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Abstract Machine learning is a growing field of research with many applications. It provides a series of techniques able to solve complex nonlinear problems, and that has promoted their application for statistical downscaling. Intercomparison exercises with other classical methods have so far shown promising results. Nevertheless, many evaluation studies of statistical downscaling methods neglect the analysis of their extrapolation capability. In this study, we aim to make a...
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Motivated by the fact that many relations cross the sentence boundary, there has been increasing interest in document-level relation extraction (DocRE). DocRE requires integrating information within and across sentences, capturing complex interactions between mentions of entities. Most existing methods are pipeline-based, requiring entities as input. However, jointly learning to extract entities and relations can improve performance and be more efficient due to shared parameters and training...
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Climate change is a global issue that must be considered and addressed immediately. Many articles have been published on climate change mitigation and adaptation. However, new methods are required to explore the complexities of climate change and provide more efficient and effective adaptation and mitigation policies. With the advancement of technology, machine learning (ML) and deep learning (DL) methods have gained considerable popularity in many fields, including climate change. This...
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The aim of this book is to provide a holistic view to ensure that AI empowers educators and learners, not over-empowers them, and that future developments and practices are truly for the common good. Artificial intelligence (Al) is increasingly having an impact on education, bringing opportunities as well as numerous challenges. These observations were noted by the Council of Europe’s Committee of Ministers in 2019 and led to the commissioning of this report, which sets out to examine the...
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Artificial intelligence (AI) is beginning to transform traditional research practices in many areas. In this context, literature reviews stand out because they operate on large and rapidly growing volumes of documents, that is, partially structured (meta)data, and pervade almost every type of paper published in information systems research or related social science disciplines. To familiarize researchers with some of the recent trends in this area, we outline how AI can expedite individual...
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Abstract Although artificial intelligence (AI; inclusive of machine learning) is gaining traction supporting climate change projections and impacts, limited work has used AI to address climate change adaptation. We identify this gap and highlight the value of AI especially in supporting complex adaptation choices and implementation. We illustrate how AI can effectively leverage precise, real‐time information in data‐scarce settings. We focus on supervised learning, transfer...
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In low- and middle-income countries (LMICs), AI has been promoted as a potential means of strengthening healthcare systems by a growing number of publications. We aimed to evaluate the scope and nature of AI technologies in the specific context of LMICs. In this systematic scoping review, we used a broad variety of AI and healthcare search terms. Our literature search included records published between 1st January 2009 and 30th September 2021 from the Scopus, EMBASE, MEDLINE, Global Health...
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In a technology context dominated by data-intensive AI systems, the consequences of data processing are no longer restricted to the well-known privacy and data protection issues but encompass prejudices against a broader array of fundamental rights. Moreover, the tension between the extensive use of these systems, on the one hand, and the growing demand for ethically and socially responsible data use on the other, reveals the lack of a framework that can fully address the societal issues raised by AI.
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Christian Stoll is a research affiliate at the Center for Energy Markets of the Technical University of Munich and at the Center for Energy and Environmental Policy Research of the Massachusetts Institute of Technology. He co-founded CCRI and works as a management consultant. His research focuses on the intersection of sustainability and cryptocurrencies. Ulrich Gallersdörfer is a research associate at the Department of Informatics of the Technical University of Munich. He is a co-founder...
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We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile, whose weights will be made freely and openly available to the public through a permissive license. It is, to the best of our knowledge, the largest dense autoregressive model that has publicly available weights at the time of submission. In this work, we describe \model{}'s architecture and training and evaluate its performance on a range of language-understanding, mathematics, and...