Uncertainty in Artificial Intelligence 2

Uncertainty in Artificial Intelligence 2
Author: L.N. Kanal
Publisher: Elsevier
Total Pages: 474
Release: 2014-06-28
Genre: Computers
ISBN: 1483296539

This second volume is arranged in four sections: Analysis contains papers which compare the attributes of various approaches to uncertainty. Tools provides sufficient information for the reader to implement uncertainty calculations. Papers in the Theory section explain various approaches to uncertainty. The Applications section describes the difficulties involved in, and the results produced by, incorporating uncertainty into actual systems.

Artificial Intelligence with Uncertainty

Artificial Intelligence with Uncertainty
Author: Deyi Li
Publisher: CRC Press
Total Pages: 311
Release: 2017-05-18
Genre: Computers
ISBN: 1498776272

This book develops a framework that shows how uncertainty in Artificial Intelligence (AI) expands and generalizes traditional AI. It explores the uncertainties of knowledge and intelligence. The authors focus on the importance of natural language – the carrier of knowledge and intelligence, and introduce efficient physical methods for data mining amd control. In this new edition, we have more in-depth description of the models and methods, of which the mathematical properties are proved strictly which make these theories and methods more complete. The authors also highlight their latest research results.

Artificial Intelligence

Artificial Intelligence
Author: David L. Poole
Publisher: Cambridge University Press
Total Pages: 821
Release: 2017-09-25
Genre: Computers
ISBN: 110719539X

Artificial Intelligence presents a practical guide to AI, including agents, machine learning and problem-solving simple and complex domains.

Uncertainty in Artificial Intelligence

Uncertainty in Artificial Intelligence
Author: Laveen N. Kanal
Publisher: North Holland
Total Pages: 509
Release: 1986
Genre: Artificial intelligence
ISBN: 9780444700582

Hardbound. How to deal with uncertainty is a subject of much controversy in Artificial Intelligence. This volume brings together a wide range of perspectives on uncertainty, many of the contributors being the principal proponents in the controversy.Some of the notable issues which emerge from these papers revolve around an interval-based calculus of uncertainty, the Dempster-Shafer Theory, and probability as the best numeric model for uncertainty. There remain strong dissenting opinions not only about probability but even about the utility of any numeric method in this context.

Reasoning about Uncertainty, second edition

Reasoning about Uncertainty, second edition
Author: Joseph Y. Halpern
Publisher: MIT Press
Total Pages: 505
Release: 2017-04-07
Genre: Computers
ISBN: 0262533804

Formal ways of representing uncertainty and various logics for reasoning about it; updated with new material on weighted probability measures, complexity-theoretic considerations, and other topics. In order to deal with uncertainty intelligently, we need to be able to represent it and reason about it. In this book, Joseph Halpern examines formal ways of representing uncertainty and considers various logics for reasoning about it. While the ideas presented are formalized in terms of definitions and theorems, the emphasis is on the philosophy of representing and reasoning about uncertainty. Halpern surveys possible formal systems for representing uncertainty, including probability measures, possibility measures, and plausibility measures; considers the updating of beliefs based on changing information and the relation to Bayes' theorem; and discusses qualitative, quantitative, and plausibilistic Bayesian networks. This second edition has been updated to reflect Halpern's recent research. New material includes a consideration of weighted probability measures and how they can be used in decision making; analyses of the Doomsday argument and the Sleeping Beauty problem; modeling games with imperfect recall using the runs-and-systems approach; a discussion of complexity-theoretic considerations; the application of first-order conditional logic to security. Reasoning about Uncertainty is accessible and relevant to researchers and students in many fields, including computer science, artificial intelligence, economics (particularly game theory), mathematics, philosophy, and statistics.

The Uncertainty Mindset

The Uncertainty Mindset
Author: Vaughn Tan
Publisher: Columbia University Press
Total Pages: 296
Release: 2020-07-28
Genre: Social Science
ISBN: 0231551878

Innovation is how businesses stay ahead of the competition and adapt to market conditions that change in unpredictable and uncertain ways. In the first decade of the twenty-first century, high-end cuisine underwent a profound transformation. Once an industry that prioritized consistency and reliability, it turned into one where constant change was a competitive necessity. A top restaurant’s reputation and success have become so closely bound up with its ability to innovate that a new organizational form, the culinary research and development team, has emerged. The best of these R&D teams continually expand the frontiers of food—they invent a constant stream of new dishes, new cooking processes and methods, and even new ways of experiencing food. How do they achieve this nonstop novelty? And what can culinary research and development teach us about how organizations innovate? Vaughn Tan opens up the black box of elite culinary R&D to provide essential insights. Drawing on years of unprecedented access to the best and most influential culinary R&D teams in the world, he reveals how they exemplify what he calls the uncertainty mindset. Such a mindset intentionally incorporates uncertainty into organization design rather than simply trying to reduce risk. It changes how organizations hire, set goals, and motivate team members and leads organizations to work in highly unconventional ways. A revelatory look at the R&D kitchen, The Uncertainty Mindset upends conventional wisdom about how to organize for innovation and offers practical insights for businesses trying to become innovative and adaptable.

The Alignment Problem: Machine Learning and Human Values

The Alignment Problem: Machine Learning and Human Values
Author: Brian Christian
Publisher: W. W. Norton & Company
Total Pages: 459
Release: 2020-10-06
Genre: Science
ISBN: 039363583X

A jaw-dropping exploration of everything that goes wrong when we build AI systems and the movement to fix them. Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us—and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem. Systems cull résumés until, years later, we discover that they have inherent gender biases. Algorithms decide bail and parole—and appear to assess Black and White defendants differently. We can no longer assume that our mortgage application, or even our medical tests, will be seen by human eyes. And as autonomous vehicles share our streets, we are increasingly putting our lives in their hands. The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software. In best-selling author Brian Christian’s riveting account, we meet the alignment problem’s “first-responders,” and learn their ambitious plan to solve it before our hands are completely off the wheel. In a masterful blend of history and on-the ground reporting, Christian traces the explosive growth in the field of machine learning and surveys its current, sprawling frontier. Readers encounter a discipline finding its legs amid exhilarating and sometimes terrifying progress. Whether they—and we—succeed or fail in solving the alignment problem will be a defining human story. The Alignment Problem offers an unflinching reckoning with humanity’s biases and blind spots, our own unstated assumptions and often contradictory goals. A dazzlingly interdisciplinary work, it takes a hard look not only at our technology but at our culture—and finds a story by turns harrowing and hopeful.

Uncertainty in Artificial Intelligence

Uncertainty in Artificial Intelligence
Author: Gregory Floyd Cooper
Publisher: Morgan Kaufmann Publishers
Total Pages: 728
Release: 1999
Genre: Artificial intelligence
ISBN:

This volume contains papers accepted for presentation at the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI99) held at the Royal Institute of Technology (KTH) in Stockholm, Sweden from July 30 through August 1, 1999. This conference continues a 15-year tradition of providing an international forum for exchange of ideas on problems of reasoning, under uncertainty. During those 15 years, UAI has moved from a little-noticed niche at the edge of the field, solidly into the mainstream of artificial intelligence research and practice. Research first presented at UAI has contributed significantly to advances in a number of related fields and has found application in a wide variety of domains. The UAI conference has acquired a reputation for excellence, and the proceedings have become an important reference source for high-quality work in the field.

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis
Author: Carole H. Sudre
Publisher: Springer Nature
Total Pages: 233
Release: 2020-10-05
Genre: Computers
ISBN: 3030603652

This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.