Rethinking AI aims to change people's perspective about artificial intelligence from being fear- or sensation-based to being knowledge- and fact-based. Too many people in today's world are fearful of AI, yet they do not understand how it works at any level. Rethinking AI aims to change this view by offering an inside perspective in the field of AI to explain the logic behind how many of the most important AI algorithms work without heavy mathematics. It shares answers to some of the most pressing questions about the emerging use of AI in certain industries, along with a debate on the safety and ethics of AI for the future.
Readers will learn how some of the most important algorithms, which serve as the basis for large language models like ChatGPT, work, such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. Rethinking AI also provides a roadmap to the reader through the different sections of the world of AI, containing algorithms that can be used to solve many different problems. In the process, readers will learn about the methods for how data is prepared and inserted into the algorithms so they can get a complete perspective on how AI works. The types of AI algorithms covered include linear regression, classification, clustering, and deep learning.
The second part of the book goes over the ethics and use of different AI algorithms in various fields, along with questions regarding each of them. Rethinking AI specifically focuses on the sectors of education, government, and business, which are poised to receive the most disruption from AI in the future. The book also discusses the current research in the field of AI and whether AI should continue to be developed.