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Low-complexity Art
Low-complexity art, first described by Jürgen Schmidhuber in 1997 and now established as a seminal topic within the larger field of computer science, is art that can be described by a short computer program (that is, a computer program of small Kolmogorov complexity). Overview Schmidhuber characterizes low-complexity art as the computer age equivalent of minimalism, minimal art. He also describes an algorithmic art, algorithmic theory of beauty and aesthetics based on the principles of algorithmic information theory and minimum description length. It explicitly addresses the subjectivity of the observation, observer and postulates that among several input data classified as comparable by a given subjective observer, the most pleasing one has the shortest description, given the observer's previous knowledge and his or her particular method for encoding the data. For example, mathematicians enjoy simple proofs with a short description in their formal language (sometimes called mathem ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Jürgen Schmidhuber
Jürgen Schmidhuber (born 17 January 1963) is a German computer scientist most noted for his work in the field of artificial intelligence, deep learning and artificial neural networks. He is a co-director of the Dalle Molle Institute for Artificial Intelligence Research in Lugano, in Ticino in southern Switzerland. Following Google Scholar, from 2016 to 2021 he has received more than 100,000 scientific citations. He has been referred to as "father of modern AI," "father of AI," "dad of mature AI," "Papa" of famous AI products, "Godfather," and "father of deep learning." (Schmidhuber himself, however, has called Alexey Grigorevich Ivakhnenko the "father of deep learning.") Schmidhuber completed his undergraduate (1987) and PhD (1991) studies at the Technical University of Munich in Munich, Germany. His PhD advisors were Wilfried Brauer and Klaus Schulten. He taught there from 2004 until 2009 when he became a professor of artificial intelligence at the Università della Sviz ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Predictability
Predictability is the degree to which a correct prediction or forecast of a system's state can be made, either qualitatively or quantitatively. Predictability and causality Causal determinism has a strong relationship with predictability. Perfect predictability implies strict determinism, but lack of predictability does not necessarily imply lack of determinism. Limitations on predictability could be caused by factors such as a lack of information or excessive complexity. In experimental physics, there are always observational errors determining variables such as positions and velocities. So perfect prediction is ''practically'' impossible. Moreover, in modern quantum mechanics, Werner Heisenberg's indeterminacy principle puts limits on the accuracy with which such quantities can be known. So such perfect predictability is also ''theoretically'' impossible. Laplace's demon Laplace's demon is a supreme intelligence who could completely predict the one possible future given ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Procedural Generation
In computing, procedural generation is a method of creating data algorithmically as opposed to manually, typically through a combination of human-generated assets and algorithms coupled with computer-generated randomness and processing power. In computer graphics, it is commonly used to create textures and 3D models. In video games, it is used to automatically create large amounts of content in a game. Depending on the implementation, advantages of procedural generation can include smaller file sizes, larger amounts of content, and randomness for less predictable gameplay. Procedural generation is a branch of media synthesis. Overview The term ''procedural'' refers to the process that computes a particular function. Fractals are geometric patterns which can often be generated procedurally. Commonplace procedural content includes textures and meshes. Sound is often also procedurally generated, and has applications in both speech synthesis as well as music. It has been used to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Demoscene
The demoscene is an international computer art subculture focused on producing demos: self-contained, sometimes extremely small, computer programs that produce audiovisual presentations. The purpose of a demo is to show off programming, visual art, and musical skills. Demos and other demoscene productions (graphics, music, videos, games) are shared at festivals known as demoparties, voted on by those who attend and released online. The scene started with the home computer revolution of the early 1980s, and the subsequent advent of software cracking. Crackers altered the code of video games to remove copy protection, claiming credit by adding introduction screens of their own (" cracktros"). They soon started competing for the best visual presentation of these additions. Through the making of intros and stand-alone demos, a new community eventually evolved, independent of the gaming and software sharing scenes. Demoscene productions can be made with the latest consumer techno ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Demo (computer Programming)
Demo, usually short for demonstration, may refer to: Music and film *Demo (music), a song typically recorded for reference rather than release * ''Demo'' (Behind Crimson Eyes), a 2004 recording by the band Behind Crimson Eyes * ''Demo'' (Deafheaven album), a 2010 EP by Deafheaven * ''Demo'' (The Flat liners Album), a 2002 album by the band The Flatliners * ''Demo'' (Miss May I album), a 2008 recording by the metalcore band Miss May I *"Demo", a 1990 single by Die Krupps * "Demo" (P-Model song), a 1979 recording by songwriters Susumu Hirasawa and Yasumi Tanaka *'' Demo (Skinless)'', a 1994 recording by the band Skinless * ''Demo 2004'' (Year of No Light album), a 2004 recording by Year of No Light *'' Demo #2'', an unreleased recording by Neutral Milk Hotel *''Demo'', 2008 debut EP by Yuna Computing and technology *Demo (computer programming), a multimedia spectacle of programming skill *The Demo, a computer demonstration in 1968, sometimes called "the mother of all demos" *DEMOn ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Curiosity
