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Richard Hamming - Wikipedia
Richard Wesley Hamming (February 11, 1915 – January 7, 1998) was an American mathematician whose work had many implications for computer engineering and telecommunications.
Information Theory: A Tutorial Introduction
Shannon's mathematical theory of communication defines fundamental limits on
how much information can be transmitted between the different components of any
man-made or biological system. This paper is an informal but rigorous
introduction to the main ideas implicit in Shannon's theory. An annotated
reading list is provided for further reading.
Dissipative Adaptation: The Origins of Life and Deep Learning
The document explores the concept of Dissipative Adaptation, drawing parallels between the emergence of life and the mechanisms of Deep Learning. It discusses the work of Jeremy England and his theory of non-equilibrium statistical mechanics known as Dissipative Adaptation, which explains the self-organizing behavior of Deep Learning. The text delves into how neural networks evolve through training, emphasizing the role of external observations in driving the system towards minimizing entropy. It contrasts the mechanisms of Dissipative Adaptation with current Deep Learning architectures, highlighting similarities in alignment of components to maximize energy dissipation or information gradient.
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.
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.
Temperature as Joules per Bit
The paper suggests that temperature should be defined in terms of entropy, rather than vice versa. It argues that the current practice of measuring entropy in joules per kelvin is a historical artifact and proposes measuring entropy in bits instead. The paper also discusses the role of information in thermodynamics and the thermodynamic cost of erasure. It concludes by suggesting that entropy, not temperature, should have its own unit and that Boltzmann's constant should be dissolved.
Probability and InformationTheory
In this chapter, the authors discuss probability theory and information theory. Probability theory is a mathematical framework for representing uncertain statements and is used in artificial intelligence for reasoning. Information theory, on the other hand, quantifies the amount of uncertainty in a probability distribution. The chapter explains various concepts, such as probability mass functions for discrete variables and probability density functions for continuous variables. It also introduces key ideas from information theory, such as entropy and mutual information. The authors provide examples and explanations to help readers understand these concepts.
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