The Story of Prediction: From Bones to Algorithms

Old, wrinkled hands tossed pale bones onto the dirt floor. The woman’s eyes scanned them, reading secrets from how they fell. Outside, a king, still smelling of war, shifted uneasily, his fingers tapping his sword handle, waiting for a sign of his fate.

Far away, a farmer gazed up at dark clouds. He once studied the flight of birds and animal behavior to predict the harvest. Last year, too much rain drowned his crops. He whispered a prayer, hoping this time for just enough water to save his land.

In the village, a young mother, cradling her baby, hesitated before knocking on the old woman's door, seeking answers about her child’s future.

Time moved on. The bones turned to dust, farms became cities, but the desire to know what comes next never faded.

Now, in sleek offices, managers tap screens, doctors study AI-generated images, and stock traders rely on algorithms to predict market swings. We no longer speak of omens but of data and algorithms. Yet beneath the surface, we are still asking the same questions as centuries ago.

From ancient seers to modern AI, the quest to predict the future endures. And while AI revolutionizes decision-making with unprecedented accuracy, its power lies in principles laid down by mathematicians like Newton and Gauss. Today these timeless ideas drive AI innovations in finance, healthcare, and beyond.

The Mathematical Foundations of Prediction

Behind every AI prediction lies centuries of mathematical principles. Just as ancient civilizations sought signs in the stars, today we use probability, statistics, linear algebra, and calculus to predict everything from stock market trends to disease outbreaks.

Probability: The Language of Uncertainty

At its core, prediction is about quantifying uncertainty. In the same way the king once sought certainty in the bones, today we turn to probability to measure the likelihood of events occurring. Bayes’ Theorem, a cornerstone of AI, allows us to update predictions based on new data. This is what powers algorithms that predict everything from the weather to next month's sales.

Fun Fact: The "naive" in Naive Bayes classifiers—simple but surprisingly powerful AI tools—comes from assuming that features are independent, even when they aren't. Despite this, they still perform well in many applications!

Statistics: From Data to Insights

Statistics turns raw data into something meaningful. Descriptive statistics summarize data, while inferential statistics help us make predictions about a larger population based on smaller samples—like guessing the quality of an entire crop from a handful of seeds. The familiar bell curve, or normal distribution, helps model many real-world phenomena, from financial markets to patient outcomes in healthcare.

Linear Algebra: The Language of Data Representation

Modern data isn’t written in ancient symbols or scattered bones but is represented by vectors and matrices. Linear algebra helps AI process vast amounts of data, uncovering patterns hidden in thousands of variables. The tools used to manipulate this data—like eigenvectors and eigenvalues—are crucial in reducing complexity in big data sets.

Calculus: Optimizing for Better Predictions

Calculus allows us to find the best solutions in complex prediction problems. Just as a farmer might adjust his planting strategy based on last year’s rainfall, AI systems use derivatives to understand how small changes in data affect outcomes. Techniques like gradient descent help machine learning algorithms iteratively improve their accuracy.

Information Theory: Quantifying Data and Uncertainty

Developed by Claude Shannon, information theory measures how much "information" is contained in a dataset, helping us understand how efficiently our models work. In the same way that ancient mystics tried to interpret signs, today’s AI models quantify uncertainty to deliver more reliable predictions. We explored this more in our previous edition.

The Evolution of Prediction: From Bayes to Generative AI

While we no longer cast bones or look to the skies for answers, we still use the same desire for foresight. Whether it’s in the boardroom or on the trading floor, modern AI systems rely on the foundational work of mathematicians like Bayes, Newton, and Gauss. Their principles, developed centuries ago, now power everything from self-driving cars to voice assistants.

Understanding these mathematical foundations is essential to making sense of today’s AI. It’s the difference between seeing AI as a mysterious oracle and using it as a well-understood tool to shape the future.

Next on Pilgrim's Guide to Perplexities: The role of gambling in building the modern world.