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on Big Data |
| By: | Baumgärtner, Martin; Zahner, Johannes |
| Abstract: | Dictionary approaches are at the forefront of current techniques for quantifying central bank communication. This paper proposes embeddings - a language model trained using machine learning techniques - to locate words and documents in a multidimensional vector space. To accomplish this, we utilize a text corpus that is unparalleled in size and diversity in the central bank communication literature, as well as introduce a novel approach to text quantification from computational linguistics. This allows us to provide high-quality central bank-specific textual representations and demonstrate their applicability by developing an index that tracks deviations in the Fed's communication towards inflation targeting. Our findings indicate that these deviations in communication significantly impact monetary policy actions, substantially reducing the reaction towards inflation deviation in the US. |
| Keywords: | Word Embedding, Neural Network, Central Bank Communication, Natural Language Processing, Transfer Learning |
| JEL: | C45 C53 E52 Z13 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:imfswp:280939&r=big |
| By: | Rajeev K. Goel; Michael A. Nelson |
| Abstract: | The advent of the internet, and more recently of artificial intelligence (AI), has challenged academic and other institutions to ensure ethical practices and reward/promote true merit. The borderless and relatively anonymous nature of the internet creates policing challenges, leading to the abuse of established rules and standards. In the context of academia, this impacts the size and scope of resources to facilitate/check plagiarism and cheating, both from the demand and supply sides. Adding some formal insights into the current topic of fundamental importance to maintaining academic integrity, this paper examines the association of anti-plagiarism/anti-cheating policies with resources that facilitate such behavior (legal or otherwise). Using unique internet search indices of the policies and resources, we find that the two are positively associated – the associated resources ratchet up with the policies. This association is robust to different modeling formulations, including when the internet policies include course syllabi. The findings reinforce the view that policies to check plagiarism and cheating are likely to lack teeth and may be a step behind the resources that facilitate unethical behaviour. |
| Keywords: | AI, artificial intelligence, plagiarism, cheating, internet, universities, colleges, United States |
| JEL: | A20 I23 L86 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10853&r=big |
| By: | Ashish Dhiman; Yibei Hu |
| Abstract: | We apply a physics-informed deep-learning approach the PINN approach to the Black-Scholes equation for pricing American and European options. We test our approach on both simulated as well as real market data, compare it to analytical/numerical benchmarks. Our model is able to accurately capture the price behaviour on simulation data, while also exhibiting reasonable performance for market data. We also experiment with the architecture and learning process of our PINN model to provide more understanding of convergence and stability issues that impact performance. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.06711&r=big |
| By: | Mingxuan He |
| Abstract: | I study the price dynamics of non-fungible tokens (NFTs) and propose a deep learning framework for dynamic valuation of NFTs. I use data from the Ethereum blockchain and OpenSea to train a deep learning model on historical trades, market trends, and traits/rarity features of Bored Ape Yacht Club NFTs. After hyperparameter tuning, the model is able to predict the price of NFTs with high accuracy. I propose an application framework for this model using zero-knowledge machine learning (zkML) and discuss its potential use cases in the context of decentralized finance (DeFi) applications. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.05346&r=big |
| By: | Marios Vasileiou (Department of Agriculture Crop Production and Rural Environment [Volos] - UTH - University of Thessaly [Volos]); Leonidas Sotirios Kyrgiakos (Department of Agriculture Crop Production and Rural Environment [Volos] - UTH - University of Thessaly [Volos]); Christina Kleisiari (Department of Agriculture Crop Production and Rural Environment [Volos] - UTH - University of Thessaly [Volos]); Georgios Kleftodimos (CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes, UMR MoISA - Montpellier Interdisciplinary center on Sustainable Agri-food systems (Social and nutritional sciences) - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - IRD - Institut de Recherche pour le Développement - CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement); George Vlontzos (Department of Agriculture Crop Production and Rural Environment [Volos] - UTH - University of Thessaly [Volos]); Hatem Belhouchette (CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes, UMR ABSys - Agrosystèmes Biodiversifiés - Cirad - Centre de Coopération Internationale en Recherche Agronomique pour le Développement - CIHEAM-IAMM - Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier - CIHEAM - Centre International de Hautes Études Agronomiques Méditerranéennes - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement); Panos M. Pardalos (UF - University of Florida [Gainesville]) |
