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A high-bias, low-variance introduction to Machine Learning for physicists
This text is an introduction to Machine Learning for physicists, highlighting the natural connections between ML and statistical physics. It explains the use of "energy-based models" inspired by statistical physics in deep learning methods. The discussion includes the application of methods from statistical physics to study deep learning and the efficiency of learning rules.
Re: [Fis] A PROPOSAL ABOUT THE DEFINITION OF INFORMATION
The email exchange discusses the concept of negative entropy and its implications in mathematics and thermodynamics. Sungchul Ji questions the validity of negative entropy based on the Third Law of Thermodynamics. Arturo Tozzi argues for the existence of negative entropy in certain cases and relates it to information theory and free energy.
Information
The text discusses the challenges and complexities of measuring and quantifying information, particularly in terms of storage capacity, compression, and entropy. It explores various examples, such as genome information, human sensory capabilities, and the information content of objects like water molecules and black holes. The relationship between information, entropy, and physical properties is also highlighted.
Landauer's principle
Landauer's principle is a physical principle that establishes the minimum energy consumption of computation. It states that irreversible changes in information stored in a computer dissipate a minimum amount of heat to the surroundings. The principle was proposed by Rolf Landauer in 1961 and states that the minimum energy needed to erase one bit of information is proportional to the temperature at which the system is operating. While the principle is widely accepted, it has faced challenges in recent years. However, it has been shown that Landauer's principle can be derived from the second law of thermodynamics and the entropy change associated with information gain.
Bremermann's limit
Bremermann's limit is a maximum rate of computation that can be achieved in a self-contained system in the material universe. It is based on Einstein's mass-energy equivalency and the Heisenberg uncertainty principle. This limit has implications for designing cryptographic algorithms, as it can determine the minimum size of encryption keys needed to create an uncrackable algorithm. The limit has also been analyzed in relation to the maximum rate at which a system with energy spread can evolve into an orthogonal state.
Bekenstein bound
The Bekenstein bound is an upper limit on the entropy or information that can be contained within a given finite region of space with a finite amount of energy. It implies that the information of a physical system must be finite if the region of space and energy are finite. The bound was derived from arguments involving black holes and has implications for thermodynamics and general relativity. It can be proven in the framework of quantum field theory and has applications in various fields, such as black hole thermodynamics and the study of human brains.
A New Physics Theory of Life | Quanta Magazine
According to physicist Jeremy England, the origin and evolution of life can be explained by the fundamental laws of nature. He proposes that living things are better at capturing and dissipating energy from their environment compared to inanimate objects. England has derived a mathematical formula based on established physics that explains this capacity. His theory, which underlies Darwin's theory of evolution, has sparked controversy among his colleagues. While some see it as a potential breakthrough, others find it speculative. England's idea is based on the second law of thermodynamics and the process of dissipating energy. He argues that self-replication and structural organization are mechanisms by which systems increase their ability to dissipate energy. His theory may have implications for understanding the formation of patterned structures in nature.
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