Chapter 3 & 19 Assignment Example | Topics and Well Written Essays - 750 words

Chapter 3 & 19 - Assignment Example Offering customized packaging for customers is however an example of a secondary value and may change with financial constraint with the aim of minimizing cost while retaining utility (Kotler and Armstrong, 2012). A company’s microenvironment and macro environment influences the entity’s decision through inducing constraints or opportunities. Factors in both scopes influences an organization’s decisions as it tried to adjust to constraints and opportunities that the environment offers. There are however many differences between macro and microenvironments. Micro environmental factors are limited to an organization or just a few organizations while macroeconomic factors are significant to all organizations in a set up. Examples of macroeconomic environment factors are political and cultural conditions, factors that affect all organizations, while suppliers and competitors are examples of micro environmental factors and their effects are limited to the subject organization (Kotler and Armstrong, 2012). Exporting, joint venture, and direct investments are some of the strategies for adapting products into a global market. Exporting involves production in a country and then moving the products to the target market in a foreign country and may be direct or indirect. Joint venture however involves collaboration with natives from the target market for product delivery while direct investment involves independent ventures in the target market. Joint venture is the best strategy because it helps an entity to manage barriers market entry by ensuring a link between the entity and natives in the target market, challenges that are significant in cases of export and direct investments (Kotler and Armstrong, 2012). Tariffs and quotas are economic policies for regulating international businesses. Both policies can be used either to increase the flow of commodities across

Ambassador for Ethopia paper Term Example | Topics and Well Written Essays - 500 words

Ambassador for Ethopia - Term Paper Example Religious allegiances of Ethiopian population are generally mixed, with Orthodox Christianity being followed by 43,5% of religious population, with different forms of Islamic faith (33,9%) and Protestant Christian churches (18,6%) being the second and the third most popular confessions, respectively (â€Å"Ethiopia†, 2011). As regards population density, it should be noted that it amounts to 186/sq. mi, and therefore Ethiopia ranks as the 123rd by population density among the nations of the world. The annual population growth of Ethiopia exceeds 3.194%, with 42.99 births/1,000 population (â€Å"Ethiopia†, 2011). This would make Ethiopia 8th among the world’s countries by population growth and 6th by birth rate, respectively. Total fertility rate equals 6,02 children born/woman. However, the extremely high infant mortality level (77.12 deaths/1,000 live births) definitely presents a difficulty to further demographic development and stabilization of the country, wh ile life expectancy at birth amounts to mere 56.19 years (â€Å"Ethiopia†, 2011).

Reasoning in Artificial Intelligence (AI): A Review

Reasoning in Artificial Intelligence (AI): A Review 1: Introduction Artificial Intelligence (AI) is one of the developing areas in computer science that aims to design and develop intelligent machines that can demonstrate higher level of resilience to complex decision-making environments (Là ³pez, 2005[1]). The computations that at any time make it possible to assist users to perceive, reason, and act forms the basis for effective Artificial Intelligence (National Research Council Staff, 1997[2]) in any given computational device (e.g. computers, robotics etc.,). This makes it clear that the AI in a given environment can be accomplished only through the simulation of the real-world scenarios into logical cases with associated reasoning in order to enable the computational device to deliver the appropriate decision for the given state of the environment (Là ³pez, 2005). This makes it clear that reasoning is one of the key elements that contribute to the collection of computations for AI. It is also interesting to note that the effectiveness of the r easoning in the world of AI has a significant level of bearing on the ability of the machine to interpret and react to the environmental status or the problem it is facing (Ruiz et al, 2005[3]). In this report a critical review on the application of reasoning as a component for effective AI is presented to the reader. The report first presents a critical overview on the concept of reasoning and its application in the Artificial Intelligence programming for the design and development of intelligent computational devices. This is followed by critical review of selected research material on the chosen topic before presenting an overview on the topic including progress made to date, key problems faced and future direction. 2: Reasoning in Artificial Intelligence 2.1: About Reasoning Reasoning is deemed as the key logical element that provides the ability for human interaction in a given social environment as argued by Sincà ¡k et al (2004)[4]. The key aspect associated with reasoning is the fact that the perception of a given individual is based on the reasons derived from the facts that relative to the environment as interpreted by the individual involved. This makes it clear that in a computational environment involving electronic devices or machines, the ability of the machine to deliver a given reason depends on the extent to which the social environment is quantified as logical conclusions with the help of a reason or combination of reasons as argued by Sincà ¡k et al (2004). The major aspect associated with reasoning is that in case of human reasoning the reasoning is accompanied with introspection which allows the individual to interpret the reason through self-observation and reporting of consciousness. This naturally provides the ability to develop the resilience to exceptional situations in the social environment thus providing a non-feeble minded human to react in one way or other to a given situation that is unique in its nature in the given