The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen. The seeds of modern AI were planted by philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.
The field of AI research was founded at a workshop held on the campus of Dartmouth College, USA during the summer of 1956. Those who attended would become the leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation, and they were given millions of dollars to make this vision come true.
Eventually, it became obvious that commercial developers and researchers had grossly underestimated the difficulty of the project. In 1974, in response to the criticism from James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence, and the difficult years that followed would later be known as an "AI winter". Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 1980s the investors became disillusioned and withdrew funding again.
Investment and interest in AI boomed in the first decades of the 21st century when machine learning was successfully applied to many problems in academia and industry due to new methods, the application of powerful computer hardware, and the collection of immense data sets.
In Greek mythology, Talos was a giant constructed of bronze who acted as guardian for the island of Crete.
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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. Moravec wrote in 1988, "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility".
Subsumption architecture is a reactive robotic architecture heavily associated with behavior-based robotics which was very popular in the 1980s and 90s. The term was introduced by Rodney Brooks and colleagues in 1986. Subsumption has been widely influential in autonomous robotics and elsewhere in real-time AI. Subsumption architecture is a control architecture that was proposed in opposition to traditional symbolic AI.
Synthetic intelligence (SI) is an alternative/opposite term for artificial intelligence emphasizing that the intelligence of machines need not be an imitation or in any way artificial; it can be a genuine form of intelligence. John Haugeland proposes an analogy with simulated diamonds and synthetic diamonds—only the synthetic diamond is truly a diamond. Synthetic means that which is produced by synthesis, combining parts to form a whole; colloquially, a human-made version of that which has arisen naturally.
Covers the history and fundamental concepts of neural networks, including the mathematical model of a neuron, gradient descent, and the multilayer perceptron.
We highlight two alternative, yet complementary, solutions for har-nessing available neural resources for improving integration of artificial limbs (ALs) through embodiment. 'Hard' embodiment exploits neural and cognitive body mechanisms by closely mimicki ...
Evolutionary algorithms are heuristic methods that operate on a population of individuals, which represent candidate solutions to optimization problems. Individuals with better quality, or fitness, are reproduced at a higher rate than less fit individuals ...
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Machine Learning (ML) is an attractive application of Non-Volatile Memory (NVM) arrays [1,2]. However, achieving speedup over GPUs will require minimal neuron circuit sharing and thus highly area-efficient peripheral circuitry, so that ML reads and writes ...