Writing Project

Depth of Automation

Artificial intelligence is part of data science and deals with developing intelligent and self-operating machines. It aims at making machines adopt the intelligence of human beings artificially or to simulate their intelligence into machines. It employs algorithms to set specific instructions that machines can follow and draw results.

Artificial intelligence technology creation is not a new concept, as it has been for more years since World War II when scientists thought of machines working as humans. This creation has developed over time, and various stages have been followed for the technology to reach advanced stages and for all avenues to be discovered. These stages follow themselves chronologically as follows, rule bases system whereby Artificial intelligence surrounds us with everything ranging from robot process automation used in business to autopilots used in aircraft. Content creation and awareness follow in which the machines based on algorithms are trained on the best human knowledge and experience with knowledge specific to certain domains. Domain-specific aptitude follows where the advanced systems can develop know-how in specific domains. The technology develops into reasoning systems whereby the machine develops a concept of intellect, intentions, knowledge, and understanding. Artificial general intelligence is then realized. This is one primary objective that many artificial intelligence developers aim to achieve. This is the stage that is represented in many sci-fiction movies where machines are leading humans in intelligence. The development then stretches to artificial super intelligence in which algorithms can defeat the smartest of all humans in every domain. This stage creates a framework for real complex problems such as hunger, poverty, and climate change to be resolved. This stage provides uniqueness as machines will even outsmart the humans who create them.

The creation path will finally lead us to Excellency. This is the point at which the capabilities of human beings will see the tremendous extension, according to Nishant et al. (2020). The technology will connect minds, and ideas and thoughts will mostly be shared by a simple flick of the mind, for instance, through deep fakes. According to Tom Simonite (2020), Deep fakes have become a hot training too. Today’s presentation has been enabled by AI-developed software. Humans can connect with other life forms, like plants, animals, and other natural activities, through artificial intelligence. Therefore artificial intelligence can be used in many different spectra of life, such as in machine translations, online shopping and advertising, the development of cars, web search, online virtual assistance, smart cities, homes, and infrastructure(Imaged, new scientist). Various issues surround artificial intelligence, ranging from legal to ethical issues, including privacy and surveillance, bias and discrimination, limited knowledge, trust deficit, and potentially the psychological challenge, which is the role of human judgment (Bernd Debusmann Jr.2021).

 Pros and Cons

Data scientists draw many advantages from the benefits of artificial intelligence, such as reducing human errors, taking risks on behalf of humans, throughout-the-day availability, easing up repetitive jobs, digital assistance, faster decision-making, and the development of new inventions. However, it comes with its disadvantages which may include high creation costs, laziness among humans creeps in, unemployment, withdrawal of emotions, and slowing down on new inventions. These new inventions have various effects on consumers, such as the enhancement of consumers purchasing intentions, proper analysis of products before purchase, and understanding of consumer preferences (Lalicic & Weismayer, 2021). Artificial intelligence provides automation of time-consuming tasks, customer onboarding, and personalized services (Pelau et al., 2021).

 Ethical Considerations

According to Brendel et al. (2021), various ethical considerations are critical in the data science development of artificial intelligence. These considerations include legislation, bias, responsibility, humanity, inequalities, and unemployment. The chain that surrounds labor is recently concerned with automation. With time, major inventions have taken place, threatening many people who rely on physical jobs, as in the pre-industrial era. Nevertheless, primary ethical considerations should have been put in place to safeguard the healthy fare of this population by ensuring proper transition to assume more challenging roles, which necessitates moving from the old manual jobs era. Most times, still today, they depend on selling their time in exchange for income to sustain themselves. Thus, therefore, data scientists should develop a technology that will be considerate to help people find meaning in non-lab intensive activities such as learning new ways to contribute to their societies, proper community engagements, and care for their families.

Inequality is a major injustice that exists among us. Data science involvement in artificial intelligence should properly consider ways to cut down on inequalities among humanity. The proper mechanism should be implemented to ensure equitable distribution of the wealth developed by machines. Many economies lean on the compensation of how each one contributes to the same; therefore, it is in a such breath that equitable distribution becomes the norm. For instance, when artificial intelligence is used, a company will most likely cut down the reliance on the human workforce, which means that earnings will go to a few people. Individuals with AI-driven companies are going to amass tremendous wealth. This encourages the widening of the wealth gap. Ethical considerations should be in place to tackle the question of a fair post-lab in our economy.

Humanity is a crucial driver of our day-to-day lives. The development of machines has various effects on our human interactions and behavior. It is, therefore, in order for these machines to be modeled in a way that better human behavior and interactions. While human beings are restricted in their interactions, artificial intelligence can channel virtually immense resources into building relationships. Any data scientist sourced by an artificial intelligence company should ensure he or she has the humanity instincts within herself or himself. Machines trigger codependency with human beings through video games and even clickbait headlines. Nowadays, technology addiction has been a critical influencer on how human beings relate. However, particular data scientist has developed machines that foster better human interactions.