environment. It is also critical to appreciate the fact that the reasoning in the mathematical perspective mainly corresponds to the extent to which a given environmental status can be interpreted using probability in order to help predict the reaction or consequence in any given situation through a sequence of actions as argued by Sincà ¡k et al (2004). The aforementioned corresponds with the case of uncertainty in the environment that challenges the normal reasoning approach to derive a specific conclusion or decision by the individual involved. The introspective nature developed in humans and some animals provides the ability to cope with the uncertainty in the environment. This adaptive nature of the non-feeble minded human is the key ingredient that provides the ability to interpret the reasons to a given situation as opposed to merely following the logical path that results through the reasoning process. The reasoning in case of AI which aims to develop the aforementioned in the electronic devices to perform complex tasks with minimal human intervention is presented in the next section. 2.2: Reasoning in Artificial Intelligence Reasoning is deemed to be one of the key components to enable effective artificial programs in order to tackle complex decision-making problems using machines as argued by Sincà ¡k et al (2004). This is naturally because of the fact that the logical path followed by a program to derive a specific decision is mainly dependant on the ability of the program to handle exceptions in the process of delivering the decision. This naturally makes it clear that the effective use of the logical reasoning to define the past, present and future states of the given problem alongside the plausible exception handlers is the basis for successfully delivering the decision for a given problem in chosen environment. The key areas of challenge in the case of reasoning are discussed below (National Research Council Staff, 1997). Adaptive Software – This is the area of computer programming under Artificial Intelligence that faces the major challenge of enabling the effective decision-making by machines. The key aspect associated with the adaptive software development is the need for effective identification of the various exceptions and the ability to enable dynamic exception handling based on a set of generic rules as argued by Yuen et al (2002)[5]. The concept of fuzzy matching and de-duplication that are popular in case of software tools used for cleansing data cleansing in the business environment follow the above-mentioned concept of adaptive software. This is the case there the ability of the software to decide the best possible outcome for a given situation is programmed using a basic set of directory rules that are further enhanced using references to a variety of combinations that comprise the database of logical combinations for reasons that can be applied to a given situation (Yuen et al, 20 02). The concept of fuzzy matching is also deemed to be a major breakthrough in the implementation of adaptive programming of machines and computing devices in Artificial Intelligence. This is naturally because of the fact that the ability of the program to not only refer to a set of rules and associated reference but also to interpret the combination of reasons derived relative to the given situation prior to arriving on a specific decision. From the aforementioned it is evident that the effective development of adaptive software for an AI device in order to perform effective decision-making in the given environment mainly depends on the extent to which the software is able to interpret the reasons prior to deriving the decision (Yuen et al, 2002). This makes it clear that the adaptive software programming in artificial intelligence is not only deemed as an area of challenge but also the one with extensive scope for development to enable the simulation of complex real-world problem s using Artificial Intelligence. It is also critical to appreciate the fact that the adaptive software programming in the case of Artificial Intelligence is mainly focused on the ability to not only identify and interpret the reasons using a set of rules and combination of outcomes but also to demonstrate a degree of introspection. In other words the adaptive software in case of Artificial Intelligence is expected to enable the device to become a learning machine as opposed to an efficient exception handler as argued by Yuen et al (2002). This further opens room for exploring into knowledge management as part of the AI device to accomplish a certain degree of introspection similar to that of a non-feeble minded human. Speech Synthesis/Recognition – This area of Artificial Intelligence can be deemed to be a derivative of the adaptive software whereby the speech/audio stream captured by the device deciphers the message for performs the appropriate task (Yuen et al, 2002). The speech recognition in the AI field of science poses key issues of matching, reasoning to enable access control/ decision-making and exception handling on top of the traditional issues of noise filtering and isolation of the speaker’s voice for interpretation. The case of speech recognition is where the aforementioned issues are faced whilst in case of speech synthesis using computers, the major issue is the decision-making as the decision through the logical reasoning alone can help produce the appropriate response to be synthesised into speech by the machine. The speech synthesis as opposed to speech recognition depends only on the adaptive nature of the software involved as argued by Yuen et al (2002). This is due to the fact that the reasons derived form the interpretation of the input captured using the decision-making rules and combinations for fuzzy matching form the basis for the actual synthesis of the sentences that comprises the speech. The grammar associated with the sentences so framed and its reproduction depends heavily on the initial decision of the adaptive software using the logical reasons identified for the given environmental situation. Hence the complexity of speech synthesis and recognition poses a great challenge for effective reasoning in Artificial Intelligence. Neural Networks – This is deemed to be yet