Abstract
The history of artificial intelligence (AI) and data science has their origins in the 1940s and 1950s respectively. However, it has been through many changes throughout its history. AI is a vast and fascinating subject. There are many more elements to discover and understand. This chapter aims to outline the history of AI and data science, from its origin to its current developments. It will also explore the ethical considerations within AI and data science, such as bias and fairness, transparency, data privacy, etc. In the end, the chapter sheds light on the ethical concerns regarding the implementation of AI and the security concerns that data science poses. The chapter also provides insights into the role of individuals, government, and society in mitigating these issues. This chapter aims to furnish the reader with the scientific foundation and essential understanding required for embarking on the journey to comprehend the realm of artificial intelligence and data science.
TopBackground And Literature Review
Before advancing further, it is essential to have a foundational understanding of AI and Data Science. This section guides readers through crucial moments and significant advancements, establishing the groundwork for a thorough understanding of these fields' intricate paths. This section will explore scholarly works, research papers, and other authoritative sources related to AI and Data Science. By reviewing these diverse data sources, readers can better understand the foundational concepts underlying AI and Data science. By acknowledging the varied origins, contextual intricacies, and essential discussions within AI and Data Science, this section seeks to cultivate a nuanced viewpoint, recognizing the unique contributions that have shaped today's technological innovation landscape.
Key Terms in this Chapter
Statistics: Statistics is a branch of mathematics that deals with finding insights from numerical data to make informed decisions.
Bias: Bias refers to the action or output of an AI system that discriminates against an individual, a group, or a thing due to the prejudiced data used for its training.
Data Scientist: A data scientist is the person responsible for sourcing, managing, preparing, and analyzing raw data to get insights for problem-solving and decision-making.
Neural Network: A Neural Network is an interconnected layered structure of multiple nodes or neurons, which enables the system to use information within these nodes or neurons simultaneously to analyze and process data.
Cookie: Cookies are small strings of data that a web server introduces into a user's computer to store the browsing activities and user information.
Data Science: Data Science is an interdisciplinary field that focuses on sourcing, managing, preparing, and analyzing raw data to extract meaningful insights that help find solutions to problems and in decision-making.
AI Winter: AI winter refers to the collapse of the Artificial Intelligence industry due to lack of funding, the disinterest of the public and stakeholders, etc.
Machine Learning: Machine Learning is the field of Artificial Intelligence that deals with the study of developing algorithms that help AI machines learn and enhance from experience.
Big Data: Big data refers to the immense and complex raw data accumulating over time due to the internet and technology use.
Artificial Intelligence (AI): AI is the branch of Computer Science that deals with systems capable of solving problems and adapting to new environments by learning, understanding, and applying knowledge from past data.