Introduction
The former United States president Gorge W. Bush proposed the No Child Left behind Act of 2001 ‘NCLB’ and signed it into law on January 8, 2002 (Lewis, 2010). The Act came as good news to the education fraternity as its provisions were very friendly. NCLB required that all public schools be given financial support by the government. Receiving federal financial support was crucial for the public schools to continue service to the American population as the author asserts (Lewis, 2010). The funding aimed to try to administer a standardized test every year to students in public schools (Chaddock, 2010). This brought equality in the public schools for all students would be tested under similar conditions countrywide a fact that brought sanity in the education sector.
Main body
The act also requires the state to provide highly qualified teaching staff to schools to achieve high grades. It is within the students’ rights to have qualified teachers, although the quality standards of teachers differ from one state to the other. Each state has its mechanism for determining the qualification standard. The act had some impacts on the students, the teachers, and the school districts as well (Lewis, 2010). Primarily, this act improved the level of accountability, which is a requirement of schools to meet certain standards. Teachers also are required to be able to offer certain professional teaching standards, which also enhances accountability. This is further emphasized by the Acts requirement for schools to pass the yearly test that shows the level of improvement made by students in every financial year (Lewis, 2010).
According to the act, schools must show improvement in every financial year to justify their need for funding. The standardized test throughout the country, therefore, acts as a standard playground for schools to show their commitment to better services to students. According to the article, the author feels that the introduction of this act has spurred competition in the education system (Lewis, 2010). Other than that, schools are more careful to meet the set threshold to avoid being underfunded due to lack of performance. The government through this act can now control the quality of the education system hence enhancing better performance countrywide. The act outlines other punishments that have contributed to the increased accountability according to the article (Lewis, 2010).
The Act has served an essential role in serving the schools and the students to recognize the significance of the education system and its effects on the country (Chrismer, Hodge, & Sainti, 2006). As Chrismer, Hodge, & Saint asserts, some of the critics of this law dispute that the penalties in this Act only affect the schools and do not add to the enhancement of their performance (2006). However, the supporters of the act think differently. Setting some guiding rules on the educational systems is paramount to the improvement of the quality of offered services. The supporters claim that the act has helped to link content standards with student outcomes (Chaddock, 2010). The standardized test measures the student’s improvement annually from the 3rd grade to the 8th grade (Chaddock, 2010). The act also requires the schools to provide the parents with information on the progress of their children’s academic progress by providing a detailed report card at the end of every academic year.
The act raised the level of education standards by requiring new teachers to have a bachelor’s degree. A fully certified teacher has to be a holder of a university degree to ensure equitable distribution of qualified teachers (Chaddock, 2010). Nonetheless, standardized testing has come under intense criticism from different parties. Some feel that it is a strain on the government expenditure while others see the move as unfair competition for states. Critics argue that standardized tests are unfair because they set students under the same conditions, which is critically impossible. All states have different challenges faced by students hence giving them a standardized test to measure their performance progress is not a prudent move. According to critics, some students will have some advantages over others depending on their locations and vice versa.
However, according to the article, the local government had failed in providing quality education to students hence the intervention of the federal government. The education system was characterized by gross malpractices that led to poor performances in public schools hence necessitating federal intervention. Such malpractices included instances where teachers would teach outside their areas of expertise (Chrismer, Hodge, & Sainti, 2006).
Conclusion
Nonetheless, the act undoubtedly has increased the quality of education in the United States of America. Scientific research is a requirement that has increased the level and quality of education in schools (Lewis, 2010) this has also increased the level of early literacy in the United States, which is a good start. The act has brought along changes that were necessary for the education system and the changes are bearing positive results already. The government had to come in to rescue the almost failing system under the local authorities. This article has clearly outlined the benefits of this act and the criticism surrounding its implementation.
References
Chaddock, G. (2010). Obama’s No Child Left Behind revise: a little more flexibility. Web.
Chrismer, S., Hodge, S., & Sainti, D. (2006). Introduction to Assessing NCLB. Web.
Lewis, T. (2010). Obama Administration to Push for NCLB Reauthorization This Year. Web.
AI And Machine Self-Learning
Introduction
Machine self-learning is a subfield of computer science related to the ability of computers to recognize patterns using AI without being programmed to do it. Its major goal is to identify how a computer can make data-driven predictions without reprogramming.
While machine self-learning was out of the reach of the majority of businesses in the past, cloud computing has made it much more affordable. Since it allows extracting information out of raw data, it has become a perfect solution for complex business problems that cannot be solved by software engineering or human judgment.
Areas of Application
Machine learning is now applied in order to create defensible competitive advantages in the following areas:
- Cognitive computing. This refers to the ways of doing business online, using pattern recognition, data mining, and natural language processing. The major purpose is to integrate intelligent computing seamlessly with the help of AI engines that make it possible to use face and emotion detection, visual recognition, and video analytics.
