Neural Networks
A neural network also known as an artificial neural network provides a unique computing architecture whose potential has only begun to be tapped. They are used to address problems that are intractable or cumbersome with traditional methods. These new computing architectures are radically different from the computers that are widely used today. ANN's are massively parallel systems that rely on dense arrangements of interconnections and surprisingly simple processors (Cr95, Ga93).
Artificial neural networks take their name from the networks of nerve cells in the brain. Although a great deal of biological detail is eliminated in these computing models, the ANN's retain enough of the structure observed in the brain to provide insight into how biological neural processing may work (He90).
Neural networks provide an effective approach for a broad spectrum of applications. Neural networks excel at problems involving patterns, which include pattern mapping, pattern completion, and pattern classification (He95).
Neural networks may be applied to translate images into keywords or even translate financial data into financial predictions (Wo96).
Neural networks utilize a parallel processing structure that has large numbers of processors and many interconnections between them. These processors are much simpler than typical central processing units (He90). In a neural network, each processor is linked to many of its neighbors so that there are many more interconnections than processors. The power of the neural network lies in the tremendous number of interconnections (Za93).
ANN's are generating much interest among engineers and scientists.
Artificial neural network models contribute to our understanding of biological models. They also provide a novel type of parallel processing that has powerful capabilities and potential for creative hardware implementations, meets the demand for fast computing hardware, and provides the potential for solving application problems (Wo96).
Neural networks excite our imagination and relentless desire to understand the self, and in addition, equip us with an assemblage of unique technological tools. But what has triggered the most interest in ne...
... middle of paper ...
... "When Computers Imitate the Workings of Brain",
Boston Business Journal, Vol. 14 (June 10, 1994), pp 24.
[Vo94] Vogel, William, "Minimally Connective, Auto-Associative, Neural
Networks", Connection Science, Vol. 6 (January 1, 1994), pp 461.
[Wo96] Internet Information. http://www.mindspring.com/~zsol/nnintro.html http://ourworld.compuserve.com/homepages/ITechnologies/ http://sharp.bu.edu/inns/nn.html http://www.eeb.ele.tue.nl/neural/contents/neural_networks.html http://www.ai.univie.ac.at/oefai/nn/ http://www.nd.com/welcome/whatisnn.htm http://www.mindspring.com/~edge/neural.html http://vita.mines.colorado.edu:3857/lpratt/applied-nnets.html
[Za93] Zahedi, F. Intelligent Systems for Business: Expert Systems with Neural
Networks, Wadsworth Publishing Company, California, 1993.
.
Andy Clark strongly argues for the theory that computers have the potential for being intelligent beings in his work “Mindware: Meat Machines.” The support Clark uses to defend his claims states the similar comparison of humans and machines using an array of symbols to perform functions. The main argument of his work can be interpreted as follows:
This Grand Challenge project is on reverse engineering the brain, and how the technology for human brain implants has developed thus far and how it will advance in the future. Reverse engineering the brain is one of fourteen Grand Challenges, which, if solved, will advance humanity. The ultimate goal of this challenge is to be able to fully simulate a human brain and understand how consciousness, thoughts, personality and free will function [Lipsman, Nir, Glannon, 2012]. As a result, computers will be enhanced, artificial intelligence will be unparalleled, and implants will aid damaged brains. Overall, reverse engineering the brain will provide massive advancements that will propel humanity into the next generation of technology.
Kandel, E. R., J. H. Schwarz, and T. M. Jessel. Principles of Neural Science. 3rd ed. Elsevier. New York: 1991.
Stergiou, C., & Siganos, D. (2011, August 6). Neural Networks. Retrieved August 6, 2011, from
The introduction to the article was interesting, “What has billions of individual pieces, trillions of connections, weights about 1.4 kilograms, and works on electrochemical energy? If you guessed a minicomputer you’re wrong. If you guessed the human brain, you’re correct!” I did not know the brain had quite this many connections. After reading our chapter I really started to grasp the complexity of the human brain and the amount of energy it expends. I felt that the article lacked facts like these further in. There was very little empirical numbers offered by the author Eric Chudler.
"My name is Dorothy," said the girl, "and I am going to the Emerald City, to ask the Oz to send me back to Kansas."
look like it understands a story, it can do no more than "go through the
Gaudin, Sharon. "Intel: Chips in Brains Will Control Computers by 2020." Computerworld. N.p., 19 Nov. 2009. Web. 11 Nov. 2013.
The traditional notion that seeks to compare human minds, with all its intricacies and biochemical functions, to that of artificially programmed digital computers, is self-defeating and it should be discredited in dialogs regarding the theory of artificial intelligence. This traditional notion is akin to comparing, in crude terms, cars and aeroplanes or ice cream and cream cheese. Human mental states are caused by various behaviours of elements in the brain, and these behaviours in are adjudged by the biochemical composition of our brains, which are responsible for our thoughts and functions. When we discuss mental states of systems it is important to distinguish between human brains and that of any natural or artificial organisms which is said to have central processing systems (i.e. brains of chimpanzees, microchips etc.). Although various similarities may exist between those systems in terms of functions and behaviourism, the intrinsic intentionality within those systems differ extensively. Although it may not be possible to prove that whether or not mental states exist at all in systems other than our own, in this paper I will strive to present arguments that a machine that computes and responds to inputs does indeed have a state of mind, but one that does not necessarily result in a form of mentality. This paper will discuss how the states and intentionality of digital computers are different from the states of human brains and yet they are indeed states of a mind resulting from various functions in their central processing systems.
Crevier, D. (1999). AI: The tumultuous history of the search for Artificial Intelligence. Basic Books: New York.
Scientists claim that devices with Artificial Intelligence will replace office workers during next 5 years (Maksimova).According to this statement it is possible to say that AI has a great influence on humanity. Pursuant to Oxford Dictionary Artificial Intelligence or AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages(dictionary).Firstly, this research will analyze positive and negative impacts of development of Artificial Intelligence on economic sphere. Then, author going to discuss social effects of Artificial Intelligence. After the considering all perspectives that link to this topic, the last step will be to draw a conclusion.
The brain is the most complex organ in the human body. It produces our every thought, action, memory, feeling and experience of the world. This mass of tissue in our heads has the consistency of butter and weighs approximately 1.4kg. It contains a staggering one hundred billion nerve cells. These nerve cells communicate and connect with each other using tiny electrical impulses and chemical signals. The number and variety of connections these nerve cells make between each other is mind-boggling. Each brain cell can and does connect with thousands or even tens of thousands of other brain cells. These cells in our brains are forming millions of different and new connections every second. The pattern and strength of these connections is constantly changing and it is these that make every brain unique. It is these changing connections that enable us to learn information, store memories, develop habits and gives us our distinct personalities (Nolte, 2002).
Our neural system creates mental connections and synapses using neurons, the most basic element of the brain. Memories and connections are categorized into neural networks, where memories build upon each other to create new behaviors and skills. By recalling these memories and skills, we then are strengthening our neurons and the connections it makes.
Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial intelligence that tries to find an answer to this question: how to make computer learn?
Artificial neural networks (ANNs) approximates the probable function that will likely produce the best output. This is done through extensive training of the system and the use of ample training rules which allows the ANNS to recognise repetitive paradigms and use them to solve problems that the system has not encountered before. These systems model the mammalian cerebral cortex (the brain) and its neurons, hence the name artificial neural networks. Before understanding the complex structure of an artificial neural network, a rudimentary knowledge of an organic neural network is essential.