Artificial Intelligence

Current Applications of Artificial Intelligence

main article: Current Applications of Artificial Intelligence
author: cuiruosh

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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.

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.

1. Bennett, C., Hauser, K. (2013). Artificial intelligence framework for simulating clinical decision-making. Artificial Intelligence in Medicine, Feb 18 – Epub
2. Caeilli, T. (1986). Digital image-processing techniques for the display of images and modeling of visual perception. Behavior Research Methods, Instruments, & Computers, 18(6), pp 493-506
3. Marcus, S. (1989). Introduction: A sampler in knowledge acquisition for the machine learning community. Machine Learning, 4(3-4), pp 247-249
4. Leake, D., Ram, A. (1995). Learning, goals, and learning goals: A perspective on goal-driven learning. Artificial Intelligence Review, 9(6), pp 387-422
5. Gros, C. (2009). Cognitive Computation with Autonomously Active Neural Networks: An Emerging Field. Cognitive Computation, 1(1), pp 77-90
6. Katić, D., Vukobratović, M. (1994). Connectionist approaches to the control of manipulation robots at the executive hierarchical level: An overview. Journal of Intelligent and Robotic Systems, 10(1), pp 1-36
7. Ivry, T., Michal, S. (n.d.). License Plate Number Recognition Using Artificial Neural Network. Introduction to Computational and Biological Vision, Mar 26- Epub
8. Golden, R.M. (1988). A unified framework for connectionist systems. Biological Cybernetics, 59(2), pp 109-120
9. Mallot, H., Giannakopoulos, F. (1996). Population networks: a large-scale framework for modeling cortical neural networks. Biological Cybernetics, 75(6), pp 441-452
10. Nilsson, N. J. (1998). Introduction to Machine Learning. (1st ed.). Stanford, CA. Stanford University
11. Smolensky, P. (1987). Connectionist AI, symbolic AI, and the brain. Artificial Intelligence Review, 1(2), pp 95-109
12. Shavlik, J. (1994). Combining Symbolic and Neural Learning. 14(3), pp 321-331
13. Lo, J. (2010). Functional model of biological neural networks. Cognitive Neurodynamics, 4(4), pp 295-313
14. Whiteson, S., Littman, M. (2011). Introduction to the special issue on empirical evaluations in reinforcement learning. Machine Learning, 84(1-2), pp 1-6
15. Ribeiro, C. (1998). Embedding a Priori Knowledge in Reinforcement Learning. Journal of Intelligent and Robotic Systems, 21(1), pp 51-71
16. Bajracharya, S., Lopes, C. (2012). Analyzing and mining a code search engine usage log. Empirical Software Engineering, 17(4-5), pp 424-466
17. Lippmann, R. P. (1989). Review of neural networks for speech recognition. Neural Computation, 1, 1–38
18. Le Cun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., & Jackel, L. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1, 541–551
19. Jordan, M. I. & Rumelhart, D. E. (1992). Forward models: Supervised learning with a distal teacher. Cognitive Science, 16, 307–354
20. Touretzky, D. S. (ed.) (1991). Special issue on connectionist approaches to language learning. Machine Learning, 7
21. Wu, Y., Doi, K., Metz, C., Asada, N., Giger, M. (1993). Simulation studies of data classification by artificial neural networks: potential applications in medical imaging and decision making. Journal of Digital Imaging, 6(2), pp 117-125

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