| Abstract: | Highlights: • AI in weed management potentials for transforming agricultural ecosystems. • AI influence in economic, social, technological, and environmental dimensions. • AI's role in enhancing food safety by reducing pesticides residues. • Digital literacy as a crucial enabler empowering stakeholders to use AI effectively. Abstract: In the face of increasing agricultural demands and environmental concerns, the effective management of weeds presents a pressing challenge in modern agriculture. Weeds not only compete with crops for resources but also pose threats to food safety and agricultural sustainability through the indiscriminate use of herbicides, which can lead to environmental contamination and herbicide-resistant weed populations. Artificial Intelligence (AI) has ushered in a paradigm shift in agriculture, particularly in the domain of weed management. AI's utilization in this domain extends beyond mere innovation, offering precise and eco-friendly solutions for the identification and control of weeds, thereby addressing critical agricultural challenges. This article aims to examine the application of AI in weed management in the context of weed detection and the increasing impact of deep learning techniques in the agricultural sector. Through an assessment of research articles, this study identifies critical factors influencing the adoption and implementation of AI in weed management. These criteria encompass factors of AI adoption (food safety, increased effectiveness, and eco-friendliness through herbicides reduction), AI implementation factors (capture technology, training datasets, AI models, and outcomes and accuracy), ancillary technologies (IoT, UAV, field robots, and herbicides), and the related impact of AI methods adoption (economic, social, technological, and environmental). Of the 5821 documents found, 99 full-text articles were assessed, and 68 were included in this study. The review highlights AI's role in enhancing food safety by reducing herbicide residues, increasing effectiveness in weed control strategies, and promoting eco-friendliness through judicious herbicide use. It underscores the importance of capture technology, training datasets, AI models, and accuracy metrics in AI implementation, emphasizing their synergy in revolutionizing weed management practices. Ancillary technologies, such as IoT, UAVs, field robots, and AI-enhanced herbicides, complement AI's capabilities, offering holistic and data-driven approaches to weed control. Additionally, the adoption of AI methods influences economic, social, technological, and environmental dimensions of agriculture. Last but not least, digital literacy emerges as a crucial enabler, empowering stakeholders to navigate AI technologies effectively and contribute to the sustainable transformation of weed management practices in agriculture. |
| Keywords: | Weed management, Artificial intelligence, Deep learning, Precision agriculture, Agroecology, Sustainability |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04297703&r=big |
| By: | Gadat, Sébastien; Doury, Antoine; Somot, Samuel |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:128959&r=big |
| By: | Miguel Calvin; Pilar Rey del Castillo |
| Abstract: | The problem of finding the factors influencing voting behavior is of crucial interest in political science and is frequently analyzed in books and articles. But there are not so many studies whose supporting information comes from official registers. This work uses official vote records in Spain matched to other files containing the values of some determinants of voting behavior at a previously unexplored level of disaggregation. The statistical relationships among the participation, the vote for parties and some socio-economic variables are analyzed by means of Gaussian Bayesian Networks. These networks, developed by the machine learning community, are built from data including only the dependencies among the variables needed to explain the data by maximizing the likelihood of the underlying probabilistic Gaussian model. The results are simple, sparse, and non-redundant graph representations encoding the complex structure of the data. The generated structure of dependencies confirms many previously studied influences, but it can also discover unreported ones such as the proportion of foreign population on all vote variables. |
