Sociology of community Race and Inequality in Postwar Detroit Essay

Sociology of community Race and Inequality in Postwar Detroit - Essay Example This paper will examine how housing segregation, workplace discrimination and deindustrialization combined over a period of slightly more than twenty five years to fuel one of the most destructive acts of civil disturbance in the country's history and contribute to the city's slow, painful decline. Naturally, the origins of this "urban crisis" can be traced back before World War II. But race riots in 1943 and 1967 provide a convenient frame for the phenomena Sugrue attributes to Detroit's decline. The people of Detroit, black and white, who became the major players in this modern tragedy largely came to the city in the Great Migration between 1916 and 1929, with a later influx during and just after WWII. Oddly enough, the racial conflicts the author describes were not carried to the city by migrants from the South eager to install Jim Crow laws in the North; instead, as Sugrue argues, "The racial politics were thoroughly homegrown" (212). Attached as they were to the personal factors of job availability and home ownership, the city's destructive racial politics can also be laid at the door of the American Dream -- and to other American Dreamers who could not or would not be persuaded to share. However, Sugrue is careful to point out that federal, state and local policies and po litics, including measures meant to enforce equality, helped in no small measure to further divide black and white Detroiters by race, class and employment status. Signs of trouble in Detroit were visible long before the riots of the late '60s, or the election of Mayor Coleman Young, or the gas crisis and the resulting American automotive industry crisis of the 1970s. Even as Detroit boomed from the industrial mobilization of WWII and the auto-driven economic expansion afterward, pervasive discrimination in the workplace and the housing market along strict racial lines thwarted sustained economic prosperity for the thousands of African Americans. Detroit and other major Northern cities went, as Sugrue describes, "from magnets of opportunity to reservations for the poor" (4) for reasons largely misunderstood or ignored, even by historians and social observers, who often seem to blame the victims or the federal aid programs of the Great Society and the War on Poverty. Instead, Sugrue also argues, it was New Deal policies and how they were applied by state and local politicians that helped ignite not black militancy, but a pervasive and radical " whiteness" that resisted equality for blacks in the workplace and the housing market as their God- and state-given right. Detroit, though examined as a case study applicable to other cities as well, is atypical in many ways that may have served to make bad situations worse. It was heavily reliant on the automotive and related industries, and lacked a significant presence of other racial minorities (13). Its ethnic communities, largely different European groups, quickly merged into a cohesive, blue-collar, home-owning "white American" culture by the 1920s, one bolstered frequently through independent union shops and churches that bucked larger social trends toward equality and civil rights. Even during the Depression years, Detroit's industrial economy chugged on, immortally captured in the epic murals of painter Diego Rivera. When WWII demanded a quick industrial mobilization, Detroit was ready physically; despite the association with the automobile, more than 40