another key challenge faced by Artificial Intelligence programming using reasoning. This is because of the fact that neural networks aim to implement the local behaviour observed by the human brain as argued by Jones (2008)[6]. The layers of perception and the level of complexity associated through the interaction between different layers of perception alongside decision-making through logical reasoning (Jones, 2008). This makes it clear that the computation of the decision using the neural networks strategy is aimed to solving highly complex problems with a greater level of external influence due to uncertainties that interact with each other or demonstrate a significant level of dependency to one another. This makes it clear that the adaptive software approach to the development of the reasoned decision-making in machines forms the basis for neural networks with a significant level complexity and dependencies involved as argued by r efenrece8. The Single Layer Perceptions (SLP) discussed by Jones (2008) and the representation of Boolean expressions using SLPs further makes it clear that the effective deployment of the neural networks can help simulate complex problems and also provide the ability to develop resilience within the machine. The learning capability and the extent to which the knowledge management can be incorporated as a component in the AI machine can be defined successfully through identification and simulation of the SLPs and their interaction with each other in a given problem environment (Jones, 2008). The case of neural networks also opens the possibility of handling multi-layer perceptions as part of adaptive software programming through independently programming each layer before enabling interaction between the layers as part of the reasoning for the decision-making (Jones, 2008). The key influential element for the aforementioned is the ability of the programmer(s) to identify the key input and output components for generating the reasons to facilitate the decision-making. The backpropagation or backward error propagation algorithm deployed in the neural networks is a salient feature that helps achieve the major aspect of learning from mistakes and errors in a given computer program as argued by Jones (2008). The backpropagation algorithm in the multi-layer networks is one of the major areas where the adaptive capabilities of the AI application program can be strengthened to reflect the real-world problem solving skills of the non-feeble minded human as argued by Jones (2008). From the aforementioned it is clear that the neural networks implementation of AI applications can be achieved to a sustainable level using the backpropagation error correction technique. This self-correcting and learning system using the neural networks approach is one of the major elements that can help implement complex problems’ simulation using AI applications. The case of reasoning discussed earlier in the light of the neural networks proves that the effective use of the layer-based approach to simulate the problems in order to allow for the interaction will help achieve reliable AI application development methodologies. The discussion presented also reveals that reasoning is one of the major elements that can help simulate real-world problems using computers or robotics regardless of the complexity of the problems. 2.3: Issues in the philosophy of Artificial Intelligence The first and foremost issue faces in the case AI implementation of simulating complex problems of the real-world is the need for replication of the real-world environment in the computer/artificial world for the device to compute the reasons and derive upon a decision. This is naturally due to the fact that the simulation process involved in the replication of the environment for the real-world problem cannot always account for exceptions that arise due to unique human behaviour in the interaction process (Jones, 2008). The lack of this facility and the fact that the environment so created cannot alter itself fundamentally apart from being altered due to the change in the state of the entities interacting within the simulated environment makes it a major hurdle for effective AI application development. Apart from the real-world environment replication, the issue faced by the AI programmers is the fact that the reasoning processes and the exhaustiveness of the reasoning is limited to the knowledge/skills of the analysts involved. This makes it clear that the process of reasoning depending upon non-feeble minded human’s response to a given problem in the real-world varies from one individual to another. Hence the reasons that can be simulated into the AI application can only be the fundamental logical reasons and the complex derivation of the reasons’ combination which is dependant on the individual cannot be replicated effectively in a computer as argued by Là ³pez (2005). Finally, the case of reasoning in the world of Artificial Intelligence is expected to provide a mathematical combination to the delivery of the desired results which cannot be accomplished in many cases due to the uniqueness of the decision made by the non-feeble minded individual involved. This poses a great challenge to the successful implementation of AI in computers and robotics especially for complex problems that has various possibilities to choose from as result. 