Essay on Artificial Intelligence (AI)

Published: 2021/11/04
Number of words: 1631

In 1950, Alan Turing tried to address the question “can machine think?” in his book ‘Computing machinery and intelligence by developing the Turing test, an imitation game involving written communication. The test involves two subjects, one is human, and another may be a machine or a human. These two subjects were unable to see, hear or touch each other. If the first subject fails to identify whether the second subject is a machine or a human-based on written communication, Alan declares that a machine can think based on this Turing test (Turing, 1950). However, there was a doubt in this theory amongst the scientist; the computer or a machine can answer the user question logically that does not mean that it can think. This thinking of the machine is referred to as ‘Artificial intelligence.’ The idea of AI has existed for the last 50 years. AI is a way of developing a system that can think intelligently based on the given scenarios. Computer technology has been using AI for the last few decades. AI technology allows machines to accomplish complex tasks, which reduces fatigue to humans, such as ‘Google’s predictive search bar.’ Now a day, artificial intelligence has enriched the life of human beings as it has become part of day-to-day life (North, 2018).

The recent decade has experienced a rush of different software that contains elements of AI. There are different subfields of AI, such as image processing, data mining, natural language processing, and machine learning. These subfields have become very significant for many tech giants. For example, Google’s predictive search bar shows suggestions on Netflix, and the spam filter in Gmail actively uses machine learning. Google Voice and Apple’s Siri actively use natural language processing. Facial recognition and tagging in Facebook use image processing. The technologies that have implemented the elements of AI have been around for the last few years only, and they have been proven very helpful for the industry (Gupta, 2017; Oke, 2008). However, the continuous research in AI has improved each subfield in the last few years. Despite the massive success of AI, it is necessary to investigate and critically assess whether the continuous development of AI will make it more intelligent than the human race? If so, what would be its repercussions?

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Classical AI was based on the knowledge-based system and mainly used if-then logic (Lawrence, Palacios-González & Harris, 2018). Humans compare themselves to others to find out they are better than others in many different ways. This human characteristic has been used to develop complex applications. Therefore, it is required to understand the implementation of the human level in AI and the function of the human brain that deals with many decisions and corresponding actions (de Kamps, 2012). The doctors and engineers are not fully aware of the engineering difficulties of the human brain; therefore, existing AI systems are modeled based on existing knowledge about the human brain. The neural network in AI resembles the brain and its neurons. The neuron network in humans passes the electric signal from one part to another to act; for example, the brain processes the data read on the laptop screen and passes the signals to the hand and fingers for the action, which results in typing on the keyboard. Whereas in AI, the task is fed through the neural network, enabling quick and better learning. The layers in the AI are connected through the nodes or neurons that facilitate the transfer of information and learning. The AI system has an input layer that sends the signal to the layer that processes the signals. The processing of the signal is based on the weighted knowledge of the neurons. These hidden processing layers are connected to the output layer, where the actual action or display shows the answer (Stafford, 2011).

AI can assist human decision-making through the expert systems within the AI. These kinds of AI programming are used to forecast the financial market, health system, navigation. The knowledge expert is responsible for the efficiency of an expert system since the knowledge expert evaluates the decision-making of a human expert and develops that into code. Therefore, the knowledge engineer must understand the obstacle and the data to resolve the issue (Narooei & Ramli, 2014). At present, AI technology is proliferating in every area, from day-to-day mobile and computing applications to space programs. It is astonishing why such expert systems are being developed when humans are completely capable of guiding the machines according to their needs. In this situation, many researchers referred to narrow artificial intelligence (ANI), which is developed only for a few and a specific task and not to resemble the human brain and actions that can accommodate multiple tasks (Müller, 2013). Concerning AI, some researchers have forecasted that intelligent machines will populate the earth in the first half of this century and take over the human race’s control by the end of this century. This prediction of apocalyptic AI by these researchers is based on the present trends of the technologies (Geraci, 2012). However, many other researchers are skeptical regarding the thought of apocalyptic AI.

