Intuitive and quick math is how man came up with the metric system. Even though the metric system is more convenient for international travel, it has a significant problem when dealing with fractional measurements. American opposition to the metric system stems solely from the fact that fractions cannot be measured. Surprisingly, it aids in obtaining precise numbers by minimizing the room for error. The International System of Units (SI) is a component of most modern metric systems since it allows for a more straightforward measurement of quantities. To maintain sustainability, the United States should work toward adopting the existing metric system. Since the beginning of the Industrial Revolution, when modern machinery was first developed, the United States has used the SI (Hicks et al., 2019). Research indicates that the country might benefit from switching to the metric system. Because of its superiority, widespread acceptance, and precise solutions, the United States should switch to the metric system.
Because of its superior results, the metric system has gained widespread acceptance worldwide. Research using the metric learning technique has determined a highly successful method of learning that bridges the gap between Euclidean and hyperbolic geometry (Vinh Tran et al., 2019). The metric system is a universally adopted method of measurement for weights and measures that allows for precise calculations by anybody, anywhere. Each metric unit indicates its value and fits in with all the others.
The United States is making strides toward metrication because it will improve its industrial and commercial output, math and science education quality, and the ability to compete successfully in global markets. Because international trade is essential to the United States economy, the country’s manufacturing sector must embrace the international standard for measurement (Hughes & Oberdorff, 2020). Back-of-the-envelope computations are greatly simplified by using the metric system. Whether working with a lab apparatus to create a solution, figuring out the square footage of a parking lot, or determining the correct dosage for a patient of a specific weight, calculations are required. Also, it shortens the time it takes to verify a computation that a computer has just vomited out. Because of these advantages, numerous sectors in the United States have chosen to adopt the metric system independently, including the scientific community, the medical community, the energy sector, and the automobile industry.
A good example is Ford Motor Company, which produced the first US-made production engine to fully employ metric technical standards and achieve interoperability with other parts. Because of this, the metric system was implemented to standardize and reduce the complexity of product requirements. In addition, line workers with intermediate skills only need the training to transition to metric working.
As a result, the metric system facilitates uniformity in communication between students and teachers across countries. Any materials used to create textbooks can be created using the metric system. Students in fields of study such as engineering, chemistry, medicine, and computer science can all benefit from a unified measurement system. Drive capacities, memory sizes, and processing speeds in computers are all specified in SI units. Students from the United States studying in countries that use the metric system will be exemplary with grocery shopping, estimating garment sizes, or navigating public transportation (Carvalho et al., 2019). The Celsius temperature measurement system has a more straightforward 0 to 100 scale than the Fahrenheit system used in the United States. Contrarily, the metric system was developed with the goals of simplicity and ease of use from the start. Calculations in the metric system are more superficial and quicker than those in the alternative system because of the decimal character of the metric system. Converting between different weights and measurements is simple. Because fractional notation is not used, mental computation is relatively straightforward. It has also made it easier to be precise, so it is effective now. It is as simple as multiplying or dividing by ten.
Metric also makes it easier to convert between different unit systems. On the other hand, customers stand to gain from the changes. This is because widespread use of the metric system in business should lead to more straightforward and uniform product packaging. It is more convenient to compare prices and save money on packaging and shipping if there is a smaller range of package sizes. They are ultimately benefiting consumers with cost savings. Metric measurements simplify calculations and make it simpler to make price and weight comparisons. Adopting the metric system has improved productivity in business and industry and simplified the teaching and learning measurement process. The same is true of American exports: they do very well in other countries (Brown, 2020). So, the economy and the people of the United States stand to benefit from the metric conversion.