3: Critical Summary of Research 3.1: Paper 1 – Programs with Common Sense by Dr McCarthy The rather ambitious paper presented by Dr McCarthy aims to provide an AI application that can help overcome the issues in speech recognition and logical reasoning that pose significant hurdles to the logical reasoning in AI application development. However, the approach to the delivery of the aforementioned in the form of an advice taker is a rather feeble approach to the AI representation of the solution to a problem of greater magnitude. Even though the paper aims to provide an Artificial Intelligence application for verbal reasoning processes that are simple in nature, the fact that the interpretation of the verbal reasoning in the light of the given problem relative to an environment is not a simple component to be simulated with ease prior to achieving the desired outcome as discussed in section 2. â€Å"One will be able to assume that the advice taker will have available to it a fairly wide class of immediate logical consequences of anything it is told and its previous knowledge†. (Dr McCarthy, Pg 2). This statement by the author in the research paper provides room for the discussion that the advice taker program proposed by Dr McCarthy is aimed to deliver an AI application using knowledge management as a core component for logical reasoning. This is so because of the nature of the statement which implies that the advice taker program will be able to deliver its decision through access to a wide range of immediate logical consequences of anything it is told and its previous knowledge. This makes it clear that the advice taker software program is not a non-viable approach as the knowledge management strategy for logical reasoning is a component under debate as well as development over a wide range of scientific applications related problems simulation using AI. The Two S tage Fuzzy Clustering based on knowledge discovery presented by Qain in Da (2006)[7] is a classical example for the aforementioned. It is also interesting to note that the knowledge management aspect of artificial intelligence programming is mainly dependant on the speed related to the access and processing of the information in order to deliver the appropriate decision relative to the given problem (Yuen et al, 2002). A classical example for the aforementioned would be the use of fuzzy matching for validation or suggestion list generation on Online Transaction Processing Application (OLTP) on a real-time basis. This is the scenario where a portion of the data provided by the user is interpreted using fuzzy matching to arrive upon a set of concrete choices for the user to choose from (Jones, 2008). The process of choosing the appropriate option from the given suggestion list by the individual user is the component that is being replaced using Artificial Intelligence in machines to c hoose the best fit for the given problem. The aforementioned is evident in case of the advice taker software program that aims to provide a solution for responding to verbal reasoning processes of the day-to-day life of a non-feeble minded individual. The author’s objective ‘to make programs that learn from their experience as effectively as humans do’, makes it clear that the knowledge management approach with the ability of the program to utilise a database type storage option to store/access its knowledge and previous experiences as part of the process. This makes it clear that the advice taker software maybe a viable option if the processing speed related to the retrieval and storage of information from a database of such magnitude which will grow in size at an exponential rate is made available for the AI application. The aforementioned approach can be achieved by the use grid computing technology as well as other processing capabilities with the availability of electronic components at affordable prices on the market. The major issue however is the design for such an application and the logical reasoning processes of retrieving such information to arrive at a decision for a given problem. Form the discuss ion presented in section 2 it is evident that the complexity in the level of logical reasoning results in higher level of computation to account for external variants thus providing the decision appropriate to the given problem. This cannot be accomplished without the ability to deliver process through the existing logical reasons from the application’s knowledgebase. Hence the processing speed and efficiency of computation in terms of both the architecture and software capability is a question that must be addressed to implement such a system. Although the advice taker software is viable in a hardware architecture perspective, the hurdle is the software component that must be capable of delivering the abstraction level discussed by the author. This is because, the ability to change the behaviour of the system by merely providing verbal commands from the user which is the main challenge faced by the AI application developers. This is so because of the fact that the effective implementation of the aforementioned can be achieved only with the effective usage of the speech recognition and logical reasoning that is already available to the software for incorporating the new logical reason as an improvement or correction to the existing set-up of the application. This approach is the major hurdle which also poses the challenge of identifying the key speech patterns that are deemed to be such corrective commands over the statements’ classification provided by the user author for providing information to the application. Fr om the above arguments it can be concluded that the author’s statement – â€Å"If one wants a machine to be able to discover an abstraction, it seems most likely that the machine must be able to represent this abstraction in some relative simple way† – is not a task that is easily realisable. It is also necessary to address the issue that the abstractions that can be realised by the user can be realised by an AI application only if the application being used already has a set of reasons or room for learning the reasons from existing reasons prior to decision-making. This process can be accomplished only through complex algorithms as well as error propagation algorithms discussed in section 2.3. This makes it clear that the realization of the advice taker software’s capability to deliver to represent any abstraction in a relative simpler way is far fetched without the appropriate implementation of self-corrective and learning algorithms. The fact th at learning is not only through capturing the previous actions of the application in similar scenarios but also to generate logical reasons based on the new information provided to the application by the users is an aspect of AI application which is still under development but the necessary ingredient for the advice taker software. However, considering the timeline associated with the research presented by Dr McCarthy and the developments till date, one can say that the AI application development has seen higher level of developments to interpret