Principles of Banking and Finance Essay Example for Free

Principles of Banking and Finance Essay What does Sub-Prime Crisis means? Sub Prime lending which is also known as near-prime, non-prime and second chance lending, means lending to people who might have trouble repaying the loan due to income ability or credit ratings which previously would not have been available to them. Credit ratings that might be not favorable to them with the standards set up initially by Financial Institutions slowly dwindle to less strict under-writing of loans. which could also due to an influx of foreign capital making lending easier to these group of people, the investment banks that sold the repackage mortgages to the consumers which is one of the way to fund for capital, and the Housing Urban Development of America policy to ensure that its citizens has access to mortgage loans easily. The cheaper interest rate packaged by the Financial Institutions which seems more affordable for the consumer for the first 1 to 5 years and the thereafter interest rate would have jumped significantly. The loans here generally referred to mortgage loans. The Crisis started or snowball into what it was in 2007 in my opinion was due to greed. Greed into thinking that the property boom would continue in perpetuity so that the borrowers could cash out more from their current property market valuation, with this cash out in terms of personal loan they could fund or finance their lifestyle be it buying a new property for investment purposes, to flip or for rental. For the luxury in life they choose to enjoy now, spending future money. As the economy slowed, jobs are being taken away from corporation in America to other countries which have a cheaper source of overhead expenses and manpower. People are being retrenched thus causing them to start defaulting on their loan repayments. A statistic done has shown that the American households do not have any savings but was laden with debt instead. The housing bubble burst, the market does not have that much capital as it used to have to continue to push property prices up anymore, thus causing the market to slow overall, foreclosures of their properties was happening. Consumers was also unable to obtain a refinancing which they had planned previously to lower their interest rate again when it went up, as financial institutions feel the pinched and controlled its lending. How did the Financial Institutions played a part in this? In the past banks have financed their mortgage lending activities through the deposits they receive from their customers. This has confined the amount of mortgage lending they could do. In recent years, banks have designed a new model where they repackage these mortgages to be sold to the bond markets. This has made it a lot easier to fund additional borrowing from the investors and interest rate was low. But it has also led to abuses as banks no longer have the incentive to check carefully the mortgages they issue to the lenders. The failure to check and curb lending in return for the possibility of profit was one of the causes. The first sign of the sub-prime crisis was as early as 2007 when HSBC Finance which is part of the banks north American subsidiary has to write off 880 million in sub-prime lending. The business has become unsustainable as borrowers started to default. The new model which we have come to know is known either as Mortgaged backed Assets or Collateral Debts Obl igations. The repackage mortgages are being sold to the bond markets, before they can be sold, credit rating agency will determine and give the model a rating. A credit rating for an issuer takes into consideration the issuers credit worthiness example its ability to pay back a loan, and affects the interest rate applied to the particular security being issued. These MBS or CDOs as it has come to know are usually marketed to countries which has a surplus in its balance sheet as it was generally known that Asians believe in savings rather than spending future money thus the products were usually marketed in Asia, It is allege that the rating agencies experienced from conflicts of interest, as they were paid by investment banks and other firms that organize and sell these structured securities to investors. If there are not to give favorable ratings to these products they risk the underwriter of these securities to another rating agency. It would be hard to sell these products if they are not being given a rating to begin with. Once they are sold the banks have in a way diverted part of the risk to the consumers. Investors should not rely too heavily on these ratings agencies opinions but instead carry out their own homework in the safeness of debt level as well as others related securities. Probably the opinions of the agencies enable them to get a conclusion, however based on past decade of event, it can only be consider as off base when it comes to the risk of credit event. Investors should try to put themselves in the shoes of the product pushers, asking themselves very important points like, why do you need to sell these products? Do you own any of these products yourself? If it is as good as you mention have the private investors bought and participated a substantial amount of their savings in it? Perhaps there need to be some form of intermediaries whereby no conflict of interest will affect their opinion and report of these products. A case study in Singapore itself which has made headlines during this crisis was the minibond saga which was being sold in Singapore by a couple of Financial Institutions. The originator of this series of structured products was the now defunct Lehman Brothers. The Minibond was being illustrated to the local consumer as a bond which is not the case it is actually a Collateral Debts Obligations. The relationship managers in banks are eager to sell the product because of the high commission and the consumer who are eager to buy because the returns are much higher than the fixed deposit being offered by the banks. An estimated of 500 million Singapore dollars was purchased for the Minibonds by consumers. It stirred a series of conflicts with the Financial Institutions that sold these products, the consumers cried fouled into being mis-sold of it, some of the consumers managed to get back part of their investment and vowed not to touch these structured products ever again. We can take a look back into the 1990s where one of the policy set up and enforce by the Housing and Urban Development of America, was one of the cause of the sub prime crisis. With the support of the government, HUD has less mortgage restriction requirements on its borrowers. The mandate was that Fannie Mae and Freddie Mac which was regulated by HUD, was to generate up to 8 million more homeowners in America. It was known as the â€Å"National Homeownership Strategy†. No down payment was required, 100% financing for the property was the norm. This was partly possible due to the influx of cheap money in the market, wit h this cheap money consumers speculated with the market, they kept buying new homes thus the good years of where the appreciation of the property keep going up. Financial Institutions dare to lend due to the market confidence that it can only keep going up, borrowers confidence that the market too can only keep going up. A check with HUD official website, apparently the US government is still supporting home ownership program without first addressing a stable income issue. Only with a stable income can a person make regular commitment to his or her housing loan commitment. Kudos to the Singapore government for taking appropriate actions during the last few years when their economy was recovering, the measures taken to prevent over speculation of the property market in Singapore. Homebuyers with the extra cash were snapping up properties, either for owner’s occupation or for investment purposes. The government either learned from the Sub-prime crisis or foreseen that if it continues the way it is going, a market crash might be imminent or the crash will be too fast and hard, no soft landing for the consumers. As they knew that property market have its up and down. Steps was taken, it used to be 90/10. Whereby the buyer have to come up with 10% cash and the remaining 90% can be financed through a financial institutions regardless of the number of property they currently owned. It was changed to 80/20 rule, 20% of which is the owner’s own cash an 80% through financing. Surprisingly it did not deter the consumers, the market still kept soaring. The next rule implemented was the 80/20 rule for first time buyers, meaning buyers without any current mortgage loan, for buyers with an existing mortgage which was not yet paid up; they are only eligible for 60/40. 60% financing for their new property and an increased in the stamp duty to be paid for to the government if it was their 3rd property for Singaporean. The hardest hit was the foreigners who are seeking to invest their money in Singapore properties as they have to pay additional 10% stamp duty which is likely to deter most of them. P rices still kept going up, the latest ruling was much more complex than the previous few. If one is looking at 80% financing one can only borrow up to the age of 65 years old and tenure of not more than 30 years. Which was not the case previously, in previous scenario it was dependent on different Banks guideline in Singapore, they could lend up to the age of 70, 75 or 80. They stepped in and put a cap at 65 as they believe that is the retirement age. If you want to extend your loan tenure your financing amount will drop to either 60% or 40%. I believe the government did this as they knew that the US is going ahead with Quantitative Easing 3, they want to prevent too much hot money from landing in Singapore shore. To sum up, we learned from our mistakes and grow not to make the same mistake twice. A healthy economy is based on real economic goods with value. Hopefully US can still continue to create innovative products like Apple and keep their manufacturing production in US soil, get employment rate up. The citizens have to maintain their expectations in terms of salary wise and spend within their means. Tighten up their way of lending and controlling Banks to a certain extent, a culture that is profit driven but with ethnics. Can heed the investment guru jim roger’s advice to focus on farming as there will be a food shortage in time to come. Induce good saving habits in everyone to save up for a rainy day. http://www.ethicalquote.com/docs/SubprimeMortgageCrisis.pdf http://news.bbc.co.uk/2/hi/business/7073131.stm http://en.wikipedia.org/wiki/Government_policies_and_the_subprime_mortgage_crisis http://www.washingtonpost.com/wp-dyn/content/article/2008/06/09/AR2008060902626.html http://www.thetruthaboutmortgage.com/mortgages-with-no-money-down/ http://www.telegraph.co.uk/finance/markets/2816291/HSBC-hit-by-sub-prime-crisis.html