To facilitate communication and cooperation with its allies, the United States military relies heavily on metric units of measurement, especially those outlined in NATO Standardization Agreements (STANAG). Kliff’s, a slang term for kilometres, has been used by ground armies to measure distances. Starting with the M-14, most military-issued guns are specified in metric (Goodhart & Taylor, 2020). Vehicles used by the military are often constructed using the metric system. Coverage of major athletic events, such as the Olympic Games and the FIFA World Cup, provides a frequent opportunity for American sports fans to be exposed to metric units. As a preparation for the Olympic Games, the track and field Olympic Trials have always been run in metric distances; the National Championships followed suit in 1974.
Those who refuse to adopt the metric system or use non-metric units are at a competitive disadvantage in the international marketplace. Compared to other measurement systems, the metric system is the most straightforward. The metric system’s clarity prevents misunderstandings due to its inherent simplicity. Since it is the standard system used worldwide, there is no debate about it or its interpretation. There is a general feeling of likeness and consistency.
Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine learning Interpretability: A survey on methods and metrics. Electronics, 8(8), 832. https://doi.org/10.3390/electronics8080832
Goodhart, A., & Taylor, J. K. (2020). LGBT military service policies in the United States. Oxford Research Encyclopedia of Politics. https://doi.org/10.1093/acrefore/9780190228637.013.1289
Hicks, M. B., Farrell, W., Aurigemma, C., Lehmann, L., Weisel, L., Nadeau, K., & Ferguson, P.(2019).Making a move towards modernized greener separations: introducing the analytical method greenness score (AMGS) calculator. Green chemistry, 21(7), 1816-1826. https://doi.org/10.1039/C8GC03875A
Hughes, R. M., & Oberdorff, T. (2020). Applications of IBI concepts and metrics to waters outside the United States and Canada., 79-93. https://doi.org/10.1201/9781003068013-6
Vinh Tran, L., Tay, Y., Zhang, S., Cong, G., & Li, X. (2020, January). Hyperml: A boosting metric learning approach in hyperbolic space for recommender systems. In Proceedings of the 13th international conference on web search and data mining (pp. 609–617). https://doi.org/10.1145/3336191.3371850
Applications Of Artificial Intelligence In Social Networks Free Writing Sample
The way we use social media is changing due to artificial intelligence (AI). AI has become a key technology for analyzing and utilizing this enormous amount of data due to the growing amount of data and users on social networks (Grover, Kar & Dwivedi, 2022). AI has the potential to transform how we view social media by giving users access to more personalized and pertinent content, enhancing user experience, and assisting businesses in learning about user behaviour.
Applications of Artificial Intelligence
Personalized content recommendations are one of the most widely used AI applications in social networks. Machine learning algorithms apply in social media sites like Twitter, Facebook, and Instagram to examine user behaviour and interests and offer tailored content recommendations. AI algorithms analyze user data such as click behaviour, search history, and interaction with the content to understand users’ preferences and interests (Grover, Kar & Dwivedi, 2022). AI uses this analysis to suggest posts, videos, images, and pertinent advertisements to users.
Facebook uses AI in its News Feed algorithm to rank posts according to their relevance to the user. The algorithm examines several variables, including the user’s engagement with the content, the posting time, the type of content, and the content’s source, to determine the ranking of posts. The Explore page on Instagram uses AI to suggest posts based on how users interact with similar content (Grover, Kar & Dwivedi, 2022). The algorithm studies a user’s likes, comments, and saved posts to determine their interests and preferences before recommending content that is similar to those interests.
Chatbots are another way that AI applies in social networks. AI-powered software called a chatbot can mimic user conversations through messaging apps. Chatbots can get employed for several tasks, including customer service, sales, and marketing (Torous et al., 2021). Immediate answers to user questions, problem-solving, and platform navigation are all possible with chatbots.
For its recruitment platform, LinkedIn, for instance, uses chatbots. The chatbot can converse with job seekers, learn about their preferences and skills, and suggest appropriate job openings (Torous et al., 2021). Additionally, the chatbot can help job seekers apply for jobs, offer feedback on their profiles, and tell them how to improve them.
