Application of Artificial Intelligence and Data Science Across Domains: A Perspective Study

Introduction

The science and engineering of developing intelligent machines is what is known as artificial intelligence (AI) (McCarthy, 2007). A subfield of computer science called artificial intelligence (AI) integrates machine learning, algorithm design, and natural language processing (Akgun & Greenhow, 2021). Like the exponential expansion that database technology witnessed in the late 20th century, this technology has grown to be highly well-known in the modern era and is currently expanding at a breakneck pace. The foundational technology that powers enterprise-level software is now databases. Similarly, during the next few decades, it is anticipated that AI will account for the majority of new value added in software. In the simplest type of AI, computers are taught to “imitate” human behaviour utilizing a wealth of data from prior instances of the same behaviour. AI enables machines to operate effectively and quickly evaluate massive volumes of data, finding solutions through supervised, unsupervised, or reinforced learning. Artificial intelligence can be a very effective tool for big businesses that produce a lot of data (Cassel, Lillian & Dicheva 2016). It is now a crucial component of technology. Automating tasks that previously would have required human intelligence is one of the primary goals of AI. Gains in efficiency can be made by reducing the number of labour resources an organization has to use on a project or the amount of time a person needs to spend on repetitive chores. For instance, medical assistant AI can be used to detect diseases based on patients' symptoms, while chatbots can be used to answer customer service concerns.

Utilizing data analysis and analytics is at the heart of data science (where it uses past and present data to predict future data). Data collection, analysis, and decision-making are all part of data science. Data science is the in-depth analysis of a vast amount of data, which entails extracting some useful information from the raw, structured, and unstructured data in order to uncover patterns in the data, through analysis, and make predictions for the future. Businesses can improve their decision-making by utilising data science. Large amounts of data are structured and stored using big data analytics so that they may be quickly accessible and analysed for better decision-making (Corsi & de Souza, 2020). Processing of data, which can be done using statistical techniques and algorithms, scientific methodologies, various technologies, etc., is required in order to extract useful data from massive data sets. It extracts useful information from unstructured data using a variety of tools and methods. The Future of Artificial Intelligence refers to data science. Real-time data from online sources like social media have been extensively collected using big data. To get better outcomes, researchers commonly combine data science and artificial intelligence methods. Contrarily, numerous research studies asserted that big data has a considerable impact on the domains of decision support systems and predictive analytics. Patterns in enormous data sets can be found by AI and data science that are not visible to the human eye. In this approach, the application of AI and Data Science technologies can add value to even commonplace and superficially unimportant data. This work consolidates most of the trending and recent applications in the field of Artificial Intelligence. Also, the key issues and trends have been discussed.