On the Preservation of Africa's Cultural Heritage in the Age of Artificial Intelligence

• URL: http://arxiv.org/abs/2403.06865v2
• Date: Wed, 13 Mar 2024 15:44:23 GMT
• Title: On the Preservation of Africa's Cultural Heritage in the Age of Artificial Intelligence
• Authors: Mohamed El Louadi
• Abstract summary: The paper traces the stages of knowledge dissemination from oral traditions to the digital era, highlighting the significance of languages and cultural diversity in this progression. It also explores the impact of digital technologies on memory, communication, and cultural preservation, emphasizing the need for promoting a culture of the digital (rather than a digital culture) in Africa and beyond.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we delve into the historical evolution of data as a fundamental element in communication and knowledge transmission. The paper traces the stages of knowledge dissemination from oral traditions to the digital era, highlighting the significance of languages and cultural diversity in this progression. It also explores the impact of digital technologies on memory, communication, and cultural preservation, emphasizing the need for promoting a culture of the digital (rather than a digital culture) in Africa and beyond. Additionally, it discusses the challenges and opportunities presented by data biases in AI development, underscoring the importance of creating diverse datasets for equitable representation. We advocate for investing in data as a crucial raw material for fostering digital literacy, economic development, and, above all, cultural preservation in the digital age.

Related papers

• Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts [65.90535970515266]
  TimeTravel is a benchmark of 10,250 expert-verified samples spanning 266 distinct cultures across 10 major historical regions. TimeTravel is designed for AI-driven analysis of manuscripts, artworks, inscriptions, and archaeological discoveries. We evaluate contemporary AI models on TimeTravel, highlighting their strengths and identifying areas for improvement.
  arXiv  Detail & Related papers  (2025-02-20T18:59:51Z)
• CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries [63.00147630084146]
  Vision-language models (VLMs) have advanced human-AI interaction but struggle with cultural understanding. CultureVerse is a large-scale multimodal benchmark covering 19, 682 cultural concepts, 188 countries/regions, 15 cultural concepts, and 3 question types. We propose CultureVLM, a series of VLMs fine-tuned on our dataset to achieve significant performance improvement in cultural understanding.
  arXiv  Detail & Related papers  (2025-01-02T14:42:37Z)
• Leveraging Retrieval-Augmented Generation for Culturally Inclusive Hakka Chatbots: Design Insights and User Perceptions [4.388667614435888]
  This study introduces a groundbreaking approach to promoting and safeguarding the rich heritage of Taiwanese Hakka culture. By integrating external databases with generative AI models, RAG technology bridges this gap. This is particularly significant in an age where digital platforms often dilute cultural identities.
  arXiv  Detail & Related papers  (2024-10-21T01:36:08Z)
• Extrinsic Evaluation of Cultural Competence in Large Language Models [53.626808086522985]
  We focus on extrinsic evaluation of cultural competence in two text generation tasks. We evaluate model outputs when an explicit cue of culture, specifically nationality, is perturbed in the prompts. We find weak correlations between text similarity of outputs for different countries and the cultural values of these countries.
  arXiv  Detail & Related papers  (2024-06-17T14:03:27Z)
• From ChatGPT, DALL-E 3 to Sora: How has Generative AI Changed Digital Humanities Research and Services? [5.3743115255502545]
  This article profoundly explores the application of large-scale language models in digital humanities research. The article first outlines the importance of ancient book resources and the necessity of digital preservation. Through specific cases, the article demonstrates how AI can assist in the organization, classification, and content generation of ancient books.
  arXiv  Detail & Related papers  (2024-04-29T09:03:19Z)
• Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking [48.21982147529661]
  This paper introduces a novel approach for massively multicultural knowledge acquisition. Our method strategically navigates from densely informative Wikipedia documents on cultural topics to an extensive network of linked pages. Our work marks an important step towards deeper understanding and bridging the gaps of cultural disparities in AI.
  arXiv  Detail & Related papers  (2024-02-14T18:16:54Z)
• Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features [19.72091739119933]
  Our study delves into the intersection of cultural features and transfer learning effectiveness. Based on these results, we advocate for the integration of cultural information into datasets. Our research signifies a step forward in the quest for more inclusive, culturally sensitive language technologies.
  arXiv  Detail & Related papers  (2023-10-10T09:29:38Z)
• Investigating Participation Mechanisms in EU Code Week [68.8204255655161]
  Digital competence (DC) is a broad set of skills, attitudes, and knowledge for confident, critical and use of digital technologies. The aim of the manuscript is to offer a detailed and comprehensive statistical description of Code Week's participation in the EU Member States.
  arXiv  Detail & Related papers  (2022-05-29T19:16:03Z)
• Populating the Digital Space for Cultural Heritage with Heritage Digital Twins [0.0]
  The present paper concerns the design of the semantic infrastructure of the digital space for cultural heritage. The concept is based on the Digital Twin, i.e. the digital counterpart of cultural heritage assets all incorporating digital information to them.
  arXiv  Detail & Related papers  (2022-05-26T07:49:27Z)
• Learning Robust Real-Time Cultural Transmission without Human Data [82.05222093231566]
  We provide a method for generating zero-shot, high recall cultural transmission in artificially intelligent agents. Our agents succeed at real-time cultural transmission from humans in novel contexts without using any pre-collected human data. This paves the way for cultural evolution as an algorithm for developing artificial general intelligence.
  arXiv  Detail & Related papers  (2022-03-01T19:32:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.