AI is also useful for social network sentiment analysis. The process of locating and classifying the opinions expressed in social media posts is known as sentiment analysis. AI algorithms can analyze the language and context of social media posts to determine whether they are positive, negative, or neutral (Torous et al., 2021). Businesses can use sentiment analysis to understand the thoughts and feelings of their customers, spot trends, and make data-driven decisions.
As an illustration, Twitter uses sentiment analysis to track user opinions of various goods and services. Businesses can access tweets that mention their goods and services using Twitter’s API and then use sentiment analysis to determine whether they are favourable, unfavourable, or neutral (Torous et al., 2021). Companies can use this information to understand customer complaints better, resolve problems, and develop goods and services.
Recognition of images and videos in social networks is another use of AI. AI algorithms can examine the content of pictures and videos to recognize objects, faces, and scenes. Search, advertising, and content moderation are just a few image and video recognition uses. As an illustration, YouTube makes pertinent user recommendations after analyzing video content. The algorithm can examine the video and audio content to determine whether a video is relevant to the user’s interests (Torous et al., 2021). Similarly, Instagram uses image recognition to determine the scope of images and offer users pertinent suggestions. The algorithm can analyze the visual content of those images to examine whether images contain objects, people, or scenes relevant to the user’s interests.
Analytics on social media can also get done with AI. The process of gathering and studying data from social media platforms to gain knowledge of preferences, user behaviour, and trends is known as social media analytics (Torous et al., 2021). AI algorithms can examine social media data to find trends, forecast patterns, and offer business recommendations.
One social media management platform that uses AI for social media analytics is Hootsuite. Hootsuite’s AI algorithms can analyze social media data, including sentiment analysis, engagement metrics, and audience demographics, to provide insights into user behaviour and preferences (Torous et al., 2021). Businesses can use this information to target their audiences, measure the success of their campaigns, and optimize their social media strategies.
Finally, advertising on social media can also get done using AI. Businesses can effectively reach their target audience and market their goods and services by using social media advertising. Ad delivery, Ad targeting, and ad performance can all get improved with AI. Facebook, for instance, uses AI to target ads. By examining user data like interests, behaviours, and demographics, Facebook’s AI algorithms can target advertisements to users most likely to be interested in the good or service (Torous et al., 2021). Facebook’s AI can also analyze ad performance metrics like click-through and conversion rates to improve ad performance and optimize ad delivery.
Although AI has many potential advantages for social media, some difficulties and worries exist. The possibility of algorithmic bias is one of the biggest obstacles. When AI algorithms discriminate against specific racial, gender, age, or other groups of people, this is known as algorithmic bias. Unfair and discriminatory outcomes may result from this. For instance, Facebook’s ad targeting algorithm has come under fire for enabling advertisers to block people of a particular race or gender from seeing their ads (Hung et al., 2020). Anti-discrimination laws make this kind of discrimination illegal, and as a result, Facebook has run into legal issues.
The effect of AI on privacy is another issue. For AI algorithms to be practical, access to much user data is necessary. Users’ search history, location, and social connections are sensitive and private data examples. AI algorithms risk improperly using this data, whether intentionally or accidentally, and violating users’ privacy. For instance, Google’s AI algorithms have come under fire for using private information (Hung et al., 2020). Google’s algorithms gather large amounts of user data to deliver tailored search results and advertisements. However, the ability to track users’ actions and whereabouts using this data have sparked worries about privacy and snooping.
The potential effect that artificial intelligence has on employment presents another difficulty. Many tasks currently done by humans, like content moderation, customer service, and data analysis, could be automated with AI. While this results in higher productivity and efficiency, it also results in job losses and a widening income gap. For instance, Facebook has come under fire for contracting out content moderation to unreliable vendors who frequently receive meagre pay while working under demanding circumstances (Hung et al., 2020). AI could potentially replace these workers, but how this would affect employment and working conditions is unclear.
