Creating Business Intelligence through Machine Learning: An Effective Business Decision Making Tool

Growing technological progressions have given rise to many issues concerning the contemporary decision making in business, which is a difficult phenomenon without Business Intelligence/ Machine Learning. The linking of machine learning with business intelligence is not only pivotal for business decision making but also for the business intelligence in totality, owing to the reason that in absence of machine learning, decision making couldn’t take place efficaciously. Machines need to learn, re-learn, and then only they can help your learning process. The below paper seeks to make this concept simple/ easy by removing the ambiguities using a general framework. In order to prove the impact of machine learning on business intelligence, we need to forecast the trends, what is going around the world – business has to stay updated, then only it can be a successful endeavour.  The paper posits the basic theories and definitions of business intelligence and machine learning. To learn from the past and forecast the future trends, many companies are adopting business intelligence tools and systems. Companies have understood the brilliance of enforcing achievements of the goals defined by their business strategies through business intelligence concepts and with the help of machine learning. It describes the insights on the role and requirement of real time BI by examining the business needs. Keywords: Business Intelligence (BI); Machine Learning (ML); Artificial Neural Networks (ANN); Self-Organizing Maps (SOM); Data Mining (DM); Data Warehousing (DW)

Detection of Fake Generated Scientific Abstracts

The widespread adoption of Large Language Models and publicly available ChatGPT has marked a significant turning point in the integration of Artificial Intelligence into people's everyday lives. The academic community has taken notice of these technological advancements and has expressed concerns regarding the difficulty of discriminating between what is real and what is artificially generated. Thus, researchers have been working on developing effective systems to identify machine-generated text. In this study, we utilize the GPT-3 model to generate scientific paper abstracts through Artificial Intelligence and explore various text representation methods when combined with Machine Learning models with the aim of identifying machine-written text. We analyze the models' performance and address several research questions that rise during the analysis of the results. By conducting this research, we shed light on the capabilities and limitations of Artificial Intelligence generated text

Machine Learning and AI in Business Intelligence: Trends and Opportunities

The integration of machine learning and artificial intelligence (AI) in business intelligence has brought forth a plethora of trends and opportunities. These cutting-edge technologies have revolutionized how businesses analyze data, gain insights, and make informed decisions. One prominent trend is the rise of predictive analytics. Machine learning algorithms can sift through vast amounts of historical data to identify patterns and trends, enabling businesses to make accurate predictions about future outcomes. This empowers organizations to optimize operations, anticipate customer needs, and mitigate risks.  By leveraging business intelligence, companies can uncover hidden patterns, identify opportunities for growth and improvement, optimize business processes, and ultimately make informed decisions that drive their success. Another trend is the adoption of AI-powered chatbots and virtual assistants. The opportunities presented by machine learning and AI in business intelligence are extensive. From automated data analysis and anomaly detection to demand forecasting and dynamic pricing, these technologies empower businesses to optimize processes, reduce costs, and identify new revenue streams. In conclusion, the integration of machine learning and AI in business intelligence offers promising trends and abundant opportunities. By leveraging these technologies, businesses can gain a competitive edge, drive innovation, and unlock new levels of success in the digital era

Imagining the thinking machine: Technological myths and the rise of artificial intelligence

This article discusses the role of technological myths in the development of Artificial Intelligence (AI) technologies from 1950s to the early 1970s. It shows how the rise of AI was accompanied by the construction of a powerful cultural myth: the creation of a thinking machine, which would be able to perfectly simulate the cognitive faculties of the human mind. Based on a content analysis of articles on Artificial Intelligence published in two magazines, the Scientific American and the New Scientist, which were aimed at a broad readership of scientists, engineers, and technologists, three dominant patterns in the construction of the AI myth are identified: (1) the recurrence of analogies and discursive shifts, by which ideas and concepts from other fields were employed to describe the functioning of AI technologies; (2) a rhetorical use of the future, imagining that present shortcomings and limitations will shortly be overcome; (3) the relevance of controversies around the claims of AI, which we argue should be considered as an integral part of the discourse surrounding the AI myth

Imagining the thinking machine: Technological myths and the rise of artificial intelligence

This article discusses the role of technological myths in the development of Artificial Intelligence (AI) technologies from 1950s to the early 1970s. It shows how the rise of AI was accompanied by the construction of a powerful cultural myth: the creation of a thinking machine, which would be able to perfectly simulate the cognitive faculties of the human mind. Based on a content analysis of articles on Artificial Intelligence published in two magazines, the Scientific American and the New Scientist, which were aimed at a broad readership of scientists, engineers, and technologists, three dominant patterns in the construction of the AI myth are identified: (1) the recurrence of analogies and discursive shifts, by which ideas and concepts from other fields were employed to describe the functioning of AI technologies; (2) a rhetorical use of the future, imagining that present shortcomings and limitations will shortly be overcome; (3) the relevance of controversies around the claims of AI, which we argue should be considered as an integral part of the discourse surrounding the AI myth