Rational Machines And Artificial Intelligence
Download Rational Machines And Artificial Intelligence PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Rational Machines And Artificial Intelligence book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Rational Machines and Artificial Intelligence
Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial Intelligence technologies such as deep learning. They are used in decision making. One element of decision making is the issue of rationality. Regulations such as the General Data Protection Regulation (GDPR) require that decisions that are made by these intelligent machines are explainable. Rational Machines and Artificial Intelligence proposes that explainable decisions are good but the explanation must be rational to prevent these decisions from being challenged. Noted author Tshilidzi Marwala studies the concept of machine rationality and compares this to the rationality bounds prescribed by Nobel Laureate Herbert Simon and rationality bounds derived from the work of Nobel Laureates Richard Thaler and Daniel Kahneman. Rational Machines and Artificial Intelligence describes why machine rationality is flexibly bounded due to advances in technology. This effectively means that optimally designed machines are more rational than human beings. Readers will also learn whether machine rationality can be quantified and identify how this can be achieved. Furthermore, the author discusses whether machine rationality is subjective. Finally, the author examines whether a population of intelligent machines collectively make more rational decisions than individual machines. Examples in biomedical engineering, social sciences and the financial sectors are used to illustrate these concepts. - Provides an introduction to the key questions and challenges surrounding Rational Machines, including, When do we rely on decisions made by intelligent machines? What do decisions made by intelligent machines mean? Are these decisions rational or fair? Can we quantify these decisions? and Is rationality subjective? - Introduces for the first time the concept of rational opportunity costs and the concept of flexibly bounded rationality as a rationality of intelligent machines and the implications of these issues on the reliability of machine decisions - Includes coverage of Rational Counterfactuals, group versus individual rationality, and rational markets - Discusses the application of Moore's Law and advancements in Artificial Intelligence, as well as developments in the area of data acquisition and analysis technologies and how they affect the boundaries of intelligent machine rationality
From Deep Learning to Rational Machines
Author: Cameron J. Buckner
language: en
Publisher: Oxford University Press
Release Date: 2024
This book explains how recent deep learning breakthroughs realized some of the most ambitious ideas of empiricist philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke, David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science.
The Balancing Problem in the Governance of Artificial Intelligence
This book examines the balancing problems in the governance of artificial intelligence (AI). AI is transforming the world at an unprecedented pace, which is revolutionary and presents significant challenges. Nevertheless, AI's complex balance dilemma necessitates careful governance as it transforms businesses, economies, and society. Fundamental issues discussed in this book include the complexities of AI's dual nature, the challenges of aligning memorizing with thinking, and the trade-offs between opportunity seeking and risk aversion. This book explores the complex interplay between AI security and transparency, the technical decision between CPUs and GPUs, and the expanding potential of quantum computing. Nevertheless, the challenge of maintaining balance is not resolved by technological advancements. It encompasses the global arena, where the forces of globalization and localization must be reconciled, and the governance sphere, where self-regulation must coexist with government control. Comprising cutting-edge research, real-world examples, and futuristic perspectives, this book guides researchers, practitioners, politicians, entrepreneurs, and leaders in navigating AI's future. The reader will learn how to capitalize on the potential of AI while avoiding its weaknesses, ensuring that this disruptive technology benefits society.