Cellular automata
A Cellular Automata can be viewed as an autonomous Finite State Machine[FSM] consisting of a number of cells.
A Cellular Automaton consists of a regular grid of cells, each as a finite number of states such as On and Off.An initial state [time t=0] is selected by assigning a state for each cell. The rule for updating the state of cells is the same for each cell and does not change over time.
Cellular Automata can also be viewed as a simple model of a spatially extended decentralized system made up of a number of individual components[cells].Cellular Automata comes in different shapes and varieties.One of the fundamental properties of a cellular automaton is the type of grid on which it is computed. The number of colors (or distinct states) a cellular automaton may assume must also be specified. This number is generally an integer, with (binary) being the simplest choice. For a binary automaton,"white" is called for color 0 and "black" for color 1. However, cellular automata having a continuous range of possible values may also be considered.
Classification
Wolfram,defined four classes into which cellular automata and several other simple computational models can be divided depending on their behavior.In order of complexity, the classes are:-
• Class I CAs evolve4 to a uniform configuration of cell states, from nearly any initial configuration. This state can be thought of in dynamical systems terms as a ‘point attractor’, or ‘limit point’. As one would suspect, the rules for class I CAs map from most or all possible neighbour configurations to the same new state. Initial lattice configurations do exist for some class I CAs that lead to non-trivial cycles, but these are very rare.
• CAs in Class II evolve to pro...
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...hborhood, additive CA are ideally suited for V LSI implementation. Different applications ranging from V LSI test domains to the design of a hardwired version of different CA based schemes have been proposed.
Pattern Recognition
The advent of neural net with the seminal work of Hopfield , popularized the use of machine intelligence techniques in the pattern recognition. However, the dense and inherent structure of neural networks is not suitable for VLSI implementation. So, researchers in the neural network domain tried to simplify the structure of the neural network by pruning unnecessary connections. Simultaneously, the CA research community explored the advantages of the sparse network structure of cellular automata for relevant applications. The hybridization of cellularity and neural network has given rise to the popular concept of cellular neural networks.
In “Life of a Cell,” the author uses rhetoric and figurtic language to reassure peoples fear of disease and to assure them the bodies system is fully capable to attacking anything that would be an issue or illness to itself. He writes about the fear of germs and bacteria; the ineveitibility of germs attacking a cell system. He writes about the many preventions and precautions others take to avoid diseases which metaphorically they “come after them for profit.” Thomas writes this in less scienfitic terms that an average person could comprehend and be assured that their fears are irritaonal to an extent. By using metaphors, similes, personification, and imagery, the reader is reassured that the human body is fully capable of handling diseases.
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:
The Lives of a Cell: Notes of a Biology Watcher by Lewis Thomas consists of short, insightful essays that offer the reader a different perspective on the world and on ourselves.
In this paper I will evaluate and present A.M. Turing’s test for machine intelligence and describe how the test works. I will explain how the Turing test is a good way to answer if machines can think. I will also discuss Objection (4) the argument from Consciousness and Objection (6) Lady Lovelace’s Objection and how Turing responded to both of the objections. And lastly, I will give my opinion on about the Turing test and if the test is a good way to answer if a machine can think.
Stergiou, C., & Siganos, D. (2011, August 6). Neural Networks. Retrieved August 6, 2011, from
Rumelhart, D.E., Hinton, G.E., & McClelland, J.L. (1986). A General Framework for Parallel Distributed Processing. In Rumelhart, D.E., & McClelland, J.L. and the PDP Research Group (1986) Eds. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1: Foundations. MIT Press: Cambridge, MA.
When people think of Neural Networks the first thing that comes to mind is our brain, while the second is computers. That comparison is due to the fact that the way that computers work is the same as a Neural Network, “the network is built up by connections between these processing units” (Sandhu, Robin. “5 examples of biomimetic technology.” LifeWire. LifeWire. 19 Oct. 2016. Web). Having to search for animals that have unique capabilities may seem boring to most people, but they didn’t even have to get off the couch to find out about Neural Networks because that was inside them all along, sometimes it’s the more obvious skills that slip past
Said E.Al-Khamy[33], defines the fractal dimension D as the measure of the complexity or the space filling ability of the fractal shape. The fractal dimension (a positive real number) is either equal or greater than the topological dimension (a positive integer number). Various formulation and methods exists to find the fractal dimension of fractal (self-similar) structure. One such method which is most popular is formulated as-
The Turing Machine is a simple kind of computer. It is limited to reading and writing symbols on a tape and moving the tape along to the left or right. The tape is marke...
Let us see now how this algorithm works. The algorithms randomly creates solutions. Each one of these solutions has a fitness value based on some criteria. Those solutions of a specific problem are also called Phenotype, while the encoding of each solution is called Genotype. We refer on Representation as the procedure of establish the mapping between genotypes and phenotypes. Representation is used as in two different ways. As mentioned before, representation establish the mapping between the genotype and the phenotype. This means that representation could encode ore decode the candidate solutions.
The number of synaptic inputs recieved by each nerve cell in our (human) nervous system varies from 1-100,000! This wide range reflects the fundamental purpose of nerve cells, to integrate info from other neurons.
The cytoskeleton is a highly dynamic intracellular platform constituted by a three-dimensional network of proteins responsible for key cellular roles as structure and shape, cell growth and development, and offering to the cell with "motility" that being the ability of the entire cell to move and for material to be moved within the cell in a regulated fashion (vesicle trafficking)’, (intechopen 2017). The cytoskeleton is made of microtubules, filaments, and fibres - they give the cytoplasm physical support. Michael Kent, (2000) describes the cytoskeleton as the ‘internal framework’, this is because it shapes the cell and provides support to cellular extensions – such as microvilli. In some cells it is used in intracellular transport. Since the shape of the cell is constantly changing, the microtubules will also change, they will readjust and reassemble to fit the needs of the cell.
According to Descartes, non-human animals are automata, which imply that their behavior is completely explicable with regards to physical mechanisms (Kirk, 2011). The philosopher explored the concept of a machine that looked and behaved like a human being. Following his attempts to unmask such a machine, Descartes concluded that no machine could behave like a human being and that characteristically explaining human behavior needed something beyond the phy...
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.
Looking at the world where we live everything in someway is connected. Our world is not simple and in fact consists of multiple complex systems. Some everyday examples of complex systems are the brain, immune system, insect colonies, and even social networks such as Facebook and twitter. So what exactly do all these have in common in order to be a complex system? First is the fact that each one has a large amount of simple components that work together by communication through signals without being under leadership. But not all systems are exactly the same so we can break it down further into chaotic systems, complex adaptive systems, and nonlinear systems. Chaotic systems differ in that they are non-linear and are sensitive to initial conditions. Therefore any uncertainty in the system will not produce an outcome that can be predicted later on. A good example of a chaotic system would be the stock market because the prediction of its outcome is unknown due to its sensitivity to initial conditions. Complex adaptive systems are just like they sound. They are capable of adapting to the environment such as the immune system. It’s white blood cells work together to recognize foreign bodies and create antibodies for future encounters.