Artificial Intelligence


Current Applications of Artificial Intelligence

main article: Current Applications of Artificial Intelligence
author: cuiruosh

SPAUN
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One of many applications of AI

With technology advancing faster than action potentials within axons, neuroscientists and investigators in related fields are beginning to understand the brain better than ever before. As the scientific sphere becomes more innovative and more intelligent, one begins to wonder just how complex the brain actually is. The modeling and creation of artificial intelligence is becoming an increasingly heated topic in neuroscience, and its importance cannot be understated. As an example, modeling allows scientists to further understand the hows (pathways) and whats (structures) of the mammalian brain, to give rise to new hypotheses on brain functions, to pinpoint functional processes to a cellular level, and finally, to determine the feasibility of a biologically validated artificial mammalian brain. Currently, the degree of complexity needed in terms of memory, computation, and communication is being proposed, giving scientists an idea of just how many computers are needed to match something as elaborate as the brain[1]. Taking the idea further, the Blue Brain Project attempts to reverse engineer the mammalian neocortex, hoping to eventually complete an accurate model of the human brain. More amazingly, University of Waterloo brings forward an unprecedented model so clever that it can complete basic IQ tests, mirror human working memory, and even demonstrate learning.

Bibliography
1. first full source reference
2. second full source reference


Machine Learning in Artificial Intelligence

main article: Machine Learning in Artificial Intelligence
author: Carl Marquardt

A Robot Student
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Machine Learning is the sub-field of Artificial Intelligence primarily concerned with discovering the fundamental laws governing learning and applying these principles to develop a machine/system that is able to learn. A primary goal of such a machine would be to use all available inputs from the environment in order to learn, without being specifically programmed to do so [1]. Currently, systems are able to learn from inputs using specific parameters. An example of a system that most people use every day would be an internet search engine, like Google. Machine perception (a more “human” goal of machine learning), allows the system/machine to collect sensory input through the faculties of vision, hearing, and touch [2]. Eventually, a machine should be able to not only learn like a human does, but collect input similarly as well. However, one of the most challenging obstacles that stand in the way of progress in this field is to develop a working model that represents how learning functions in humans and is applicable to a synthetic system [3]. There are currently many models in development to try and model learning which span many different fields of study ranging from Philosophy to Neuroscience. These models have had success in a variety of applications, but are so far limited in terms of their generalization.

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