information from the user to provide an appropriate decision using the logical reasoning approach. The author’s argument that for a machine to learn arbitrary behaviour simulating the possible arbitrary behaviours and trying them out is a method that is extensively used in the twenty-first century implementation of the artificial intelligence for computers and robotics. The knowledge developed in the machines programmed using AI is m ainly through the use of the arbitrary behaviours simulated and their results loaded into the machine as logical reasons for the AI application to refer when faced with a given problem. Form the arguments of the author on the five features necessary for an AI application hold viable in the current AI application development environment although the ability of the system to create subroutines which can be included into procedures as units is still a complex task. The magnitude of the processor speed and related requirements on the hardware architecture is the problem faced by the developers as opposed to the actual development of such a system. The author’s statement that ‘In order for a program to be capable of learning something it must first be capable of being told it’ is one of the many components of the AI application development that has seen tremendous development since the dawn of the twenty-first century (Jones, 2008). The multiple layer processing strategy to address complex problems in the real world that have influential variants both within the input provided as well as the output in the current state of AI application development is synonymous to the above statement by Dr McCarthy. The neural networks for adaptive behaviour presented in great detail by Pfeifer and Scheier (2001)[8] further justifies the aforementioned. This also opens room for discussion on the extent to which the advice taker application can learn from experience through the use of neural networks as an adaptive behaviour component for programming robots and other devices facing complex real-world problems. This is the kind of adaptive behaviour that is represented by the advice taker application by Dr McCarthy who described it nearly half a century ago. The viability of using neural networks to take comments in the form of sentences (imperative or declarative) is plausible with the use of the adaptive behaviour strategy described above using neural networks. Finally, the construction of the advice taker described by the author can be met with in the current AI application development environment although the viability of the same would have been an enormous challenge at the time when the paper was published. The advice taker construction in the twenty-first century AI environment can be accomplished using either a combination of computers and robotics or one of the two as a sole operating environment. So development of the AI application either using computers or robotics for the delivery of the advice taker is plausible depending upon the delivery scope for the application and its operational environment. Some of the hurdles faced however would be with the speech recognition and the ability to distinguish imperative sentences to declarative sentences. The second issue faced in the case of the advice taker will be the scope of application as the simulation of various instances for generating the knowledge database is plausible only withi n the defined scope of the application’s target environment as opposed to the non-feeble human mind that can interact with multiple environments at ease. The multiple layer neural networks approach may help tackle the problem only to a certain level as the ability to distinguish between different environments when formed as layers is not easily plausible without the knowledge on its interpretation stored within the system. Finally, a self-corrective system for AI application is plausible in the twenty-first century but the self learning system using the logical reasons provided is still scarce and requires a greater level of design resilience to account for input and output variants of the system. The stimulus-response forms described by the author in the paper is realisable using the multiple layer neural networks implementation with the limitation on the scope of the advice taker restricted to a specific problem or set of problems. The adaptive behaviour simulated using the neural networks mentioned earlier justifies the ability to achieve the aforementioned. 3.2: Paper 2 – A Logic for Default Reasoning Default reasoning in the twenty-first century AI applications is one of the major elements that attribute to the effective functioning of the systems without terminating unexpectedly unable to handle the exception raised due to the combination of the logic as argued by Pfeifer and Scheier (2001). This is naturally because of the fact that the effective use of the default reasoning process in the current AI application development environment aims to provide default reasoning when an exhaustive list of the reasons that are simulated and rules combinations are effectively managed. However, the definition of exhaustive or the perception of an exhaustive list for the development in a given environment is limited to the number of simulations that the users can develop at the time of AI application design and the adaptive capabilities of the AI system post implementation (refernece8). This makes it clear that the effective use of the default reasoning in the AI application development can be achieved only through handling a wide variety of exceptional conditions that arise in the normal operating environment for the problem being simulated (Pfeifer and Scheier, 2001). In the light of the above arguments the assertion by the author on the default reasoning as beliefs which may well be modified or rejected by subsequent observations holds true in the current AI development environment. The default reasoning strategy described by the author is deemed to be a critical component in the AI application development mainly because of the fact that the defaulting reasons are not only aimed to prevent unhandled exceptions leading to abnormal termination of the program but also the effective learning from experience strategy implemented within the