Everybody is about algorithmic processes and feed manipulation, but is that really new? What if the algorithm powering your Google search results and the predictive text on your phone originated from a 100-year-old feud between two Russian mathematicians? It sounds like fiction, but it’s the real origin story of Markov chains, a mathematical concept that quietly shapes our digital world.
What are Markov chains and why do they matter?
At its core, a Markov chain is a model for predicting the next event in a sequence based only on its current state. It possesses a “memoryless” property, meaning it simplifies incredibly complex systems by disregarding their long history. This seemingly simple idea has profound implications for the technology industry.
Also interesting: Riding the Digital Marketing Wave – An In-Depth Look at SEO and SEA
Think about how Google became the titan of search. Early search engines ranked pages by keyword density, which users quickly learned to game. The breakthrough came with PageRank, designed by Larry Page and Sergey Brin, who modeled the web as a vast Markov chain. In this model, each webpage represents a “state,” and every link serves as a “transition.” By simulating the wanderings of a random web surfer, they could calculate which pages matter most, not by counting keywords, but by evaluating both the quality and number of inbound links. This framework remains the backbone of Google’s relevance and quality-based results.
Recommendations and predictive text: Markov chains on social media
The same probabilistic approach underpins recommendation engines on social media platforms and the predictive text tools you use every day. By analyzing the current state of your browsing, viewing, or typing patterns, algorithms make highly educated guesses about what you’ll explore or write next.
Markov chains have also played a significant role outside the digital sphere, as seen in fields such as nuclear physics. The mathematician Stanislaw Ulam, recovering from illness and immersed in numerous games of solitaire, realized that simulating a problem multiple times and tracking the results could provide valuable statistical approximations where direct calculation was impossible.
This idea evolved into the Monte Carlo method. Later, John von Neumann recognized that modeling neutron interactions inside a nuclear bomb required accounting for dependencies between events, a perfect case for Markov chains. This combination provided a crucial computational tool for the Manhattan Project.
Modern AI advances: From simple chains to sophisticated attention
Modern large language models, which underpin AI chatbots and translation tools, extend these principles even further. While classical Markov chains examine immediate context, state-of-the-art models utilize mechanisms known as “attention.” This technique weighs multiple words or concepts across a sentence or paragraph, sometimes even far apart, to better interpret meaning. For example, in “After studying blood and mitochondria, the scientist examined the cell,” attention systems help the AI connect “cell” with its biological meaning, not its prison sense, by attending closely to earlier context.
Future risks: When models train on their own output
However, this rapid technological evolution brings new challenges. As AI models generate more online content, new language models will inevitably train on the previous wave of AI-generated text. According to some researchers cited in the video, this could create a feedback loop where models become less creative and diverse, a “dull, stable state” where outputs begin to repeat and novelty declines. It’s a sobering risk for an ecosystem that relies on fresh data and variety.
Before you go: China’s Journey to Global Dominance in Trade and Manufacturing
For a comprehensive look into how this strange math predicts almost anything, check out the detailed video from Veritasium below. It provides an insightful journey into the history and impact of these transformative algorithms.
YouTube: The Strange Math That Predicts (Almost) Anything
By clicking play, you agree to YouTube's Terms of Service and Privacy Policy. Data may be shared with YouTube/Google.
Photo credit: The feature image is symbolic and has been done by Igor Vetushko.
