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Related Concept Videos

Entropy02:39

Entropy

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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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Entropy01:18

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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
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Entropy and the Second Law of Thermodynamics01:20

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The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
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The Second Law of Thermodynamics01:14

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In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Scientists refer to the measure of randomness or disorder within a system as entropy. High entropy means high disorder and low energy. To better understand entropy, think of a student’s bedroom. If no energy or work were put into it, the room would quickly become messy. It would exist in a very disordered state, one of high entropy. Energy must be...
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Entropy within the Cell01:22

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A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
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Entropy Change in Reversible Processes01:10

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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
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Related Experiment Video

Updated: Nov 27, 2025

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
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Learning Entropy as a Learning-Based Information Concept.

Ivo Bukovsky1, Witold Kinsner2, Noriyasu Homma3

  • 1Department of Mechanics, Biomechanics, and Mechatronics, Research Centre for Low-Carbon Energy Technologies, Faculty of Mechanical Engineering, Czech Technical University in Prague, Technicka 4, 166 07 Prague 6, Czech Republic.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

Learning entropy (LE) quantifies machine learning systems' learning effort as a novelty detection measure for data points. This non-probabilistic approach differs from Shannon information and doesn't require model accuracy.

Keywords:
informationlearninglearning systemsnon-probabilistic entropynovelty detection

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Area of Science:

  • Machine Learning
  • Information Theory
  • Cognitive Science

Background:

  • Introduced a novel non-probabilistic novelty detection measure: learning entropy (LE).
  • LE quantifies the learning effort of machine learning systems for individual data points.
  • Model accuracy is not a prerequisite for novelty detection using LE.

Purpose of the Study:

  • To extend the explanation of learning entropy (LE) from an informatics to a cognitive information measure.
  • To emphasize LE's distinction from Shannon's probabilistic information concept.
  • To discuss the potentials, limitations, and challenges of LE.

Main Methods:

  • Recalled fundamental derivations of learning entropy (LE).
  • Recalled practical estimations of LE.
  • Extended existing derivations and estimations of LE.

Main Results:

  • Demonstrated that learning effort is a quantifiable novelty measure for complex dynamic systems.
  • Extended the theoretical and practical understanding of LE.
  • Provided a discussion on the current state and future challenges of LE.

Conclusions:

  • Learning entropy (LE) offers a novel, non-probabilistic approach to novelty detection in complex systems.
  • LE provides a cognitive information measure distinct from traditional probabilistic information.
  • Further research is needed to address the limitations and challenges of LE.