| Keywords: | Bayesian networks, Gaussian distributions, voting behaviour, elections, voter turnout, political participation |
| JEL: | C46 D31 D72 D91 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10855&r=big |
| By: | Daube, Carl Heinz |
| Abstract: | The aim of this working paper is to provide a brief introduction to artificial intelligence and highlight specific potential applications in financial and investment decision-making. On the one hand, it is about where AI is already being used today in many areas of the financial industry. On the other hand, the aim is to show examples of what will be possible in the near future and where AI might lead to better, more sound decisions |
| Abstract: | Ziel dieses Working Papers ist es, eine kurze Einführung in die Künstliche Intelligenz zu geben und konkrete Einsatzmöglichkeiten in der Finanz- und Investitionsentscheidung aufzuzeigen. Dabei geht es zum einen darum, wo KI heute schon in vielen Bereichen der Finanzindustrie zum Einsatz kommt. Zum anderen geht es darum exemplarisch aufzuzeigen, was in naher Zukunft möglich sein wird und wo es auf der Basis von KI zu besseren, fundierteren Entscheidungen kommen könnte. |
| Keywords: | AI, Artificial Intelligence, investment decision, finance decision |
| JEL: | G00 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:280899&r=big |
| By: | Jackson, Emerson Abraham |
| Abstract: | This theoretical review explores the evolution of artificial intelligence (AI) and its impact on teaching and learning in the digital age. Investigating AI's integration into educational settings, the paper synthesises theoretical frameworks, empirical studies, and emerging trends. Drawing on constructivist, socio-cultural, and cognitive learning theories, the review analyses AI's implications for educational practices. It traces the historical development of AI in education, highlighting key milestones and the evolution of AI technologies. The paper adopts a theoretical framework to comprehensively analyse AI's impact, focusing on intelligent tutoring systems, adaptive learning platforms, virtual reality, natural language processing, and gamification. Theoretical foundations underscore AI's role in active learning, personalised environments, social interaction, and cognitive load management. The review addresses challenges, including equity, ethical considerations, and the evolving role of educators. It emphasises the need for clear ethical guidelines, professional development for educators, and ongoing research to navigate the evolving landscape of AI in education. Theoretical implications suggest a nuanced synthesis of technology and pedagogy, acknowledging the dynamic interplay between the two, and call for continued research to address technical challenges, ethical considerations, and effective strategies for professional development in this dynamic intersection of technology and education. |
| Abstract: | Cette revue théorique explore l'évolution de l'intelligence artificielle (IA) et son impact sur l'enseignement et l'apprentissage à l'ère numérique. En examinant l'intégration de l'IA dans les environnements éducatifs, l'article synthétise des cadres théoriques, des études empiriques et des tendances émergentes. S'appuyant sur les théories constructivistes, socio-culturelles et d'apprentissage cognitif, la revue analyse les implications de l'IA pour les pratiques éducatives. Elle retrace le développement historique de l'IA dans l'éducation, mettant en évidence des jalons clés et l'évolution des technologies d'IA. L'article adopte un cadre théorique pour analyser de manière exhaustive l'impact de l'IA, en se concentrant sur les systèmes de tutorat intelligents, les plateformes d'apprentissage adaptatif, la réalité virtuelle, le traitement du langage naturel et la ludification. Les fondements théoriques soulignent le rôle de l'IA dans l'apprentissage actif, les environnements personnalisés, l'interaction sociale et la gestion de la charge cognitive. La revue aborde des défis tels que l'équité, les considérations éthiques et le rôle en évolution des éducateurs. Elle souligne la nécessité de lignes directrices éthiques claires, de développement professionnel pour les éducateurs et de recherches continues pour naviguer dans le paysage en évolution de l'IA dans l'éducation. Les implications théoriques suggèrent une synthèse nuancée de la technologie et de la pédagogie, reconnaissant l'interaction dynamique entre les deux, et appellent à des recherches continues pour relever les défis techniques, les considérations éthiques et les stratégies efficaces de développement professionnel dans cette intersection dynamique de la technologie et de l'éducation. |
| Keywords: | Artificial Intelligence, Theoretical Review, Teaching and Learning, Digital Age |
| JEL: | A22 I21 O33 |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:280893&r=big |