Markov Chains

Have you ever noticed how your phone seems to predict what you're going to type next? Or how a streaming service recommends shows based on what you've watched before? These are just a few examples of how AI uses Markov chains to make predictions.

What are Markov Chains?

Imagine a system that can predict what might happen next based on what's happening now. That's the basic idea behind Markov chains. They are a special kind of tool that helps us understand sequences of events and make predictions about what might come next, considering what has happened recently.

Understanding Markov Chains with an Example

Think about a simple weather forecast with only two possibilities: sunny or rainy. A Markov chain can analyze past weather data to determine the probability of rain today, considering if it rained or was sunny yesterday. For example, if it rained yesterday, the Markov chain might predict a higher chance of rain today compared to a sunny yesterday.

How Markov Chains Work

To make predictions, Markov chains consider the probability of moving from one state (e.g., sunny) to another (e.g., rainy) based on past data. This helps AI models make informed decisions one step at a time.

The Power of Markov Chains in AI

While Markov chains themselves are simple, their power lies in their ability to model complex sequences of events.

• Predictive Text: Smartphones use Markov chains to analyze your typing patterns and predict the next word based on the current word and the words before it.

• Recommendation Systems: Streaming services and online stores use Markov chains to analyze your past viewing or purchase history to recommend content you might be interested in next.

• Spam Filtering: Email spam filters can use Markov chains to analyze the content and language patterns of emails to identify potential spam based on past spam messages.

Deepen Your AI Understanding with De-Bug!

Curious to explore more? Stay tuned for upcoming newsletters where we dive into practical AI applications. We break down complex concepts into relatable examples and deliver them straight to your inbox.

Join us and become an AI insider, equipped to navigate this ever-evolving field!