Entropy is the measure of disorder, randomness, or uncertainty. In physics, it describes the dispersal of energy. In information theory, it measures the unpredictability of information. High entropy, therefore, means more disorder or uncertainty. Low entropy means more order or predictability. It quantifies how much information is missing to perfectly describe the system's state.
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| Image: Generated by Gemini AI - Bubbles in water |
Now, imagine you threw those cards up high in the air. They land scattered all over the floor, mixed up randomly and disorganized. That's like high entropy – everything is a mess.
So, entropy fundamentally measures how messy or disorganized something is.
So let's clarify with some more examples:
A clean room has low entropy. A messy room has high entropy.
A glass of ice water has low entropy (the cold water molecules mostly stay together). If the ice melts and the water warms up, the molecules move around more randomly, increasing entropy.
Flipping a coin is a high-entropy event because there's a 50/50 chance it will be heads or tails. You can't predict the outcome with certainty. If you had a two-headed coin, that would be low entropy because you know it will always land on heads.
So, in simple terms, entropy is about how much things are mixed up or how hard it is to predict what will happen.
Types:
Thermodynamic Entropy: Used in physics, measures disorder in a system.
Information Entropy: Used in information theory, quantifies uncertainty in data.
Uses:
Thermodynamics: Understanding energy distribution in physical systems.
Information Theory: Optimizing data compression and transmission.
Examples:
Thermodynamic Entropy:
- Ice melting into water increases entropy.
- A messy room has high entropy. A tidy room has low entropy.
Information Entropy:
- Unpredictable text data has high entropy, while repetitive text has low entropy.
- A coin flip has high entropy (50/50 chance). A two-headed coin has low entropy (100% heads).