Integration of AI Tools Into Your Research

Artificial Intelligence: An Introduction for the Inquisitive Reader guides readers through the history and development of AI, from its early mathematical beginnings through to the exciting possibilities of its potential future applications. To make this journey as accessible as possible, the authors build their narrative around accounts of some of the more popular and well-known demonstrations of artificial intelligence including Deep Blue, AlphaGo and even Texas Hold'em, followed by their historical background, so that AI can be seen as a natural development of mathematics and computer science. As the book moves forward, more technical descriptions are presented at a pace that should be suitable for all levels of readers, gradually building a broad and reasonably deep understanding and appreciation for the basic mathematics, physics, and computer science that is rapidly developing artificial intelligence as it is today. Features: Only mathematical prerequisite is an elementary knowledge of calculus Accessible to anyone with an interest in AI and its mathematics and computer science Suitable as a supplementary reading for a course in AI or the History of Mathematics and Computer Science in regard to artificial intelligence.

This book provides a framework for thinking about foundational philosophical questions surrounding the use of deep artificial neural networks ("deep learning") to achieve artificial intelligence. Specifically, it links recent breakthroughs to classic works in empiricist philosophy of mind. In recent assessments of deep learning's potential, scientists have cited historical figures from the philosophical debate between nativism and empiricism, which concerns the origins of abstract knowledge. These empiricists were faculty psychologists; that is, they argued that the extraction of abstract knowledge from experience involves the active engagement of psychological faculties such as perception, memory, imagination, attention, and empathy. This book explains how recent deep learning breakthroughs realized some of the most ambitious ideas about these faculties from 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: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit, and philosophers can see how some of the historical empiricists' most ambitious speculations can now be realized in specific computational systems.

The prolific deployment of Artificial Intelligence (AI) across different fields has introduced novel challenges for AI developers and researchers. AI is permeating decision making for the masses, and its applications range from self-driving automobiles to financial loan approvals. With AI making decisions that have ethical implications, responsibilities are now being pushed to AI designers who may be far-removed from how, where, and when these ethical decisions occur. Trolley Crash: Approaching Key Metrics for Ethical AI Practitioners, Researchers, and Policy Makers provides audiences with a catalogue of perspectives and methodologies from the latest research in ethical computing. This work integrates philosophical and computational approaches into a unified framework for ethical reasoning in the current AI landscape, specifically focusing on approaches for developing metrics. Written for AI researchers, ethicists, computer scientists, software engineers, operations researchers, and autonomous systems designers and developers, Trolley Crash will be a welcome reference for those who wish to better understand metrics for ethical reasoning in autonomous systems and related computational applications.

This open access book provides a detailed review of the latest methods and applications of artificial intelligence (AI) and machine learning (ML) in medicine. With chapters focusing on enabling the reader to develop a thorough understanding of the key concepts in these subject areas along with a range of methods and resulting models that can be utilized to solve healthcare problems, the use of causal and predictive models are comprehensively discussed. Care is taken to systematically describe the concepts to facilitate the reader in developing a thorough conceptual understanding of how different methods and resulting models function and how these relate to their applicability to various issues in health care and medical sciences. Guidance is also given on how to avoid pitfalls that can be encountered on a day-to-day basis and stratify potential clinical risks. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfallsis a comprehensive guide to how AI and ML techniques can best be applied in health care. The emphasis placed on how to avoid a variety of pitfalls that can be encountered makes it an indispensable guide for all medical informatics professionals and physicians who utilize these methodologies on a day-to-day basis. Furthermore, this work will be of significant interest to health data scientists, administrators and to students in the health sciences seeking an up-to-date resource on the topic.

Companies have more data at their fingertips than ever before. Yet, studies show that many executives and organizations fail to extract real value from it. Challenging the conventional wisdom of data-driven decision-making, marketing professors and behavioral scientists Bart De Langhe and Stefano Puntoni argue that many analytics efforts flounder because data analyses are disconnected from the decisions to be made. In their important book, they offer a new approach they call decision-driven analytics. Counterintuitively, they argue that the key to making good decisions with data is to start by putting data in the background. Drawing from their own research and teaching, as well as real-world business cases, De Langhe and Puntoni offer four pillars of decision-driven analytics and guide you around common mistakes that have held back many organizations from using data for impact. In Decision-Driven Analytics, you will learn how to: + Avoid common pitfalls in data-driven decision-making; + Close the gap between managers and decision-making on one side, and data scientists and data analytics on the other; + Enhance the impact of data analytics on business outcomes; + Think without data to make better decisions; + Prepare for artificial intelligence's impact on data analytics; and + Evaluate the costs and benefits of decision-driven analytics. A must-read for anyone who wants to harness the power of data for competitive advantage, Decision-Driven Analytics will equip you with the skills and tools you need to more effectively use data for business outcomes and to make better decisions in today's complex and data-rich world.

In recent years, artificial intelligence (AI) has moved in popular discourse from the purview of science-fiction imaginings to the key financial sector of the twenty-first century. As world powers, trillion-dollar companies, and public intellectuals emphasize the importance of AI, the general concerns people raise relate to economic movement, control, bias, and safety.   This book adds a further concern, namely the way our approach to AI reinforces assumptions about dignity and personhood tied to the sort of thinking that is characteristic of bourgeois capitalists. The experience of poverty reveals that people who are poor do not think the same way as the upper classes--their experience of the world must be understood through the reality of survival within resource-scarce settings and the attendant domination and discrimination that come with being poor. These experiences do not fit well with the "ideal choice" selection model that underlies AI modeling, and numerous failures of AI to help the poor demonstrate that those who benefit primarily from AI are those who already live well.   As a result, the fervor surrounding AI often serves to dehumanize the poor by eliminating employment opportunities, automating social work, reinforcing biases, and prioritizing profit over stability. Worst of all, however, AI functions to satisfy a psychological need for us to have "others" against whom we can distinguish ourselves without having to feel guilty about the reality of the struggle of the poor. Taking seriously the theological perspective of the "preferential option for the poor," this work contends that to avoid relegating poor people to nonhuman status, we must be willing to put aside the fantasy that AI is "intelligent" and focus rather on the all-too-human embodied reality of the poor.