- Personal assistants and chatbots. These technologies are meant to simulate interactions with real users. They learn from past patterns of conversation to offer a life-like interactive experience.
- Internet of things. Data-driven cloud platforms made it possible to construct a seamless virtual environment using IoT, which is made intelligent with the help of machine learning.
- Business intelligence. When cloud technology and machine learning were too expensive for the majority of businesses, organizations had to collect data connected with the habits of their clients manually and locally. Machine learning made it possible to find underlying patterns and eliminate the necessity of manual input.
- Security and data hosting. Cybersecurity has become smarter with the introduction of machine learning since its complex algorithms easily detect anomalous patterns, allowing users to pinpoint intruding malware and prevent attacks before they damage the system. Data hosting is also affected by machine learning since it supports faster data flow.
Machine Learning and Cloud Computing
Cloud computing makes it possible to use machine learning pre-trained patterns in order to generate personalized tailored models. These services are scalable, fast, and easy to apply. Businesses use it to build large-scale, highly sophisticated regression models that are too demanding to rely on hardware. In the past, they used systems that required a huge amount of processing power in order to be effective. Nowadays, the same functions are easily performed by the cloud.
Over a certain period of time, machine learning techniques will become intelligent enough to be able to make decisions without any human presence whatsoever. Both users of the cloud and cloud providers generate new algorithms. The cloud has all the essential components for this. It features abundant storage, computational power, and a massive amount of data, which allows it to use scalable machine learning platforms.
Ways of Improvement
Although the concept of machine learning is rather old (it was first defined already in 1959), its major algorithm remains basically the same: The system analyzes the human feedback in order to calculate, which answer is the most probable. Due to the increasing popularity of machines coupled with cloud computing in business, the concept requires further development to provide more diversified and efficient algorithms.
Significant improvement can be achieved with changes introduced to problem definition and data. Data tactics include:
- Getting more data. If it is possible to obtain more data, machine self-learning techniques will demonstrate better performance.
- Inventing more data. If is it impossible to get more data, it can be generated using a probabilistic model.
- Cleaning data. Sometimes machine learning is hindered owing to missing or corrupt observations. They must be removed or fixed if possible in order to improve the quality of the input.
- Resampling data. Using a smaller sample will help speed up the process and improve representation.
- Reframing the problem. Changing the types of prediction may bring about better results.
- Rescaling data. A lift of performance can also be achieved by normalization or standardization of input variables.
- Transforming data. In order to better expose features in the data, it is necessary to reshape its distribution.
- Projecting data. Lower dimensional space allows creating a compressed representation of the dataset.
- Selecting features. Feature importance methods make it possible to identify whether all input variables are equally significant.
- Engineering features. New data features can be created to signify important events.
Another method of leveraging cloud computing with the help of data sets is to improve the algorithms applied. First and foremost, it is essential to identify those data representations and algorithms that demonstrate performance above average. After that, it will be much easier to develop the most efficient combinations. The following tactics can be used to achieve this:
- Resampling method. The user should identify what resampling strategy is applied and find out the configuration that allows the best application of available data.
- Evaluation metric. Machine self-learning uses different metrics to evaluate the skill of predictions. Thus, it is necessary to select the one that best captures the peculiarities of the problem that has to be solved.
- Baseline performance. It is better to use a zero rule or a random algorithm for establishing a baseline, which is required for being able to rank all evaluated algorithms.
- Spot-checking linear algorithms. Since linear methods are frequently effective, fast to train, and easy to comprehend, they are preferable when one wants to achieve better results. A diverse suite of them should be evaluated to identify the best possible combination.
- Spot-checking nonlinear algorithms. These algorithms are much more complex and require more data to work properly. That is why it is necessary to perform a thorough analysis to be able to evaluate their performance.
- Borrowing methods from the literature on the topic. If the problem of machine learning is too difficult to solve, one must address the literature on the topic to get ideas concerning algorithm types or extensions of traditionally applied methods.
- Changing standard configurations. Before deciding to change an algorithm, the user must give it an opportunity to show its best performance. For this purpose, he/she should try to change the configuration.
Combined techniques can also be rather effective:
- Blending model predictions. Different algorithms can be applied simultaneously to make multiple models.
- Blending data representations. Predictions can be combined from models that are trained on different projections of one and the same problem.
- Blending data samples. The user can create subsamples of training data and create an effective algorithm.
- Correcting predictions. Prediction errors should be eliminated to achieve better performance.
Conclusion
With the development of online operations, machine learning has become a focus of attention of a number of businesses since it allows personalizing data and achieving higher customer satisfaction. Coupled with cloud computing, it makes it possible for organizations to process huge amounts of information and make valuable predictions. Yet, despite its growing popularity, machine learning still uses old algorithms based on user feedback. It is now necessary to apply tactics to improve and update them in order to leverage cloud computing datasets.
