Sidhdharth Patel
ENG 101
Impacts of machine learning
The world is constantly experiencing technological advancements that are likely to continue moving forward. Over the recent years, notable steps have been made in the expansion of knowledge surrounding machine learning and the associated technology. Machine learning can, therefore, be described as the application of Artificial Intelligence (AI) tools providing computer systems with the learning and improvement abilities from experiences without explicit programming. The focus in machine learning is the development of programs with the ability to acquire data and use it for their learning purposes. There have been concerns about the impact of machine learning on the today’s society since some experts have suggested that the severity of such impacts is not easily predictable. This paper highlights machine learning as a part of the modern society with its increased applicability on various areas both in businesses and other aspects of the society and an assessment of its impacts, especially on business enterprises.

The first impact of machine learning can be seen in the labor market where the continued development of knowledge in AI is likely to affect the skills set of the people. Even though experts have suggested that such a sharp reduction in the number of workers is not possible in the short run, people are increasingly becoming worried as a result of the fear of losing jobs to machines that can do the same work (Makridakis 46). Therefore the skills that people choose to acquire as well as the type of business investments will determine how long the human workers will still be relevant in the future workplace. However, it is important to note that predicting how machine learning is likely to affect each profession is difficult since machine learning either automates or semi-automates the individual tasks but most jobs require accomplishing multiple tasks at the same time which can only be possible through human labor. But with the continued research on this subject soon machine learning will enable the machines also to accomplish multiple tasks. Makridakis (57) outline that the jobs that are already widely applying the AI tools include the financial markets analysis, credit card fraud detection, recommendation systems. According to (Bini 5) more studies are still occurring in the field of medical diagnosis with predictable success.
Machine learning is also likely to improve the businesses processes that are sometimes inefficient when done by the human being. Machine learning enhances either the automation or the semi-automation of business processes making it easier to coordinate and accomplish some of these tasks in time (Gabriel, Signolet and Westwell 6). The main area where AI tools have been used to enhance the businesses processes are the manufacturing sector where automation in the processes of delivering the raw materials into the production unit results in a similar process at the production of finished products which increases efficiency during the production process (Littover 485). The manufacturing processes undertaken by humans are sometimes prone to error thereby increasing the number of wastages (Bottou 135). For instance, machine learning has enabled chip maker NVIDIA to increase their production massively. Machine learning has further been used to manage the supply chain effectively which ensures that products in transit are easily monitored and evaluated during the entire supply chain (Zhou 56).
Machine learning and the AI also have positive impacts on the quality of life in the society. Tech companies including the well-established ones such as Google, Apple, and Facebook among others as well as startups are in an arms race to deliver the best machine learning techniques for the people (Makridakis 52). The positive impact of that is the provision of advanced products and services that are likely to improve the quality of life. For example, the self-driving car concept is majorly based on machine learning with various companies already in the race to produce this car which will revolutionize the future transport. The AI technology is being expanded to other areas such as the autonomous flying cars which can significantly reduce the commuting time (Bottou 142). Other areas that are likely to improve life are AI diagnosis and doctor assistants that will improve healthcare. Already the AI technology is used in sports, for example in the virtual assistant referees to manage soccer matches in the Spanish football leagues that have tremendously improved the accuracy and efficiency of sports management.
Zhou (55) highlights that machine learning and the AI technologies have also been used to predict the future events by combining the current and past data thereby increasing the accuracy of these predictions. For example, AI is increasingly used by the meteorological department to predict the weather and the climatic patterns such that timely warnings are issued in cases of extreme weather conditions (Greenwald and Oertel). These tools were vital in predicting the different hurricanes that wreaked havoc in the US in 2017 such that there were timely warnings to vacate the paths of the hurricanes thereby reducing the death rates from such tragedies.

Essentially, machine learning is increasingly becoming part of the society with its increased applicability on various areas both in businesses and other aspects of the society. Although there are concerns that the machine learning concept is likely to result in job losses, this concepts has tremendous benefits to the society. More research should, therefore, continue such that the technology is successfully incorporated into the society while addressing the concerns.

Works Cited
Bini, Stefano A. “Artificial Intelligence, Machine Learning, Deep Learning, And Cognitive Computing: What Do These Terms Mean And How Will They Impact Health Care?.” The Journal of Arthroplasty (2018): n. pag. Web.

Bottou, Léon. “From Machine Learning To Machine Reasoning.” Machine Learning 94.2 (2013): 133-149. Web.

Gabriel, Florence, Jason Signolet, and Martin Westwell. “A Machine Learning Approach To Investigating The Effects Of Mathematics Dispositions On Mathematical Literacy.” International Journal of Research ; Method in Education (2017): 1-22. Web.

Greenwald, Hal S., and Carsten K. Oertel. “Future Directions In Machine Learning.” Frontiers in Robotics and AI 3 (2017): n. pag. Web.

Littover, Mati. “Artificial Intelligence Applications In Manufacturing.” Engineering Applications of Artificial Intelligence 6.5 (1993): 485-488. Web.

Makridakis, Spyros. “The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact On Society And Firms.” Futures 90 (2017): 46-60. Web.

Zhou, Zhi-Hua. “Machine Learning Challenges And Impact: An Interview With Thomas Dietterich.” National Science Review 5.1 (2017): 54-58. Web.