Towards A Learning State
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Towards a Learning State
Author: Ferid Belhaj
language: en
Publisher: World Bank Publications
Release Date: 2022-10-06
The MENA region is facing important vulnerabilities, which the current crises—first the pandemic, then the war in Ukraine—have exacerbated. Prices of food and energy are higher, hurting the most vulnerable, and rising interest rates from the global tightening of monetary policy are making debt service more burdensome. Part I explores some of the resulting vulnerabilities for MENA. MENA countries are facing diverging paths for future growth. Oil Exporters have seen windfall increases in state revenues from the rise in hydrocarbon prices, while oil importers face heightened stress and risk—from higher import bills, especially for food and energy, and the depreciation of local currencies in some countries. Part II of this report argues that poor governance, and, in particular, the lack of government transparency and accountability, is at the root of the region’s development failings—including low growth, exclusion of the most disadvantaged and women, and overuse of such precious natural resources as land and water.
Towards the Learning Grid
Annotation Towards the Learning Grid Advances in Human Learning Services Volume 127 Frontiers in Artificial Intelligence and Applications Edited by: P. Ritrovato, C. Allison, S.A. Cerri, T. Dimitrakos, M. Gaeta and S. Salerno November 2005, approx. 248 pp., hardcover There is a paradigm shift in informatics in general and in technologies enhancing human learning in particular. The debate between the evolutionaries those that wish to optimize and refine current approaches and the revolutionaries those that support a fundamental change of approach is quite actual. Within the Internet communities, the debate is hidden behind the words semantic WEB versus semantic Grid ; within educational technologists between content/resource centered and conversation centered e-learning, or either between teaching and pedagogy on the one side, and learning and communities of practice on the other. In general, in informatics, the shift from a product-page oriented to a service-conversation oriented view may possibly impact most if not all the foreseen applications, in e-learning, but also in e-science, e-democracy, e-commerce, e-health, etc. Part A of the book is dedicated to Position papers: visions about what to do and why to do it in the next years. The remaining parts (B to D) offer partial answers to how to do it. Part B concerns what we called: Content-centered services, i.e.: a vision of learning systems that privileges knowledge and its structures, standards and their interoperability, storage and retrieval services. The subsequent part C has been called: Holistic services to refer to more mature and integrated solutions that address not only content but more generally the creation and management of human Virtual Communities connected on the Grid in order to offer and consume different services facilitating and enhancing human learning. Finally part D is concerned with new directions in learning services.
Federated Learning Systems
Author: Muhammad Habib ur Rehman
language: en
Publisher: Springer Nature
Release Date: 2021-06-11
This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications. The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers. This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications. Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development. Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors’ control of their critical data.