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Artificial life

Artificial life
Artificial life (often abbreviated ALife or A-Life[1]) is a field of study and an associated art form which examine systems related to life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry.[2] The discipline was named by Christopher Langton, an American computer scientist, in 1986.[3] There are three main kinds of alife,[4] named for their approaches: soft,[5] from software; hard,[6] from hardware; and wet, from biochemistry. Artificial life imitates traditional biology by trying to recreate some aspects of biological phenomena.[7] The term "artificial intelligence" is often used to specifically refer to soft alife.[8] Overview[edit] Artificial life studies the logic of living systems in artificial environments in order to gain a deeper understanding of the complex information processing that defines such systems. Philosophy[edit] Organizations[edit] Software-based - "soft"[edit] Techniques[edit] Notable simulators[edit] Related:  Artificial Life (A-Life)

Welcome to Hive! Artificial intelligence AI research is highly technical and specialized, and is deeply divided into subfields that often fail to communicate with each other.[5] Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[6] General intelligence is still among the field's long-term goals.[7] Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. History[edit] Research[edit] Goals[edit] Planning[edit] Logic-based

The Iron Giant (1999 Cellular automaton The concept was originally discovered in the 1940s by Stanislaw Ulam and John von Neumann while they were contemporaries at Los Alamos National Laboratory. While studied by some throughout the 1950s and 1960s, it was not until the 1970s and Conway's Game of Life, a two-dimensional cellular automaton, that interest in the subject expanded beyond academia. In the 1980s, Stephen Wolfram engaged in a systematic study of one-dimensional cellular automata, or what he calls elementary cellular automata; his research assistant Matthew Cook showed that one of these rules is Turing-complete. Wolfram published A New Kind of Science in 2002, claiming that cellular automata have applications in many fields of science. These include computer processors and cryptography. The primary classifications of cellular automata as outlined by Wolfram are numbered one to four. Overview[edit] A torus, a toroidal shape Cellular automata are often simulated on a finite grid rather than an infinite one. History[edit]

artificial life Welcome to Apache™ Hadoop™! Newsroom - Genius of Einstein, Fourier key to new humanlike computer vision This graphic illustrates a new computer-vision technology that builds on the basic physics and mathematical equations related to how heat diffuses over surfaces. The technique mimics how humans perceive three-dimensional shapes by instantly recognizing objects no matter how they are twisted or bent, an advance that could help machines see more like people. Here, a "heat mean signature" of a human hand model is used to perceive the six segments of the overall shape and define the fingertips. (Purdue University image/Karthik Ramani and Yi Fang) Download image WEST LAFAYETTE, Ind. - Two new techniques for computer-vision technology mimic how humans perceive three-dimensional shapes by instantly recognizing objects no matter how they are twisted or bent, an advance that could help machines see more like people. "Humans can easily perceive 3-D shapes, but it's not so easy for a computer," he said. As heat diffuses over a surface it follows and captures the precise contours of a shape.

A.I. Artificial Intelligence (2001 Neural network An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background[edit] There is no single formal definition of what an artificial neural network is. History[edit] and

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