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Unorganized Machine
An unorganized machine is a concept mentioned in a 1948 report in which Alan Turing Alan Mathison Turing (; 23 June 1912 – 7 June 1954) was an English mathematician, computer scientist, logician, cryptanalyst, philosopher, and theoretical biologist. Turing was highly influential in the development of theoretical ... suggested that the infant human cortex was what he called an "unorganised machine". Overview Turing defined the class of unorganized machines as largely random in their initial construction, but capable of being trained to perform particular tasks. Turing's unorganized machines were in fact very early examples of randomly connected, binary neural networks, and Turing claimed that these were the simplest possible model of the nervous system. Turing had been interested in the possibility of simulating neural systems for at least the previous two years. In correspondence with William Ross Ashby in 1946 he writes: {{cquote, I am more interested in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Alan Turing
Alan Mathison Turing (; 23 June 1912 – 7 June 1954) was an English mathematician, computer scientist, logician, cryptanalyst, philosopher, and theoretical biologist. Turing was highly influential in the development of theoretical computer science, providing a formalisation of the concepts of algorithm and computation with the Turing machine, which can be considered a model of a general-purpose computer. He is widely considered to be the father of theoretical computer science and artificial intelligence. Born in Maida Vale, London, Turing was raised in southern England. He graduated at King's College, Cambridge, with a degree in mathematics. Whilst he was a fellow at Cambridge, he published a proof demonstrating that some purely mathematical yes–no questions can never be answered by computation and defined a Turing machine, and went on to prove that the halting problem for Turing machines is undecidable. In 1938, he obtained his PhD from the Department ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cerebral Cortex
The cerebral cortex, also known as the cerebral mantle, is the outer layer of neural tissue of the cerebrum of the brain in humans and other mammals. The cerebral cortex mostly consists of the six-layered neocortex, with just 10% consisting of allocortex. It is separated into two cortices, by the longitudinal fissure that divides the cerebrum into the left and right cerebral hemispheres. The two hemispheres are joined beneath the cortex by the corpus callosum. The cerebral cortex is the largest site of neural integration in the central nervous system. It plays a key role in attention, perception, awareness, thought, memory, language, and consciousness. The cerebral cortex is part of the brain responsible for cognition. In most mammals, apart from small mammals that have small brains, the cerebral cortex is folded, providing a greater surface area in the confined volume of the cranium. Apart from minimising brain and cranial volume, cortical folding is crucial for the br ... [...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 fro ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Nervous System
In biology, the nervous system is the highly complex part of an animal that coordinates its actions and sensory information by transmitting signals to and from different parts of its body. The nervous system detects environmental changes that impact the body, then works in tandem with the endocrine system to respond to such events. Nervous tissue first arose in wormlike organisms about 550 to 600 million years ago. In vertebrates it consists of two main parts, the central nervous system (CNS) and the peripheral nervous system (PNS). The CNS consists of the brain and spinal cord. The PNS consists mainly of nerves, which are enclosed bundles of the long fibers or axons, that connect the CNS to every other part of the body. Nerves that transmit signals from the brain are called motor nerves or '' efferent'' nerves, while those nerves that transmit information from the body to the CNS are called sensory nerves or '' afferent''. Spinal nerves are mixed nerves that serve ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
William Ross Ashby
W. Ross Ashby (6 September 1903 – 15 November 1972) was an English psychiatrist and a pioneer in cybernetics, the study of the science of communications and automatic control systems in both machines and living things. His first name was not used: he was known as Ross Ashby. His two books, ''Design for a Brain'' and '' An Introduction to Cybernetics'', introduced exact and logical thinking into the brand new discipline of cybernetics and were highly influential. These "missionary works" along with his technical contributions made Ashby "the major theoretician of cybernetics after Wiener". Biography William Ross Ashby was born in 1903 in London, where his father was working at an advertising agency.Biography of W. Ross Ashby The W. Ross Ashby Digital Archive, 2008. From 1921 he studied at |
ACE Computer
The Automatic Computing Engine (ACE) was a British early electronic serial stored-program computer designed by Alan Turing. It was based on the earlier Pilot ACE. It led to the MOSAIC computer, the Bendix G-15, and other computers. Background The project was managed by John R. Womersley, superintendent of the Mathematics Division of the National Physical Laboratory (NPL). The use of the word ''Engine'' was in homage to Charles Babbage and his Difference Engine and Analytical Engine. Turing's technical design ''Proposed Electronic Calculator'' was the product of his theoretical work in 1936 "On Computable Numbers" (and ) and his wartime experience at Bletchley Park where the Colossus computers had been successful in breaking German military codes. In his 1936 paper, Turing described his idea as a "universal computing machine", but it is now known as the Universal Turing machine. Turing was sought by Womersley to work in the NPL on the ACE project; he accepted and began w ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Negated AND Gate
In digital electronics, a NAND gate (NOT-AND) is a logic gate which produces an output which is false only if all its inputs are true; thus its output is complement to that of an AND gate. A LOW (0) output results only if all the inputs to the gate are HIGH (1); if any input is LOW (0), a HIGH (1) output results. A NAND gate is made using transistors and junction diodes. By De Morgan's laws, a two-input NAND gate's logic may be expressed as =+, making a NAND gate equivalent to inverters followed by an OR gate. The NAND gate is significant because any boolean function can be implemented by using a combination of NAND gates. This property is called functional completeness. It shares this property with the NOR gate. Digital systems employing certain logic circuits take advantage of NAND's functional completeness. The function is logically equivalent to One way of expressing A NAND B is \overline, where the symbol signifies AND and the bar signifies the negation of the express ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Genetic Algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc. Methodology Optimization problems In a genetic algorithm, a population of candidate solutions (called individuals, creatures, organisms, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also p ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