Asean Solution for Rohingya Plight Essay Example for Free

Asean Solution for Rohingya Plight Essay WHY is Asean silent on the issue of Rohingya Muslims, who have been suffering for centuries under the Myanmar army junta? Recently, they were told to leave the country to a third world nation willing to receive them. This is cruelty towards their own people and it seems that the world is just watching the injustice done to them, without any assistance, support or solution. In June 2012, over 2000 people were displaced in sectarian violence in Myanmar, in which most victims were Muslims. The government promised a full investigation. Representatives from different religions and minorities condemned the atrocities inflicted on the Rohingya Muslims which is a serious human rights violation that the United Nations should stop immediately. In June 2012, 11 innocent Muslims were killed by the Burmese Army and the Buddhist mobs after bringing them down from a bus. A vehement protest was carried out in the Muslim majority province of Arakan, but the protesters fell victims to the tyranny of the mob and the army. People were reported killed and millions of homes destroyed in fires as Rohingyas and Buddhist-ethnic Arakanese clashed in western Myanmar. Myanmar has a Buddhist majority. The Muslim minority in Myanmar are mostly the Rohingyas and the descendants of Muslim immigrants from India (including what is now Bangladesh) and China, as well as descendants of earlier Arab and Persian settlers. Indian Muslim were brought to Burma by the British to aid them in clerical work and business. After independence, many Muslims remained in the country. Over the years, thousands of Rohingyas have fled to Thailand. According to reports, there are roughly 111,000 refugees housed in 9 camps along the Thai-Myanmar border. In February 2009, there was evidence of refugees being towed to sea and abandoned and other reports of brutality by the Thai military in which Thailands then prime minister Abhisit Vejjajiva said that there were some instances in which Rohingyas were pushed out to sea. Will the present generation see the light of day? They are part of mankind, why then are we responding apathetically towards them? Asean must respond immediately to solve their issues. Please be sympathetic and empathetic towards the Rohingyas.

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

Basseri and The Nuer :: essays papers

Basseri and The Nuer There are many cultures throughout the world, which may be far apart and yet still have similarities. Two of those such cultures, the Basseri, that live in Iran, and the Nuer, whom live in Sudan, have their differences, but also have some similarities. Many of the differences and similarities come from their subsistence strategies and the social and political organization of their societies. With the regions of the world, both the Basseri and the Nuer live in, they’ve had to adapt to the environment they live in along with the limitations imposed by that environment. Among the differences and similarities of the Basseri and the Nuer, their subsistence strategies are the most diverse in differences and similarities. Both the Basseri and the Nuer rely on their domesticated animals as a source of subsistence. A difference between the Basseri and the Nuer is that the Basseri have goats and sheep to provide the bulk of their subsistence products, while the Nuer use cattle as a source of subsistence. Another subsistence strategy of the Basseri is foraging, which is suited well for their nomadic way of life, by hunting large game and finding plants and mushrooms in the springtime. The Nuer, on the other hand, have a mixed subsistence strategy between pastoralism and horticulture. The Nuer cannot rely solely on either one, so other than the cattle they also cultivate millet, their main crop, and a small amount of maize and beans. The social and political organization of the Basseri and the Nuer are very much different. The Basseri’s social organization is based upon that of nuclear families; they are also neolocal, meaning that upon marriage a couple starts their own nuclear family in a new tent. After marriage, in order for the couple to begin a new household, the husband usually receives part of his father’s herd and at times, if not given any animals, the husband can work and receive animals as a payment. During the spring, the nomadic tribes can be supported in large numbers in a single camp; while during the winter, camps are setup in smaller groups. The Basseri reckon descent patrilineally where inheritance is usually from father to son. A woman bestows membership rights to her own tribe or her offspring. The Basseri consider themselves one unified tribe because they are all subsumed under the authority of a single leader, the chief of all the Basseri.

