Current Topics In Artificial Intelligence

This book constitutes the thoroughly referred post-proceedings of the 11th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2005, held in Santiago de Compostela, Spain in November 2005. The 48 revised full papers presented together with an invited paper were carefully selected during two rounds of reviewing and improvement from an initial total of 147 submissions. The papers span the entire spectrum of artificial intelligence from foundational and theoretical issues to advanced applications in various fields.

This book constitutes the thoroughly refereed joint post-proceedings of the 10th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2003, and the 5th Conference on Technology Transfer, TTIA 2003, held in San Sebastián, Spain, in November 2003. The 66 revised full papers presented together with one invited paper were carefully selected during two rounds of reviewing and improvement from an initial total of 214 submissions. The papers span the entire spectrum of artificial intelligence and advanced applications in various fields.

This book constitutes the refereed proceedings of the 12th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2007, held in Salamanca, Spain, in November 2007, in conjunction with the 7th Workshop on Artificial Intelligence Technology Transfer, TTIA 2007. The 28 revised full papers presented address all current issues of artificial intelligence ranging from methodological and foundational aspects to advanced applications in various fields.

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

This book constitutes the refereed proceedings of the 13th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2009, held in Seville, Spain, in November 2009, in conjunction with the Workshop on Artificial Intelligence Technology Transfer, TTIA 2009. The 31 revised full papers presented were carefully selected from 125 submissions. The papers address the following topics: machine learning, multiagents, natural language, planning, diagnosis, evolutive algorithms and neural networks, knowledge representation and engineering, tutoring systems, uncertainty bayesian networks, vision, and applications.

A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.

Exploring issues from big-data to robotics, this volume is the first to comprehensively examine the regulatory implications of AI technology.

The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises.