is itself an algorithmic process.
Artificial intelligence is the historically established name for the field of research aimed at creating systems for solving cognitive tasks. In the modern sense, the term most often points to systems using deep learning and language models. This is not a replacement for more precise concepts, but a «superstructure» that indicates the field of application rather than a specific technical method.
An important conclusion: AI does not replace algorithms and is not something fundamentally different. Pitting AI against algorithms is as pointless as talking about «the car instead of wheels».A car is a complex system that includes wheels. Similarly, modern AI systems are specific configurations of algorithms tuned for certain tasks.
Realizing this fact dispels many myths. If AI is built on algorithms, it means it obeys the laws of determinism or probability just like any program. It has limitations determined by data and architecture. This means its behavior is, in principle, subject to analysis, audit, and correction, even if in practice this can be a labor-intensive process.
Summary
An algorithm is a sequence of steps from data to a result. Machine learning is a way of building algorithms where rules are extracted from data rather than set manually. Artificial intelligence is the name of a field that today largely relies on machine learning but is not reduced to it and retains the algorithmic nature of computation.
Each of these concepts has its own boundaries. Using them correctly is not a formal requirement, but a guarantee of constructive dialogue about technologies that are increasingly influencing decision-making, content creation, and work with information.