Discussion and evaluation

The literature review shows the neural in AI and its working that resembles the human brain and neurons, the expert system, and its application in the real world. The AI at this moment cannot think for themselves as the programmer has to input the data or information in the system. The present AI system is comparatively weak, and it is called ANI due to the ability to perform specific tasks and incapable of making any different decisions (Müller, 2014). At the same time, strong AI, an artificial general intelligence (AGI), resembles human intelligence in all aspects, including performing intellectual tasks. It is easier to create an ANI than an AGI. AGI is yet to be developed, but once it is developed, it will plan, reason, solve problems, learn quickly, comprehend ideas, learn from experience, and think abstractly (Warwick, 2012). A human being is better at the cognitive task than other animals; however, the question of human intelligence to be general is truly debatable as human intelligence is very advancing compared to the intelligence of other species (Goertzel, 2014).

The machines are better at doing things such as communication; however, it is necessary to note that they do not comprehend the process, but they follow the program, which is a set of instructions. Therefore, they do not show a sign of intelligence as they are following the instructions. At this moment, advanced AI technology does not exist; however, continuous development shows promising results towards developing a robust AI machine that will think and act similar to human beings. Unfortunately, human beings have not yet explored themselves fully to understand how a machine that resembles them would think and act (Bostrom & Shulman, 2016).


AI is playing a vital role in the world right now and the things we can do. The ever-growing AI technology and its continuous innovation will be the dominant factor in interacting with computing technology. Further research and innovation are necessary to develop proper AGI. The influence of AI has improved human life; however, it shows the dependency of humans on technology, and complete reliance will change the future of this technology and human lives forever. Although, this is just the beginning of AI.

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The understanding of the AI concepts has dramatically changed from the philosophical AI in the 1959s and now. Scientists and engineers are trying to break the obstacles to create a new and efficient intelligent system. The literature reviews show that it is essential that artificial intelligence is necessary, and its continuous research and innovation will help to learn more about machine intelligence.

Nonetheless, it is required to reconsider the direction in which the research and application of AI are progressing and the potential obstacles in developing it. Despite many breakthroughs in AI, many scientists believe that the golden era of AI is yet to arrive, which means we could be around the corner or decades away from this golden age of AI. However, the repercussions of entering into this golden era should be considered beforehand to make AI technology safe in terms of using it and safeguard the human race.

Work cited

Bostrom, N., & Shulman, C. (2016). How Hard is Artificial Intelligence? Evolutionary Arguments and Selection Effects. Journal Of Conscious Studies, 19(7-8), 101-130.

Bostrom, N., & Yudkowsky, E. (2011). The ethics of artificial intelligence. Draft for Cambridge Handbook of Artificial Intelligence, 1(1), 3.

De Kamps, M. (2012). Towards truly human-level intelligence in artificial applications. Cognitive Systems Research, 14(1), 1-9. doi: 10.1016/j.cogsys.2011.01.003

Geraci, R. (2012). Apocalyptic AI (2nd ed.). Oxford: Oxford Univ. Press.

Goertzel, T. (2014). The path to more general artificial intelligence. Journal of Experimental & Theoretical Artificial Intelligence, 26(3), 343-354. doi: 10.1080/0952813x.2014.895106

Gupta, N. (2017). A Literature Survey on Artificial Intelligence. International Journal of Engineering Research & Technology, 5(19), 4-5.

Lawrence, D., Palacios-González, C., & Harris, J. (2018). Artificial Intelligence. Cambridge Quarterly of Healthcare Ethics, 25(2), 250-260.

Müller, V. (2014). Philosophy and Theory of Artificial Intelligence. Berlin: Springer Berlin.

Müller, V. (2014). Risks of general artificial intelligence. Journal Of Experimental & Theoretical Artificial Intelligence, 26(3), 297-301. doi: 10.1080/0952813x.2014.895110

Narooei, K., & Ramli, R. (2014). New Approaches in Tool Path Optimization of CNC Machining: A Review. Applied Mechanics and Materials, 663, 657-661. doi: 10.4028/

North, R. (2018). The Importance of Artificial Intelligence in this advanced world. Retrieved from

Oke, S. (2008). A Literature Review on Artificial Intelligence. International Journal of Information and Management Sciences, 19(4), 536-537.

Stafford, R. (2010). Constraints of Biological Neural Networks and Their Consideration in AI Applications. Advances in Artificial Intelligence, 2010, 1-6. doi: 10.1155/2010/845723

Turing, A. (1950). Computing Machinery and Intelligence (1st ed., pp. 433-460). Mind.

Warwick, K. (2012). Artificial intelligence. New York: Routledge.

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