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Conversational AI
This book provides a comprehensive introduction to Conversational AI. While the idea of interacting with a computer using voice or text goes back a long way, it is only in recent years that this idea has become a reality with the emergence of digital personal assistants, smart speakers, and chatbots. Advances in AI, particularly in deep learning, along with the availability of massive computing power and vast amounts of data, have led to a new generation of dialogue systems and conversational interfaces. Current research in Conversational AI focuses mainly on the application of machine learning and statistical data-driven approaches to the development of dialogue systems. However, it is impo...
The Ethics of Artificial Intelligence
The Ethics of Artificial Intelligence has two goals. The first goal is meta-theoretical and is fulfilled by Part One, which comprises the first three chapters: an interpretation of the past (Chapter 1), the present (Chapter 2), and the future of AI (Chapter 3). Part One develops the thesis that AI is an unprecedented divorce between agency and intelligence. On this basis, Part Two investigates the consequences of such a divorce, developing the thesis that AI as a new form of agency can be harnessed ethically and unethically. It begins (Chapter 4) by offering a unified perspective on the many principles that have been proposed to frame the ethics of AI. This leads to a discussion (Chapter 5) ...
Research Handbook on Health, AI and the Law
This is an open access title available under the terms of a CC BY-NC-ND 4.0 License. It is free to read, download and share on Elgaronline, thanks to generous funding support from Hamad Bin Khalifa University (HBKU). The Research Handbook on Health, AI and the Law explores the use of AI in healthcare, identifying the important laws and ethical issues that arise from its use. Adopting an international approach, it analyses the varying responses of multiple jurisdictions to the use of AI and examines the influence of major religious and secular ethical traditions.
Digitalization of Society and Socio-political Issues 1
Digitalization is a long and constant sociohistoric process in which all areas of societys activities are reconfigured. Digitalization of Society and Socio-political Issues 1 examines the transformations linked to the development of digital platforms and social media, which affect the cultural and communicational industries. It analyzes the formation of Big Data, their algorithmic processing and the societal changes which result (social monitoring and control in particular). Through critical views, it equally presents the various ways in which technology participates in relations of power and domination, and contributes to possible emancipatory practices.
STAIRS 2012
The field of Artificial Intelligence is one in which novel ideas and new and original perspectives are of more than usual importance. The Starting AI Researchers' Symposium (STAIRS) is an international meeting which supports AI researchers from all countries at the beginning of their career, PhD students and those who have held a PhD for less than one year. It offers doctoral students and young post-doctoral AI fellows a unique and valuable opportunity to gain experience in presenting their work in a supportive scientific environment, where they can obtain constructive feedback on the technical content of their work, as well as advice on how to present it, and where they can also establish c...
Deep Reinforcement Learning
Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how sub...
Machine Learning for Business Analytics
MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning —also known as data mining or data analytics— is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life ca...
Seizure Prediction in Epilepsy
Comprising some 30 contributions, experts from around the world present and discuss recent advances related to seizure prediction in epilepsy. The book covers an extraordinarily broad spectrum, starting from modeling epilepsy in single cells or networks of a few cells to precisely-tailored seizure prediction techniques as applied to human data. This unique overview of our current level of knowledge and future perspectives provides theoreticians as well as practitioners, newcomers and experts with an up-to-date survey of developments in this important field of research.
Recent Advances in Reinforcement Learning
This book constitutes revised and selected papers of the 9th European Workshop on Reinforcement Learning, EWRL 2011, which took place in Athens, Greece in September 2011. The papers presented were carefully reviewed and selected from 40 submissions. The papers are organized in topical sections online reinforcement learning, learning and exploring MDPs, function approximation methods for reinforcement learning, macro-actions in reinforcement learning, policy search and bounds, multi-task and transfer reinforcement learning, multi-agent reinforcement learning, apprenticeship and inverse reinforcement learning and real-world reinforcement learning.