Themes Raised In Frankenstein By Mary Shelley
Frankenstein is a novel written by British author Mary Shelley in 1818. The novel describes the story of a young gifted scientist, Victor Frankenstein, who created a living creature as an unorthodox experiment. Frankensteins creation occurred to be hideous and, therefore, rejected by the scientist and humanity (Shelley 28). This alienation became a driving force for the creatures revenge through murders. In Frankenstein, Shelly addresses numerous themes such as prejudice, revenge, society and isolation, nature, and death, to name just a few. Still, the most essential theme discussed in the novel is one of ethics in science. The dilemma is that, on the one hand, science is, by definition, about thinking and acting beyond the existing borders as it is the only way to discover something new. However, on the other hand, the results of scientific developments are unpredictable. The current paper argues that Shellys appeal to the audience is that science is not almighty, and scholars should not try to perform such duties of God as, for example, giving a life for a creature. Besides, regardless of what the outcomes are, scientists should bear responsibility for their actions.
The ethical debate on science, described in Frankenstein, does not lose its actuality up till now. What is more, in the 21st century it has become even more topical due to numerous inventions and scientific developments. The most burning dilemma considers the issue of life and death, as it has already been mentioned. Such procedures as abortions, euthanasia, extracorporal fertilization, transplantation, bioengineering, and cloning raise debates on whether it is ethical or not to give or take away lives in an unnatural way. The example of Victor Frankenstein illustrates that people should not try to “play God” (Shafer par. 5). Nevertheless, many people are currently alive due to clinical intervention which would be impossible without new technologies. Thus, doctors who save peoples lives, and innovators who develop various technologies that affect peoples longevity, to some extent, act like God by postponing the death of individuals. Based on Frankensteins experience, it could be claimed that scientists should foremost think of the ethics of their actions.
Concerning the topic of Frankensteins ethical dilemmas, it should be mentioned that, in the novel, Shelley illustrates the power of science to blur the line between life and death. Frankenstein notes that “Life and death appeared to me ideal bounds, which I should first breakthrough” and creates a living being out of dead pieces (Shelley 26). Therefore, it could be concluded that the scientist eradicates the difference between life and death by returning to life dead scattered parts and uniting them as the monster (Peters 147). The protagonist of Shelleys novel managed to “renew life where death had devoted the body to corruption” (Shelley 26). This way, the fuzziness of distinctions between life and death is evident in the novel since Frankenstein refutes the fact that once something is dead, it could not revive again in the real, not mythological, circumstances.
The problem with the creator and his creation is exacerbated by the fact that the former appeared to be unable to provide the latter with a decent life. Frankenstein’s creature was horrendous and separated from society because of this reason. The scientist created a being that was doomed to suffer. This point returns us to the problem of the creators responsibility for what is being produced. Frankenstein wanted his experiment to be successful and the only goal he was pursuing is giving life to the artificially created creature. He did not take into consideration the fact that life is a more complex phenomenon than simply breathing and moving the body. The life of a sentient being is also about interaction with others and inclusion in the society of self-like creatures. Frankensteins monster was single and rejected even by the father. Thus, Victor Frankenstein illustrates that scientists should not be short-sighted. Instead, they should think about how the object that they created would behave in society, whether it would be harmful to others and for itself.
To sum up, it should be noted that Frankenstein could be viewed as a guide for scientists, researchers, and experimentalist that they should not forget to focus on the ethical side of their developments as well. Even though Frankenstein tried to conquer death, he finally failed to do so and proved that his capacity to control life is limited (Neel 421). Shelley illustrates that the attempt to erase the bound between life and death does not lead to any decent results. The further science develops, the more burning remains the debates about the permissibility of humans to decide the questions of life and death. Thus, it seems that Shelleys novel would never become outdated. At the same time, Shelleys view on ethics and morality in science and the link between the latter and religion is not the truth in the first instance. Her opinion could be disproved by those who suppose that there is no higher power than people and that people are free to do whatever they want for the sake of technological and scientific progress. Nevertheless, one should not forget that in any case a creator should be answerable for the consequences of the experiment and should never reject the creation.
Works Cited
Neel, Alexandra. “Still Life in Frankenstein.” Novel: A Forum on Fiction, vol. 48, no. 3, 2015, pp. 421–445.
Peters, Ted. “Playing God with Frankenstein.” Theology and Science, vol. 16, no. 2, 2018, pp. 145-150.
Shafer, Audrey. “Why Frankenstein Matters.” Stanford Medicine, 2018.
Shelley, Mary. Frankenstein. Broadview Press, 2012.