A data scientist should be open to developing his or her work. Though artificial intelligence has the capability and speed of processing, it can only sometimes be trusted to be fair and neutral. These systems are created by humans who can sometimes be judgmental and biased; therefore, companies should strive to have data scientists who will push the agenda of social progress and catalysts of positive change.

A data scientist should always be responsible. Mechanisms should be in place to ensure minimal mistakes occur. Considerations should be in place to enforce training to ensure data scientists work in the correct patterns and the input provided. Another challenge that comes with the development of artificial intelligence is the issue of security. Doubts are always cast on the confidentiality of the data stored. The more powerful a technology becomes, the more it can be used for significant adversaries. Therefore a data scientist should ensure the proper safeguard of data, as improper use can cause immense damage. This is where cyber security comes into place, as these systems are faster and deal with massive data loads.

Consideration should also be in place to deter any consequence which may come as a result of evil genies. This is put in place in case of unforeseen future consequences. For instance, in scenarios where these machines develop formulas that may adversely affect the population. A data scientist should develop mechanisms to detect these mistakes in advance.

Karpatneet al. (2017) argued that data science theories range from machine learning, supervised and unsupervised learning, feature selection, ensemble learning, innovation, and experimentation. Machine learning focus is based on how systems earn from data. Most systems are trained on how to use data and make decisions where the systems improve their decision-making skills by loading more data. This new data manages to beat the spammers who try to infiltrate its confidentiality. Recently machine learning has been reemerging as the reincarnation of artificial intelligence. Machine learning is more about learning and matching inputs with outputs. It tends to focus more on the forecast and works well with big data. The best machine learning algorithm is always chosen, and sample data is run through it for validation.

Artificial intelligence systems may also learn through supervised or unsupervised methods. In a supervised system, the output is churned out based on the input presented, whereas in unsupervised learning, inputs are reorganized and enhanced to restructure unlabeled data. Supervised learning includes broad topics such as regression, classification, and forecasting. Unsupervised learning will include features such as association models and clustering.

Predictions and forecasts play a vital role in the establishment of data science. Artificial intelligence employs predictions to identify an outcome. The alternative forecasts encompass a range of outcomes. In addition, innovation and experimentation bring about new ideas and outcomes. It seeks to merge new ideas with fresh algorithms. Many companies have embraced the use of social websites to run these experiments. It is through these ways that one gets to learn more about human behavior.

Ensemble learning brings about more combinations of machine learning. It employs the technique of boosting, which is the loss function that is being optimized, and does not weigh all the examples in the training data set equally. It also brings in stacking, where models are chained into one another so that the output of low levels becomes the input of higher-level models.

 

  References

Sameer Dhanrajani (2019) Reimagining Strategic management theories and models with artificial intelligence: Retrieved from google: https://www.forbes.com/sites/cognitiveworld/2019/02/27/reimagining-strategic-management-theories-and-models-with-artificial-intelligence/?sh=5bde39a4403e

Julia Bossman.(2016).ethical issues in artificial intelligence{image}Retrieved from  Alumni, Global Shapers Community;https://www.weforum.org/agenda/2016/10/top-10-ethical-issues-in-artificial-intelligence/

Barbara von der Osten.(2021) artificial intelligence from and cons{image}Retrived from rock content writerhttps://rockcontent.com/blog/artificial-intelligence-pros-and-cons/

Galicia, L., & Weismayer, C. (2021). Consumers' reasons and perceived value co-creation of using artificial intelligence-enabled travel service agents. Journal of Business Research, 129, 891-901.

Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, p. 53, 102104.

Bhbosale, S., Pujari, V., & Multani, Z. (2020). Advantages And Disadvantages Of Artificial Intelligence. Aayushi International Interdisciplinary Research Journal, 77, 227-230

. Pelau, C., Ene, I., & Pop, M. I. (2021). THE IMPACT OF ARTIFICIAL INTELLIGENCE ON CONSUMER'S IDENTITY AND HUMAN SKILLS. Amfiteatru Economic, 23(56), 33-45.

Brendel, A. B., Mirbabaie, M., Lembcke, T. B., & Hofeditz, L. (2021). Ethical management of artificial intelligence. Sustainability, 13(4), 1974.

Karpatne, A., Atluri, G., Faghmous, J. H., Steinbach, M., Banerjee, A., Ganguly, A., ... & Kumar, V. (2017). Theory-guided data science: A new paradigm for scientific discovery from data. IEEE Transactions on knowledge and data engineering, 29(10), 2318-2331.