Literature Review on Artificial Intelligence

Artificial Intelligence (AI) has, in the 21st Century, become a significant area of research in fields like medicine, accounting, law, stock market, education, engineering, and science. The field of Artificial Intelligence has grown massively to the extent that trucking the proliferation of research has become a challenging task. Apart from applying AI to distinct fields, research has also been segregated into numerous areas, with each field springing up distinct fields of understanding (Eiter et al., 2003).

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Evolution of Artificial Intelligence

Artificial Intelligence has a much longer history than it is commonly known or understood. Nonetheless, its contemporary iteration is owed to Alan Turing (1950) and the conference held in Dartmouth College in 1956. It was during the conference that the term Artificial Intelligence was devised and defined by John McCarthy as a Science and Engineering of creating intelligent technologies. Russel and Norvig (2020) referred to the period as the time when AI was born. The authors argue that one of the paradigms of artificial intelligence was around high-level cognition. It did not revolve around the ability to perceive objects, execute multifaceted motor skills, or recognize concepts that are shared by most human beings or animals but in the ability to take part in multi-step artifacts, understand the meaning of natural language, reason about their reasoning, and generate novel plans that enable the machine to achieve its goals (Langley, 2011). Based on this kind of argument, this general human-like intelligence is documented as strong Artificial Intelligence. Kurzweil (2005) argues that the primary approach of strong AI revolves around symbolic reasoning whereby computers are not just numeric calculators but also general symbol schemers. As documented by Newell and Simon (1976), intelligent behavior needs to manipulate and interpret symbolic structures. Although the approach showed promise originally, most fields in AI have retreated from the approach because of the challenges associated with it, as well as the lack of progress made in the 21st century.

Reasoning in Artificial Intelligence

The concept regarding reasoning in AI has been analyzed under distinct areas that include the complexity of reasoning, conditional plausibility, logic and consistency, and sampling algorithm. As such, literature about the complexity of reasoning relates expressive description and spatial congruence logics. For that reason, it shows that the satisfiability challenge in spatial algebra MC-4 is NP, and it is based on the individuation of three maximal tractable subclasses. Cheng and Druzdzel (2000) studied the complexity of scription logics ALCQI and ALCQ combination with terminological formalization that is based on cardinality restriction concepts.

AI Functions

Artificial Intelligence has numerous functions. Nonetheless, this literature review will focus on the function of AI, as stated by Dejoux and Leon (2018). For instance, the authors document that AI is designed to mimic a problem-solving capability or behavior possessed by human beings. Besides, they also argue that AI is designed to automatically refine its methodology while improving outcomes as it continues to get more information or data. Based on their findings and numerous other studies, there is a potential of adopting Artificial Intelligence in numerous fields like digital marketing, healthcare generation, and manufacturing. According to Wang and Wang (2007), for all the manufacturing sector, factories are more likely to use Artificial Intelligence since product automation has been increasing and the industry is using more cyber-physical systems and AI. On the other hand, healthcare researchers are also proposing the use of AI systems that are linked to sensors, which are placed to monitor human beings or patients, as well as record their health (Rubik and Jabs, 2018). When it comes to digital marketing, researchers predict that the demand for forecasting using Artificial Intelligence will be more likely to increase in the future. Besides, the researchers also project that chatbox interactions will get to 22 billion from the current 2.6 billion. Nonetheless, they argue that the opportunities are only available to firms and people who understand the significance of AI.

Application of Artificial Intelligence in Robots

Robots are described as advanced automation technologies that are used for both production and non-production activities to make life easier, as well as improve productivity in organizations or workplaces. In manufacturing systems, most manufacturing companies have turned to robots and automation as the best and reliable manufacturing system solutions. In most cases, robots are used and found in unusual places where working and environmental conditions present dangerous or hazardous tasks that should be performed. As such, research on robots has been regarded and viewed on varied dimensions, robot participation in agent teams, robot’s localization, and navigation. Shatkay and Kaelbling’s (2002) study is credited to robot navigation. They described a formal framework that would incorporate geometrical constraints and odometric information into both algorithm and model that learns them. Fox et al. (1999) offer another version of the Markov localization that provides precise position estimates tailored towards a dynamic environment. The idea of Markov’s localization is maintaining a probability density over a robot’s space of location in its environment. The act entails presenting a different environment from what is attained in the literature, thereby contributing a unique knowledge to the already available studies. Generally, there are distinct benefits of AI in creativity because they are motivated by human thinking and the human mind.

References

Cheng, J., & Druzdzel, M. J. (2000). AIS-BN: An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks.Journal of Artificial Intelligence Research,13, 155-188.

Dejoux, C., & Lon, E. (2018).Mtamorphose des managers…: l’re du numrique et de l’intelligence artificielle. Pearson.

Eiter, T., Faber, W., Leone, N., Pfeifer, G. and Polleres, A., Answer set planning under action costs, Journal of Articial Intelligence Research, Vol. 19, pp.25-71, 2003.

Fox, D., Burgard, W., & Thrun, S. (1999). Markov localization for mobile robots in dynamic environments.Journal of artificial intelligence research,11, 391-427.

Kurzweil, R. (2005).The singularity is near: When humans transcend biology. Penguin.

Langley, P. (2011). Artificial intelligence and cognitive systems.AISB Quarterly,2.

Newell, A., & Simon, H. A. (1976). Computer science as empirical inquiry: Symbols and search. InACM Turing award lectures(p. 1975).

Norvig, P. R., & Intelligence, S. A. (2020).A modern approach. Upper Saddle River, NJ, USA:: Prentice-Hall.

Shatkay, H., & Kaelbling, L. P. (2002). Learning geometrically-constrained hidden Markov models for robot navigation: Bridging the topological-geometrical gap.Journal of Artificial Intelligence Research,16, 167-207.

Wang S., Wang Y., Du W., Sun F., Wang X., Zhou C. and Liang Y., A multi-approaches-guided genetic algorithm with application to operon prediction, Articial Intelligence in Medicine, Vol. 41, No. 2, pp.151-159, 2007

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