No plagiarismI provided the Resources in the file attached, make sure you use the one I provided and read them. I also attached Part 1, Part 2 and Part 3 of this project so you can see the path and pr

ANNOTATED BIBLIOGRAPHY 9

Student's name: Emmanuel Domenech

Professor's name: Ms. Stephanie Jacobe

Topic: Artificial Intelligence

Institution: University of Maryland Global Campus

Date: June 09, 2020

An Annotated Bibliography

Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, the present, and future of AI. California Management Review 61(4), 5-14.

Michael Haenlein and Andreas Kaplan (2019) define AI and examine its past, present, and future. They assert that the technology can be grouped into analytical, human-inspired, and human-AI depending on the type of intelligence exhibited: emotional, cognitive, and social intelligence. According to the authors, the past of the AI involves the ups and downs experienced since the idea was conceived in the 1950s. They assert that focus on the technology came in the 1970s when expert and intelligent systems were developed. Starting in the 1970s, a proliferation of AI has been seen leading to the present’ state where it dominates in areas, such as business and communication. It is focused on the interpretation of vast data. They add that the benefits of the technology have been seen in various enterprises, such as in the automation of machinery. They conclude that the future of the technology needs to see improved regulation to ensure that users are protected.

Shrestha, Y. R., Ben-Menahem, S. M., & Krogh, G. v. (2019). Organizational Decision-making Structures in the Age of Artificial Intelligence. California Management Review 61(4), 66-83.

The researchers have looked into how the advent of AI impacts traditional organizational decision-making, which is solely based on human capabilities. They state that the introduction of AI will change how corporate decisions are made. First, they look into the relationship between AI-decision-making and human decision-making along with five key factors, which are; the decision specificity, the process and outcome interpretability, the alternative set size, the speed of the process, and replicability. Based on these factors, the researchers claim that AI decision-making can be integrated while employing three models: AI-human sequential, hybrid AI-Human, and entire human-AI delegation. They have shown that these models can be employed in various working environments depending on the presenting needs. They conclude that they will enable the integration of AI decision-making in a way that does not necessarily replace the traditional organizational decision-making in modern business environments.

Shanka, D. B., Graves, C., Gott, A., Gamez, P., & Rodriguez, A. (2019). Feeling our way to machine minds: People's emotions when perceiving mind in Artificial Intelligence. Computers in Human Behavior 98(2019), 256-266.

Daniel Shanka (2019) reiterates that it has become too common for people to interact with smart devices with in-built AI tools and technologies. Through an examination of two studies, the researchers affirmed that the interactions with the devices usually exhibit some characteristics of the mind. Individuals who interact with smart devices are amazed, surprised, amused, and confused when encountering devices that are able to depict human-like abilities. The researchers purport that when humans interact with the said devices, emotional reactions usually happen as individuals tend to negotiate the concepts of AI devices and processes that indicate human-like behaviors. They add that such findings suggest that the psychology of mind perception is likely to be impacted in the future, which will significantly influence the traditional social interactions as sophisticated smart devices continue to be developed by the tech giants across the globe. Humans will become socially closer to smart machines in the future.

Anysz, H., Brzozowski, L., Kretowicz, W., & Narloch, P. (2020). Feature Importance of Stabilised Rammed Earth Components Affecting the Compressive Strength Calculated with Explainable Artificial Intelligence Tools. Materials 13(2317), 1-20.

Hubert Anysz (2020) states that the CSRE, which stands for Cement-Stabilized Rammed Earth, is a construction material that is deemed more sustainable as it allows the cost structure to be economized. The authors add that the two aspects are usually realized because the soil material used in the mixture is generally outsourced from areas very close to the site of construction. However, the authors add that it is imperative that the compressive strength is well understood. To do so, machine learning tools can be employed, which are; random forest, decision trees, and artificial neural networks. The devices can be used to predict the compressive strength, which is typically based on the relative mixtures of the CSRE. The researchers gathered about 434 samples of CSRE, where the models were used to test and predict the compressive strength. The researchers concluded that the three models could be relied on when making predictions on the reliability of the construction materials.