application. The learn from experience described in the section 2 as well as the discussion presented in section 3.1 reveal that the assignment of a default reason for an adaptive AI application will provide room for identifying the exceptions that occur in the course of solving problems thus capturing new exceptions that can replace the existing default value. Furthermore, the fact that the effective use of the default reasoning strategy in AI applications also limits the learning capabilities of the application in cases where the adaptive behaviour of the system is not effective although preventing abnormal termination of the sys tem using the default reason. The logical representation of the exceptions and defaults and the interpretation used by the author to interpret the phrase ‘in the absence of any information to the contrary’ as ‘consistent to assume’ justifies the aforementioned. It is further evident from the arguments of the author that the default reason creation and its implementation into the neural network as a set of logical reasons are complex than the typical case wise conditional analysis on establishing a given condition holds true to the situation on hand. Another interesting factor to the aforementioned it the fact that the definition of the conditions must incorporate room for partial success owing to the fact that the typical logical approach of success or failure do not always apply to the AI application problems. Hence it is necessary to ensure that the application is capable of accommodating partial success as well as accounting for a concrete number to the given problem in order to gener ate an appropriate decision. The discussion on the non-monotonic character of the application defines the ability to effectively formulate the condition for default reasoning rather than merely defaulting due to the failure of the system to accommodate for the changes in the environment as argued by Pfeifer and Scheier (2001). Carbonell (1980)[9] further argues that the type hierarchies and their influence on the AI system have a significant bearing on the default reasoning strategies defined for a given AI application. This is naturally because of the fact that the introduction of the type hierarchies in the AI application will provide the application to not only interpret the problem against the set of rules and reference data stored as reasons but also assign it within the hierarchy in order to identify the viability of applying a default reason to the given problem. The arguments of Carbonell (1980) on Single-Type and Multi-Type inclusion with either strict or non-strict partiti oning justify the above-mentioned argument. It is further critical to appreciate the fact that the effective implementation of the type hierarchy in a logical reasoning environment will provide the AI application with greater level of granularity to the definition and interpretation of the reasons pertaining to a given problem (Pfeiffer and Scheier, 2001). It is this state of the AI application that can help achieve a significant level of independence and ability to interact effectively in the environment with minimal human intervention. The discussion on the inheritance mechanisms presented by Carbonell (1980) alongside the implementation of the inheritance properties as the basis for the implementation of AI systems in the twenty-first century (Pfeifer and Scheier, 2001) further justify the need for default reasoning as an interactive component as opposed to a problem solving constant to prevent abnorm

Star child evolution in 2001 :: essays research papers

The Evolution of the Star-Child   Ã‚  Ã‚  Ã‚  Ã‚  Film both reflects and creates social culture. Indeed, a film indicates social trends, presents ideas, and analyzes history for its contemporary time period; thus, by viewing a film it becomes possible to infer and make judgments about a society's culture. The filmmaker's message is embedded within the plot and symbolism, and filmmakers often critique social culture through their movies. It is possible to view the evolution of culture through the progression of films over time.   Ã‚  Ã‚  Ã‚  Ã‚  Religious films in the pre-1968 era distinguish themselves as literal interpretations of the Old and New Testaments. America, in this era, held religion as central to everyday life. DeMille's â€Å"King of Kings† and similar movies that follow in form support this inference. Consider the context of DeMille's 1932 movies, â€Å"Sign of the Cross.† Depression and the great dustbowl were commonplace across America. The masses embraced religion and this is evident in the literal bible story films. Additionally, the righteous people in the films represented the working class people of America, and the blasphemers represented the elite minority. They were usually powerful, corrupt, and sometimes Jewish. American culture has evolved as technology has advanced. In 1968, the paradigm of religion in film shifted with the release of Kubrick's â€Å"2001: A Space Odyssey.† Rebellion, social activism, drugs, sex and most importantly, technology, dominated social culture. Kubrick's film challenges all previous religious film movies, yet this epic movie contains powerful symbols that reflect the changes in social thought. Thus far, evolution of movies and culture has been discussed. â€Å"2001: A Space Odyssey† is interesting because Kubrick realizes this concept of cultural evolution. Thus, he created his movie to embody this concept and manifest itself in man as a physical being. The underlying theme of Kubrick's movie is evolution: the progression of monkeys to humans, humans to machines (HAL), machines to the star-child. That â€Å"2001† concedes to evolution validates science and technology, while it detracts from religion. Thus, this slow paced film indicates the inevitable evolution of all things. The star-child, spurred by the monolith, represents the destiny of humans as they evolve with technology. The monkeys loose their innocence and become corrupted when the monolith presents itself. This is because the monolith prompts the monkeys to explore ways in which odds and ends in nature may be utilized; in other words, the monkeys develop tools. The monolith is not a deity in the sense that it is a physical creator.