AI has many applications in social networks, including chatbots, sentiment analysis, analytics, image and video recognition, and advertising. These programs can assist companies in learning more about user behaviour and preferences, enhancing user experience, and more successfully connecting with their target market. However, AI presents some difficulties and issues, including privacy, algorithmic bias, and employment (Hung et al., 2020). As AI develops, it is crucial to ensure that individuals can use it ethically and responsibly and benefit users and society.
Grover, P., Kar, A. K., & Dwivedi, Y. K. (2022). Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions. Annals of Operations Research, 308(1-2), 177-213.
Torous, J., Bucci, S., Bell, I. H., Kessing, L. V., Faurholt‐Jepsen, M., Whelan, P., … & Firth, J. (2021). The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry, 20(3), 318-335.
Hung, M., Lauren, E., Hon, E. S., Birmingham, W. C., Xu, J., Su, S., … & Lipsky, M. S. (2020). Social network analysis of COVID-19 sentiments: Application of artificial intelligence. Journal of medical Internet research, 22(8), e22590.
Capitalism In Urban Areas University Essay Example
The economic system known as capitalism emphasizes private ownership and the free market and is used as a determinant in resource allocation, and dictates prices grounded on supply and demand. It has significantly impacted urban communities and has been the dominant economic system in the United States for centuries. Economic inequality is one significant effect of capitalism on urban communities. This essay explores the social impacts of capitalism concerning urban communities in the United States by examining how capitalism can cause a change in the United States based on sociological factors that affect urban communities.
People who own property and have money to invest are rewarded in capitalism, leaving those who do not often behind. In urban areas, poverty, a lack of affordable housing, and unemployment are all manifestations of this economic inequality. In the past, wealthy capitalists have invested in suburban areas, which has led to urban decay and a lack of investment in urban communities. Due to this economic inequality, the wealthy have access to better healthcare, education, and other services, while the poor struggle to meet their basic needs. The free enterprise additionally affects the arrangement of public administrations in metropolitan networks. In capitalist societies, the government or private businesses frequently provide public services like education, healthcare, and public transportation. According to Walker (2018), these services are typically provided by private businesses in the United States, which indicates that they are subject to the market’s demands and are meant to bring in money. In urban areas, where there may be less demand for these services or where residents may need help to afford them, this can lead to an inadequate supply of public services (McCreary & Milligan, 2021). Finally, in urban communities, capitalism can significantly impact the environment. Environmental injustices, environmental degradation, and pollution are frequently the outcomes of capitalism’s emphasis on profit and economic expansion over environmental concerns (Miller & Liu, 2021). For instance, businesses may decide to locate factories and other polluting industries in urban areas, where residents are less likely to resist and land is less expensive; this can bring about expanded contamination and ecological debasement in metropolitan networks, affecting occupants.
Capitalism has had several effects on the sociological factors that affect urban communities. Gentrification is one way that urban communities have been impacted by capitalism (McCreary & Milligan, 2021). Improvement alludes to the method involved with revamping or overhauling a region, frequently described by the deluge of center and high society inhabitants, while dislodging the first lower-pay occupants. Gentrification is when a neighborhood’s property values rise due to increased demand, usually from constructing new commercial or residential buildings. The original residents are forced to leave their homes as developers gravitate toward locations where they can make the most money. As a result of this process, lower-income residents of urban communities have been forced to relocate to other areas with fewer resources and opportunities. Another way free enterprise has affected metropolitan networks is by improving lodging strategies that advance homeownership. Homeownership is frequently promoted to acquire wealth and stability (Dantzler, 2021). However, the obstacles to homeownership are significant for many families and individuals with lower incomes. These obstacles include a lack of financial education, bad credit, and affordable housing. Consequently, homeownership policies have primarily benefited middle- and upper-class individuals, increasing wealth and income inequality.