Shii, E., Ebner, D., Kimura, S., Agha-Mir-Salim, L., Uchimido, R., & Celi, A. (2020). The advent of medical artificial intelligence: lessons from the Japanese approach. Journal of Intensive Care 8(35), 2-6.

Euma Ishii (2020) reiterates that the AI technologies are essential tools for various human enterprises. The authors add that AI has been hailed as a critical driver of critical care medicine due to the exploitation of vast data that guide the manner in which care should be taken. However, the authors note that there have been problems in relation to how technology is adopted in Japan. They claim that while the technology is poised to bring about many benefits and changes, the clinicians are not familiar with the technologies, which creates a significant gap. In response, Japan's national policy and private sectors have embarked on the creation of a workforce that is familiar with AI technologies and applications. They emphasize that even in other sectors and regions, it is imperative that teams are equipped with the necessary skills and competencies that will enable them to use and exploit the technologies. In so doing, the desired outcomes will be realized.

Li, Y., & Hu, H. (2020). Influential Factor Analysis and Projection of Industrial CO2 on Extreme Learning Machine Improved by Genetic Algorithm. Polish Journal of Environmental Studies 29(3), 2259-2271.

Yanmei Li and Hongdan Hu (2020) studied the carbon emissions in China, which they claim is on a grander scale in the industrial sector than others. To learn more about the trend, the authors argue that it was vital that the influencing factors were studied. They carried out regression analysis and bivariate correlation analysis on 13 influencing factors, which were grouped into four broad categories. A factor analysis was also carried out on each category. Notably, the researchers used the GA-ELM machine in a bid to estimate the industrial carbon emission, which indicated that the use of the tool had a higher level of accuracy and predictability than the ELM and the backpropagation neural networks. Also, the analysis revealed that the five core influencing factors had an essential impact on the industrial emissions of carbon gases. They recommended the use of the GA-ELM in the prediction of industrial carbon emission in Chinese industries.

He, C., Liu, Y., Yao, T., Xu, F., Hu, Y., & Zheng, J. (2019). A fast learning algorithm based on extreme learning machine for regular fuzzy neural network. Journal of Intelligent & Fuzzy Systems 36(2019), 3263–3269.

Chunmei He (2019) looks into the aspects and details of the regular fuzzy neural network (RFNN) and provides essential information as follows. They say that it is a type of fuzzy network, which usually fuzzes the feed-forward neural networks. Also, the researchers add that the RFNN is able to accommodate information directly, and it is credited with the merits of such a system as well as a neural network. Notably, based on the learning machine, and commonly known as the ELM, the authors present it as a fast learning algorithm. They have provided a simulation example, which shows how the fuzzy network is realized using the rules under the RFNN. The authors conclude that the RFNN that is trained using the proposed algorithms shows effective approximation and performance abilities. The author recommends the adoption of the network in a bid to promote approximation and performance abilities.

References

Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, the present, and future of AI. California Management Review 61(4), 5-14.

He, C., Liu, Y., Yao, T., Xu, F., Hu, Y., & Zheng, J. (2019). A fast learning algorithm based on extreme learning machine for regular fuzzy neural network. Journal of Intelligent & Fuzzy Systems 36 (2019), 3263–3269.

Li, Y., & Hu, H. (2020). Influential Factor Analysis and Projection of Industrial CO2 on Extreme Learning Machine Improved by Genetic Algorithm. Polish Journal of Environmental Studies 29(3), 2259-2271.

Shanka, D. B., Graves, C., Gott, A., Gamez, P., & Rodriguez, A. (2019). Feeling our way to machine minds: People's emotions when perceiving mind in Artificial Intelligence. Computers in Human Behavior 98(2019), 256-266.

Shii, E., Ebner, D., Kimura, S., Agha-Mir-Salim, L., Uchimido, R., & Celi, A. (2020). The advent of medical artificial intelligence: lessons from the Japanese approach. Journal of Intensive Care 8(35), 2-16.

Shrestha, Y. R., Ben-Menahem, S. M., & Krogh, G. v. (2019). Organizational Decision-making Structures in the Age of Artificial Intelligence. California Management Review 61(4), 66-83.