Codes on a screen

The origins of algorithms, artificial intelligence, and machine learning – Where it all began

,
Dagmar Damazyn

Artificial intelligence is everywhere today. It recommends movies, answers questions, and even drives cars. But what exactly is behind the term? Where does a simple algorithm end, and where does real AI begin? Understanding the history of these technologies reveals why they are often confused – and why this says more about human perception than about technology itself.

Is it dangerous to confuse algorithms with AI?

As AI becomes a marketing buzzword, it’s easy to assume that any complex technology is “intelligent.” This misunderstanding has consequences. If we fail to distinguish between simple algorithms and adaptive, learning AI, we risk overestimating what machines can do – and misplacing trust where it doesn’t belong. Imagine relying on a rule-based chatbot to provide mental health support. Recognizing the difference between static rules and systems that genuinely learn is not just a technical distinction; it’s essential for informed decision-making in a digital world.

What is an algorithm – And why it’s not AI

We use algorithms every day, often without realizing it. An algorithm is a clearly defined sequence of instructions for solving a problem – like a recipe for baking a cake or a formula in mathematics. The concept dates back centuries: In the 9th century, the Persian mathematician Muhammad ibn Mūsā al-Khwārizmī wrote a groundbreaking book on systematic calculation methods. His name later became the root of the term “algorithm.”

But an algorithm is exactly that: a fixed series of steps, precise but not intelligent. A tax calculator or a navigation system processes input following predetermined rules – without learning or adapting.

But what if machines could do more?

This question intrigued Ada Lovelace, who 1843 wrote the first algorithm for a mechanical computing machine, Charles Babbage’s Analytical Engine. She envisioned a world where machines could crunch numbers and compose music or create complex patterns. At the time, her ideas were pure science fiction – today, they have become reality through artificial intelligence.

Artificial Intelligence: Machines that can think?

While algorithms rigidly follow commands, artificial intelligence (AI) aims for something greater: machines that act, learn, and even “think” independently. In 1950, Alan Turing posed the question that changed everything: “Can machines think?” He proposed the Turing Test, which evaluates a machine’s ability to imitate human conversation so well that it becomes indistinguishable from a person. This provided the first practical definition of machine intelligence.

AI as a field was officially born in 1956 at the Dartmouth Conference, where John McCarthy coined the term “Artificial Intelligence.” Early AI systems, however, were far from true thinking machines. They relied on rule-based symbol manipulation – a beginning, but no breakthrough.

Machine Learning: Learning instead of following

The real shift came with the concept of machine learning (ML). Unlike traditional algorithms, ML systems learn from data. In the 1950s, computer scientist Arthur Samuel developed a program that improved its checkers game through practice. His philosophy: Why program when machines can learn on their own? Thus, the term “machine learning” was born.

Milestones such as IBM’s Deep Blue defeating chess champion Garry Kasparov in 1997 and AlphaGo’s victory in the complex board game Go demonstrated the potential of learning systems. These machines made decisions based on “experiences” – moving beyond static steps to true adaptability.

Why do we confuse algorithms with AI?

Why are simple rule-based systems often mistaken for AI? The confusion stems from a combination of limited digital literacy and clever marketing.

  1. Lack of knowledge: Many people don’t recognize the difference between static algorithms and adaptive systems.
  2. Marketing hype: Products are branded as “AI-powered” even when they are built on basic rule-based frameworks.
  3. Technological mystique: Complex or hidden processes often appear more intelligent than they are.

Example: A basic chatbot with preprogrammed responses is not AI. Only when it learns from interactions does a static structure evolve into a dynamic, intelligent system.

Are we heading toward a bigger risk?

The true risk may not be the confusion between simple algorithms and AI – it’s the moment when we can no longer distinguish artificial from human intelligence. Systems like OpenAI’s o1 are built on Chain of Thought (CoT) reasoning, mimicking human-like problem-solving steps. This approach doesn’t just generate responses; it mirrors how humans think through complex questions.

As AI becomes more sophisticated, the boundary between human thought and machine reasoning blurs. Understanding what’s behind the curtain – whether a static algorithm or a dynamic learning model – will be the key to navigating this new era safely and responsibly.