Curiosity (from Latin '' cūriōsitās'', from ''cūriōsus'' "careful, diligent, curious", akin to ''cura'' "care") is a quality related to inquisitive thinking such as exploration, investigation, and learning, evident by observation in humans and other animals. Curiosity is heavily associated with all aspects of human development, in which derives the process of learning and desire to acquire knowledge and skill. The term ''curiosity'' can also be used to denote the behavior or emotion of being curious, in regard to the desire to gain knowledge or information. Curiosity as a behavior and emotion is attributed over millennia as the driving force behind not only human development, but developments in science, language, and industry. Causes Curiosity can be seen as an innate quality of many different species. It is common to human beings at all ages from infancy through adulthood, and is easy to observe in many other animal species; these include apes, cats, and rodents. Ear ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Agents
In artificial intelligence, an intelligent agent (IA) is anything which perceives its environment, takes actions autonomously in order to achieve goals, and may improve its performance with machine learning, learning or may use knowledge representation, knowledge. They may be simple or complex — a thermostat is considered an example of an intelligent agent, as is a human being, as is any system that meets the definition, such as a firm, a state (polity), state, or a biome. Leading AI textbooks define "artificial intelligence" as the "study and design of intelligent agents", a definition that considers goal-directed behavior to be the essence of intelligence. Goal-directed agents are also described using a term borrowed from economics, "rational agent". An agent has an "objective function" that encapsulates all the IA's goals. Such an agent is designed to create and execute whatever plan will, upon completion, maximize the expected value of the objective function. For exampl ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Reinforcement Learning
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). The environment is typically stated in the form of a Markov decision process (MDP), because many reinforcement learning algorithms for this context use dynamic programming techniques. The main difference between the classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematica ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
First Derivative
In mathematics, the derivative of a function of a real variable measures the sensitivity to change of the function value (output value) with respect to a change in its argument (input value). Derivatives are a fundamental tool of calculus. For example, the derivative of the position of a moving object with respect to time is the object's velocity: this measures how quickly the position of the object changes when time advances. The derivative of a function of a single variable at a chosen input value, when it exists, is the slope of the tangent line to the graph of the function at that point. The tangent line is the best linear approximation of the function near that input value. For this reason, the derivative is often described as the "instantaneous rate of change", the ratio of the instantaneous change in the dependent variable to that of the independent variable. Derivatives can be generalized to functions of several real variables. In this generalization, the derivativ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Continuum Limit
In mathematical physics and mathematics, the continuum limit or scaling limit of a lattice model (physics), lattice model refers to its behaviour in the limit as the lattice spacing goes to zero. It is often useful to use lattice models to approximate real-world processes, such as Brownian motion. Indeed, according to Donsker's theorem, the discrete random walk would, in the scaling limit, approach the true Brownian motion. Terminology The term ''continuum limit'' mostly finds use in the physical sciences, often in reference to models of aspects of quantum physics, while the term ''scaling limit'' is more common in mathematical use. Application in quantum field theory A lattice model that approximates a Continuum (theory), continuum quantum field theory in the limit as the lattice spacing goes to zero may correspond to finding a second order phase transition of the model. This is the scaling limit of the model. See also * Universality classes References *H. E. Stanley, ''Intro ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neural Network
A neural network is a network or circuit of biological neurons, or, in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus, a neural network is either a biological neural network, made up of biological neurons, or an artificial neural network, used for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled in artificial neural networks as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1. These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset. Self-learning resulting from e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Self-similarity
__NOTOC__ In mathematics, a self-similar object is exactly or approximately similar to a part of itself (i.e., the whole has the same shape as one or more of the parts). Many objects in the real world, such as coastlines, are statistically self-similar: parts of them show the same statistical properties at many scales. Self-similarity is a typical property of fractals. Scale invariance is an exact form of self-similarity where at any magnification there is a smaller piece of the object that is similar to the whole. For instance, a side of the Koch snowflake is both symmetrical and scale-invariant; it can be continually magnified 3x without changing shape. The non-trivial similarity evident in fractals is distinguished by their fine structure, or detail on arbitrarily small scales. As a counterexample, whereas any portion of a straight line may resemble the whole, further detail is not revealed. A time developing phenomenon is said to exhibit self-similarity if the numerical v ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
