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Cognitive computer. A cognitive computer is a computer that hardwires artificial intelligence and machine learning algorithms into an integrated circuit that closely reproduces the behavior of the human brain. [1] It generally adopts a neuromorphic engineering approach. Synonyms include neuromorphic chip and cognitive chip. [2][3]
Neuromorphic computing is an approach to computing that is inspired by the structure and function of the human brain. [1][2] A neuromorphic computer/chip is any device that uses physical artificial neurons to do computations. [3][4] In recent times, the term neuromorphic has been used to describe analog, digital, mixed-mode analog/digital VLSI ...
Moravec's paradox is the observation in artificial intelligence and robotics that, contrary to traditional assumptions, reasoning requires very little computation, but sensorimotor and perception skills require enormous computational resources. The principle was articulated by Hans Moravec, Rodney Brooks, Marvin Minsky and others in the 1980s.
Rather than relying on raw computing horsepower, it uses an old-school, as-yet-unproven type of "nueromorphic" tech that's modeled after the human brain. Intel unveils an AI chip that mimics the ...
Brain-inspired computing refers to computational models and methods that are mainly based on the mechanism of the brain, rather than completely imitating the brain. The goal is to enable the machine to realize various cognitive abilities and coordination mechanisms of human beings in a brain-inspired manner, and finally achieve or exceed Human ...
In general, the term cognitive computing has been used to refer to new hardware and/or software that mimics the functioning of the human brain [5][6][7][8][9] (2004). In this sense, cognitive computing is a new type of computing with the goal of more accurate models of how the human brain/ mind senses, reasons, and responds to stimulus.
Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks. Their creation was inspired by neural circuitry. [1] [a] While some of the computational implementations ANNs relate to earlier discoveries in mathematics, the first implementation of ANNs was by psychologist Frank Rosenblatt, who developed the perceptron. [1]
e. In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [ 1 ][ 2 ] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in the brain.