Albert Camus’ the Plague Essay

Can God possibly exist in a world full of madness and injustice? Albert Camus and Samuel Beckett address these questions in The Plague and Waiting for Godot. Though their thinking follows the ideals of existentialism, their conclusions are different. Camus did not believe in God, nor did he agree with the vast majority of the historical beliefs of the Christian religion. His stance on Christianity is summed up most simply by his remark that â€Å"in its essence, Christianity (and this is its paradoxical greatness) is a doctrine of injustice. It is founded on the sacrifice of the innocent and the acceptance of this sacrifice† (Bree 49). Camus felt that Jesus Christ was an innocent man who was unjustly killed. This does conflicts with all of Camus’ values. However, Camus did not believe that Jesus was the son of God. Camus’ inability to accept Christian theology is voiced in The Plague by Riex and juxtaposed against the beliefs preached by Father Paneloux (Rhein 42). Paneloux’s attitude toward the plague contrasts sharply with Rieux’s. In his first sermon, he preaches that the plague is divine in origin and punitive in its purpose. He attempts to put aside his desires for a rational explanation and simply accepts God’s will. In this way he is not revolting and therefore falls victim to the plague. Father Paneloux’s belief that there are no innocent victims is shaken as he watches a young boy die of the plague. Camus purposefully describes a long, painful death to achieve the greatest effect on Paneloux: â€Å"When the spasms had passed, utterly exhausted, tensing his thin legs and arms, on which, within forty-eight hours, the flesh had wasted to the bone, the child lay flat, in a grotesque parody of crucifixion† (215). Paneloux cannot deny that the child was an innocent victim and is forced to rethink his ideas. During his second sermon, a change is seen in Father Paneloux. He now uses the pronoun â€Å"we† instead of â€Å"you,† and he has adopted a new policy in which he tells people to believe â€Å"all or nothing† (224). Father Paneloux, as a Christian, is faced with a decision: either he accepts that God is the ultimate ruler and brings goodness out of the evil that afflicts men, or he sides with Rieux and denies God. The conclusion formed by Camus is that because this is a world in which innocent people are tortured, there is no God. Samuel Beckett does not necessarily deny the existence of God in Waiting for Godot. If God does exist, then He contributes to the chaos by remaining silent. The French philosopher Blaise Pascal noted the arbitrariness of life and that the universe works based on percentages. He advocated using such arbitrariness to one’s advantage, including believing in God. If He does not exist, nobody would care in the end, but if He does, a believer is on the safe side all along, so one cannot lose. In this play, either God does not exist, or He does not care. Whichever is the case, chance and arbitrariness determine human life in the absence of a divine power. This ties in with the two tramps’ chances for salvation. As one critic observes, â€Å"For just as man cannot live by bread alone, he now realizes that he cannot live by mere thinking or hanging on in vain to a thread of salvation which does not seem to exist† (Lumley 203). This explains Vladimir and Estragon’s contemplation of suicide after Godot remains absent for yet another day. One could also argue, in the absurd sense, that each man has a fifty-fifty chance of salvation. One of the two prisoners who were crucified with Jesus was given salvation. This element of chance for salvation can also be extended to Pozzo and Lucky in Waiting for Godot. When they come across the two tramps, Pozzo is on his way to sell Lucky because he claims that Lucky has grown old and only hinders him. In this way Pozzo is trying to draw that fifty-fifty chance of salvation for himself. One of the ways in which Lucky hinders him is that Lucky could be the one to be redeemed, leaving Pozzo to be damned. Even Lucky’s speech is concerned with salvation: Given the existence†¦ of a personal God†¦ outside time without who from the heights of divine apathia divide athambia divide apaia loves us dearly with some exceptions for reasons unknown†¦ and suffers†¦ with those who for reasons unknown are plunged in torment. (28) After removing all of Lucky’s nonsensical meanderings, the gist of his speech is that God does not communicate with humans and condemns them for unknown reasons. His silence causes the real hopelessness, and this is what makes Waiting for Godot a tragedy

Similar Themes but Dissimilar Fate

