Название: Transformative AI: Responsible, Transparent, and Trustworthy AI systems
Автор: Ahmed Banafa
Издательство: River Publishers
Серия: River Publishers Series in Computing and Information Science and Technology
Год: 2024
Страниц: 172
Язык: английский
Формат: pdf (true)
Размер: 15.1 MB

Transformative Artificial Intelligence provides a comprehensive overview of the latest trends, challenges, applications, and opportunities in the field of Artificial Intelligence (AI). The book covers the state of the art in AI research, including Machine Learning, Natural Language Processing (NLP), Computer Vision, and robotics, and explores how these technologies are transforming various industries and domains, such as healthcare, finance, education, and entertainment.

The book also addresses the challenges that come with the widespread adoption of AI, including ethical concerns, bias, and the impact on jobs and society. It provides insights into how to mitigate these challenges and how to design AI systems that are responsible, transparent, and trustworthy.

The book offers a forward-looking perspective on the future of AI, exploring the emerging trends and applications that are likely to shape the next decade of AI innovation. It also provides practical guidance for businesses and individuals on how to leverage the power of AI to create new products, services, and opportunities.

Machine Learning is a subset of Artificial Intelligence (AI) that involves the use of algorithms and statistical models to enable machines to learn from data and improve their performance on specific tasks. It is a rapidly growing field with numerous applications across various industries, including healthcare, finance, transportation, and more. In this essay, we will explore what Machine Learning is, how it works, and its current and potential future applications. At its core, Machine Learning involves the use of algorithms that enable machines to learn from data. These algorithms are designed to identify patterns and relationships in the data and use that information to make predictions or take actions. One of the most common types of Machine Learning algorithms is supervised learning, which involves training a model on a labeled dataset, where the correct output is known for each input. The model then uses this training data to make predictions on new, unseen data. Another type of Machine Learning is unsupervised learning, which involves training a model on an unlabeled dataset and allowing it to identify patterns and relationships on its own. This type of learning is often used in applications such as clustering, where the goal is to group similar items together. Reinforcement learning is another type of Machine Learning, which involves training a model to make decisions based on feedback from its environment.

The book is divided into two parts, each covering a distinct aspect of AI.

The first part introduces the basic concepts of AI, including the history, types, components of AI, and the most common techniques and algorithms used in AI, such as Machine Learning, Deep Learning, Natural Language Processing, and robotics.

The second part delves into the various applications of AI in different fields, such as IoT, blockchain, quantum computing, robotics, and autonomous cars. In addition, the book covers the ethical and social implications of AI, such as bias, privacy, and job displacement.

This book aims to provide a balanced perspective on AI, presenting its opportunities as well as its challenges. It also includes real-world examples and case studies to help readers understand how AI is being used in practice.

Overall, the book is an essential read for anyone who wants to stay ahead of the curve in the rapidly evolving field of Artificial Intelligence and understand the impact that this transformative technology will have on our lives in the coming years.