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1818 results in Knowledge Management, Databases and Data Mining

Page 26 of 91

  • 1 - Introduction

  • Julian McAuley, University of California, San Diego
  • Book:
    Personalized Machine Learning
    Published online:
    20 January 2022
    Print publication:
    03 February 2022, pp 1-16
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Handbook of Artificial Intelligence and Robotic Process Automation
  • Policy and Government Applications
  • Edited by Al Naqvi, J. Mark Munoz
  • Published by:
    Anthem Press
    Published online:
    20 January 2022
    Print publication:
    27 November 2020
  • On the commercial side, artificial intelligence applications are powering many sectors. Globally, governments are exploring how to comprehend, incorporate, apply, and use artificial intelligence technologies. The scope of government use of artificial intelligence technology goes beyond that of commercial organizations and is far more complex.

    In government, the challenges will be as follows: (1) How can governments use artificial intelligence technology to improve their efficiencies? (2) How can governments become more citizen-centric, service based, accessible, and responsive? (3) How can governments protect their citizens from the misuse of artificial intelligence (e.g., alleged Russian bots’ interference in US elections)? (4) How can governments use artificial intelligence technology to make better policy decisions and avoid wrong decisions (economic, social, etc.)? (5) How can governments develop new standards to govern and manage the deployment of artificial intelligence technologies (e.g., autonomous cars, financial markets and trading, healthcare bots)? (6) How will the legislative bodies respond to the rise of intelligent machines? (7) How will the use of artificial intelligence in the military change the arms race? (8) What roles will governments need to play in developing global standards related to artificial intelligence (United Nations)? (9) How can governments improve their countries’ productivity with artificial intelligence? (10) How can governments handle the upcoming unemployment that would result from AI automation?

    All the above questions are at an early stage of exploration and many have not been addressed comprehensively. This book deals with all the above issues and provides the first guide to governments and policy makers of the world on artificial intelligence.

Personalized Machine Learning
  • Julian McAuley
  • Published online:
    20 January 2022
    Print publication:
    03 February 2022
  • Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.

Edge Learning for Distributed Big Data Analytics
  • Theory, Algorithms, and System Design
  • Song Guo, Zhihao Qu
  • Published online:
    14 January 2022
    Print publication:
    10 February 2022
  • Discover this multi-disciplinary and insightful work, which integrates machine learning, edge computing, and big data. Presents the basics of training machine learning models, key challenges and issues, as well as comprehensive techniques including edge learning algorithms, and system design issues. Describes architectures, frameworks, and key technologies for learning performance, security, and privacy, as well as incentive issues in training/inference at the network edge. Intended to stimulate fruitful discussions, inspire further research ideas, and inform readers from both academia and industry backgrounds. Essential reading for experienced researchers and developers, or for those who are just entering the field.

Page 26 of 91