Parallel incidents that can be found in â€Å"Pyramus and Thisbe† and Shakespeare’s â€Å"A Midsummer Night’s Dream† demonstrate Shakespeare’s adaptation of the tragic myth. The mere mention of the myth in Act 5, confirms the playwright’s attempt to imitate the theme of the story. However, in contrast to the other, â€Å"A Midsummer Night’s Dream† being a comedy, offers a happy ending, where lovers are united and blessed by fate. The story of Pyramus and Thisbe occurs as a play within â€Å"A Midsummer Night’s Dream. † It is presented in Theseus’s wedding, supposedly to satirize the love between Lysander and Hermia. However, a twist occurs in the end, giving the play a happy ending, thus departing from the real context of the myth. Parallelism between the two can be recognized in the theme, characterization, and plot. Both use the theme of forbidden love and disobedience. The beginning of the play suggests a close thematic resemblance to the myth. Egeus, the father of Hermia, seeks Theseus’s judgment regarding his disobedient daughter. Hermia, the daughter, is arranged to marry Demetrius, but she loves another man named Lysander, who also occurs in the scene. The lovers are very much in love but Egeus refuses to have them marry because of a promise he has given Demetrius. From this, we can see parallelism in the theme of forbidden love and disobedience of children to their parents. However, the presence of Demetrius is an addition, because in the myth, there is no mention of a third party. As such, Demetrius’s character is one element that suggests Shakespeare’s intention of dissuading from the old lovers’ myth. Like Pyramus and Thisbe, Lysander and Hermia are blinded by their love. That night, they profess their love for one another. Like the lovers in the myth, they seem unable to live without each other. Therefore, they plan to elope to Lysander’s aunt’s house to get married in secrecy. In doing so, they need to trod a forest where they meet a different fate. Similarly, Pyramus and Thisbe, being forbidden to continue with their love, decide to elope the next night and see each other at a monument, where they meet a tragic ending. This event in the plot makes a good resemblance with that of the myth, where lovers decide to take full control of their fate. However, just like the old myth goes, the lovers are doomed not to have everything going according to their plans. In Act 3 of the play, we see Shakespeare’s intention to make a twisted ending with the decision Lysander makes. As Lysander and Hermia lose each other in the forest, we find another parallelism where Pyramus fails to see Thisbe in the designated place. According to the myth, Pyramus does not find Thisbe and thinks that she is slain by a lion. In thinking so, he kills himself, and when Thisbe sees him dying, she does the same. Taking resemblance to the myth, Lysander is supposed to meet his death in the forest. This should happen in the hands of Demetrius who decides to take revenge over him for losing Hermia. However, the death of Lysander that the audience expects does not happen. Instead of dying like Pyramus, Lysander confesses his change of heart to Demetrius, â€Å"Content with Hermia! No; I do repent The tedious minutes I with her have spent. Not Hermia but Helena I love: Who will not change a raven for a dove? (Act 3, Scene 2) He confesses that he has had a change of heart and does not love Hermia anymore. Instead, he loves Helena, the girl who loves Demetrius. Because of this twist, Lysander is saved from potential death. Similarities in characterization can be recognized in the two literary texts. In the myth, the characters are too overcome by love, as in the play. Specifically, we see Thisbe and Hermia with great similarity in their intentions. In her dialogue with Demetrius, Hermia shows characteristics of Thisbe of being passionate to her love and willing to die. She pleads to Demetrius, â€Å"For thou, I fear, hast given me cause to curse, If thou hast slain Lysander in his sleep, Being o'er shoes in blood, plunge in the deep, And kill me too. † (Act 3, Scene 3) This shows the discernment of Hermia to die instead of living without Lysander. Like Thisbe, Hermia feels there is no tomorrow if she will not be reunited with Lysander. More than the characterization, we see a similarity in the portrayal of women in the two plots. Both assign women martyr roles of being true to their lovers. Not only do we see Hermia deeply in love with Lysander, but Helena with Demetrius as well. As such we see that the centuries that passed between the myth foretold and the writing of the play did not effect the way women are perceived in the society. As both literary texts contain, they are pictured as martyrs who await their lovers, willing to give up their life for the sake of the other. The theme, characterization, and some parts of the plot demonstrate similarities between the two works. This only shows that the theme of forbidden love among youths is very recurrent in literature. Specifically, the theme of disobedience to one’s parents reveal imitation of the myth. In addition, the characterization which suggests the theme of loyalty despite death as seen in Hermia clearly resembles that of myth. The readiness to die just to escape suffering, and follow a lover till death are also elements taken from â€Å"Pyramus and Thisbe. † Overall, while the play shares similarities with the myth, Shakespeare’s disposition to end his play in a more romantic way provides the characters with a different fate.