While early computer pioneers were struggling to create rigid rules of logic (if X, then Y), a Russian mathematician named Andrei Markov was unknowingly laying the groundwork for how the digital “brain” of today’s S366 AI works. He didn’t look at circuits, but at letters and the chances of them appearing one after the other.

1. Beyond Numbers: The “Eugene Onegin” Experiment

In 1913, Markov undertook a study that seemed more like a literary curiosity than a mathematical revolution. He took the first 20,000 letters of Alexander Pushkin’s famous poem “Eugene Onegin” and analyzed them by hand.

He wasn’t interested in rhyme or story, but in sequence. He noticed a fascinating pattern:

After a vowel, the probability of a consonant occurring was about 87%.

After a consonant, the probability of a vowel occurring was about 66%.

Why is this colossal? Because Markov proved that language is not chaos, but a system of probabilities. He created the concept that “the future is determined by the present, not the past.” In mathematics, this is called the Markov Property: to guess what letter comes next, you don’t need to know the whole book, but only the letter you have in front of you now.

2. Practical Example: What does a Markov Chain look like in action?

Imagine a simplified S366 AI system that knows only three weather states: Sunny, Cloudy, Rainy. A Markov Chain looks like a probability map:

If it’s Sunny today, there’s a 70% chance that it’ll still be Sunny tomorrow and a 30% chance that it’ll be Cloudy tomorrow.

If it's Cloudy, there's a 50% chance of rain and a 50% chance of sunshine.

The machine doesn't "understand" the weather. It just follows the path with the highest probability. This is exactly how text prediction (LLM) works: if you type "I'm going to...", the AI ​​instantly calculates that "work" or "bed" are much more likely than "astrophysics."

3. Hidden Markov Models (HMM)

As technology has advanced, we've moved on to "hidden" models. Think of it like a detective: the AI ​​sees the output (the words you said), but has to guess the "hidden state" (what you actually meant to say). These models were fundamental in the 80s and 90s for:

Speech recognition: How computers turn sounds into text.

Early Translations: The First Attempts at Switching Between Languages ​​Without a Human Translator

4. Why Does Markov Matter to S366 AI?

Without this probabilistic foundation, today’s virtual assistants would be slow robots searching through gigantic databases according to fixed rules. Thanks to Markov, S366 AI can be fluid. It doesn’t look for a “pre-written” answer, but instead builds it by calculating in milliseconds what word should come next to form a logical and correct sentence for your company.

Conclusion: The Step to Neural Networks

Andrei Markov taught machines to “guess” intelligently. But pure probability has a limit: it can’t understand the complex nuances of emotion or the deep context of a 100-page document. For that, a new revolution was needed.

In Episode 4, we'll leave probability tables behind and enter the fascinating world of Neural Networks. We'll see how researchers have attempted to build a silicon "brain," drawing inspiration directly from human biology.