How Does It Work?

Imagine you’re playing a guessing game with a friend. They think of something, and you ask questions to narrow down the possibilities. With each answer, your knowledge base grows, and your guesses become more accurate.

AI-powered 20 Questions works on a similar principle, but on a much larger scale. It employs a technique called decision tree learning.

The Decision Tree

A decision tree is a flowchart-like structure used to make decisions. In the case of 20 Questions AI, the tree is created by feeding the AI vast amounts of data. This data could be anything from general knowledge to specific trivia.

As the AI processes this data, it builds a complex decision tree. Each node in the tree represents a question, and each branch represents a possible answer. The leaves of the tree are the final guesses.

The Guessing Process

When you start a game, the AI begins at the root of the tree. It asks the first question, and based on your answer, it moves down a specific branch. This process continues until the AI reaches a leaf node, which is its final guess.

The Magic Behind the Scenes

The key to the AI’s accuracy lies in the quality and quantity of the data it’s trained on. The more data it processes, the more complex and accurate its decision tree becomes.

Additionally, the AI can learn from its mistakes. If it makes an incorrect guess, it can adjust its decision tree to avoid making the same mistake in the future.

Here’s a simplified example:

Let’s say the AI is trying to guess an animal. It might start with a broad question like, “Is it a mammal?”

• If you answer “yes,” it might follow up with, “Does it live in water?”
• If you answer “no,” it might ask, “Does it fly?”

And so on, until it narrows down the possibilities and makes a final guess.