Meg Bogins The Women Troubadours Essay -- Meg Bogin Women Troubadours

Meg Bogin's The Women Troubadours What is Bieiris de Romans’ speaker seeking from the woman, Maria, about whom Bieiris writes? More generally, what are female troubadours as a whole seeking from their loves, and their craft? Meg Bogin, in her The Women Troubadours, asserts that â€Å"their poems were addressed to women†¦ to whom they vowed eternal homage and obedience. In exchange for their prostration, the troubadours expected to be ennobled, enriched, or simply made ‘better’† (Bogin, 9). Is the poetry of female troubadours less about the women being addressed and more about the troubadours themselves? By performing a close textual analysis of Bieiris de Romans’ poem to Maria, I hope to elucidate some possible answers to these questions. The poem opens with Bieiris’ speaker addressing her subject as â€Å"Lady Maria.† Rather than merely employing the woman’s first name, or utilizing a possessive phrase such as â€Å"my love† or â€Å"my Maria,† the speaker addresses her as â€Å"lady.† This implies a certain bestowal of respect upon her subject, and is potentially also a means to convey an understanding on the speaker’s part that this Maria has not yet consented to be hers. Next, the speaker proceeds to enumerate copious qualities that she finds pleasing in Maria. She begins by praising Maria’s â€Å"merit and distinction.† By â€Å"distinction† we can safely assume that the speaker refers to a pleasing reputation that Maria has cultivated within society, and possibly also the speaker’s own opinion that Maria is able to be distinguished as superior to other women. The term â€Å"merit,† however, is relatively ambiguous. By â€Å"merit,† the speaker could be indicating one or many qualities, including, but not limited to, virtue, achievement, a... ...g female companion, who will comply with her wishes and desires. Thus, Maria, judging from the qualities attributed to her in the poem, seems a perfect target for Bieiris’ speaker’s affections. Bieiris also appears to have created a speaker who is more concerned with being given the ability to express her desires than with the woman about whom those desires are expressed. Maria seems to be utilized as somewhat of a passive vessel about whom Bieiris can write and express herself in a literary fashion. The desire that Bieiris succeeds in expressing, then, is less one for Maria in particular and more one for composing lyric poetry in general. As a troubadour, Bieiris most likely avidly seeks patronage. Thus Maria is less of a goal to achieve and more a means to a different end: composing poetry for the sake of procuring a reputation, and obtaining financial gain.

Abortion :: essays research papers

Perhaps no contemporary issue inspires more heated debate than abortion - the deliberate termination of a pregnancy. Many have witnessed and experienced the bitterness of such a controversial debate. There are two types of people in this world. Pro - choice activists believe in the right to choose a safe, legal abortion. Pro - life activists believe that abortion is murder. Why is the debate over abortion so emotional? Some argue about the rights of the fetus (unborn child) while others argue if abortion should be legal. The debate usually strays from these basic issues. For example, pro - choice advocates try to convince their opponents women's rights are at risk. Pro - life supporters argue that the rights of the fetus are as important as those of the mother, and that abortion is murder. "Other pro - choice defenders argue that if abortion is murder, why do so many pro - life advocates fight against the most logical methods of preventing this so - called genocide - birth control an d sex education?" (Reardon, 138) Another reason for the bitterness of this debate is that most pro - choice and pro - life advocates reached their conclusions about abortion very early in life, probably even earlier than they can remember. They were taught from previous generations that there was only one correct point of view. Many people have trouble seeing why others who were brought up with the opposite viewpoint cannot simply look at the "facts" and be persuaded to change their minds. Tempers flare when opponents resort to oversimplification because the issue is definitely not simple. According to recent public opinion polls, the majority of Americans (at least 60 percent) hold beliefs that place them somewhere between the two most extreme, or radical, positions on the abortion issue. Although radical groups on both sides of the issue may get the most media attention, most Americans have moderate viewpoints. Individuals in this moderate viewpoint may lean toward pro - life or pro - choice, but they seek to stay at middle ground. Most people feel uncomfortable with abortion and are troubled by many of the reasons given for having one but these same people are also uncomfortable with the governments's interference in a woman's right to choose an abortion, a right granted in 1973 by the landmark Supreme Court decision in Roe vs. Wade. Half the adults surveyed by the New York Times in 1996 supported the availability of safe, legal abortions while only 9 percent felt that no abortions at all should be permitted.