The growth of the gig economy has also affected urban communities. Short-term contracts or freelance work are characteristic of the gig economy, which is frequently facilitated by online platforms. While the gig economy has opened doors for certain people to acquire additional pay, it has likewise prompted the precarization of work, with fewer advantages and assurances for laborers. Dantzler (2021) reiterates that this affects metropolitan networks, where an absence of stable work has added to neediness and financial uncertainty. Lastly, the rise of consumer culture is evidence of capitalism’s influence on urban communities. The development of worldwide companies has prompted the normalization of items and administrations, bringing about the homogenization of culture. As a result, some of the distinctive cultural identities of urban communities have been replaced by a consumer-driven culture that encourages materialism and instant gratification.
The private enterprise also adversely affects social points of view since it restrains infrastructure power. If you set forth some parcel of energy, your business can pay off for you. Nonetheless, private enterprises can also achieve an imbalance that encourages shamefulness. For example, an organization could acquire restraining infrastructure power (Enright et al., 2018). From that point onward, it might charge misleadingly exorbitant costs to clients to prevent new clients from joining. Organizations with monopsony power can endure paying laborers compensation fundamentally lower than the specialist’s efficiency (Walker, 2018). Laborers are constrained to work without pay. Appropriately, industrialists who approach private property can “exploit” their impressive plan of action capacity to make generally more unmistakable advantages than the rest of society.
Additionally, it achieves heritage, allowing the confidential property to be gone down through ages; thus, individuals who acquire capital can bring in a ton of cash without buckling down. They can secure the best private schooling and occupations. Therefore, potential open doors and results should be more evenly appropriated. These differences truly intend that there is certainly not a level battleground in that frame of mind; there isn’t correspondence of possibility, and a couple of populace value preposterous advantages. It is challenging to contend that free enterprise will undeniably cause imbalance (Walker, 2018). The private enterprise depends on the rule that pays and wages ought to be disseminated by the unregulated economy. Wage correspondence must be guaranteed by government intercession. A few defenders of an “entrepreneur framework” may contend that the public authority should, in any case, address a portion of the imbalance that exists in industrialist social orders (Enright et al., 2018). Direct syndication power and giving free schooling, for example, to ensure that everybody has equivalent admittance to open doors and instruction, which may be burdening legacy riches.In conclusion, based on sociological factors that have an impact on urban communities, capitalism has the potential to alter the United States significantly. These changes include increased social stratification, inadequate public service provision, declining community and cultural heritage, and adverse environmental effects. To resolve these issues, policymakers should consider rearranging abundance and assets, giving reasonable lodging and public administrations, advancing local area and social legacy, and advancing supportable improvement in metropolitan regions.
Dantzler, P. A. (2021). The Urban Process under Racial Capitalism: Race, Anti-Blackness, And Capital Accumulation. Journal of Race,Ethnicity and The City, 2(2), 113-134. Https://Doi.Org/10.1080/26884674.2021.1934201
Enright, T., Björkman, L., Mcguirk, P., Peck, J., Purcell, M., Scott, A. J., & Rossi, U. (2018). Cities In Global Capitalism. The AAG Review of Books, 6(1), 59-75. Https://Doi.Org/10.1080/2325548X.2018.1402288
Mccreary, T., & Milligan, R. (2021). The Limits of Liberal Recognition: Racial Capitalism, Settler Colonialism, and Environmental Governance In Vancouver And Atlanta. Antipode, 53(3), 724-744. Https://Doi.Org/10.1111/Anti.12465
Miller, R., & Liu, K. (2021). After The Virus: Disaster Capitalism, Digital Inequity, and Transformative Education For The Future Of Schooling. Education and Urban Society, 00131245211065419. Https://Doi.Org/10.1177/00131245211065419
Walker, R. A. (2018). A Theory of Suburbanization: Capitalism and The Construction Of Urban Space In The United States. In Urbanization and Urban Planning In Capitalist Society (Pp. 383-429). Routledge.