AI-Based Reconstruction from Inherited Personal Data: Analysis, Feasibility, and Prospects

• URL: http://arxiv.org/abs/2507.03059v1
• Date: Thu, 03 Jul 2025 16:19:15 GMT
• Title: AI-Based Reconstruction from Inherited Personal Data: Analysis, Feasibility, and Prospects
• Authors: Mark Zilberman,
• Abstract summary: This article explores the feasibility of creating an "electronic copy" of a deceased researcher by training artificial intelligence (AI) on the data stored in their personal computers.<n>By analyzing typical data volumes on inherited researcher computers, it is estimated that approximately one million words are available for AI training.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This article explores the feasibility of creating an "electronic copy" of a deceased researcher by training artificial intelligence (AI) on the data stored in their personal computers. By analyzing typical data volumes on inherited researcher computers, including textual files such as articles, emails, and drafts, it is estimated that approximately one million words are available for AI training. This volume is sufficient for fine-tuning advanced pre-trained models like GPT-4 to replicate a researcher's writing style, domain expertise, and rhetorical voice with high fidelity. The study also discusses the potential enhancements from including non-textual data and file metadata to enrich the AI's representation of the researcher. Extensions of the concept include communication between living researchers and their electronic copies, collaboration among individual electronic copies, as well as the creation and interconnection of organizational electronic copies to optimize information access and strategic decision-making. Ethical considerations such as ownership and security of these electronic copies are highlighted as critical for responsible implementation. The findings suggest promising opportunities for AI-driven preservation and augmentation of intellectual legacy.

Related papers

• Evidencing Unauthorized Training Data from AI Generated Content using Information Isotopes [0.0]
  In a rush to stay competitive, some institutions may inadvertently or even deliberately include unauthorized data for AI training.<n>We introduce the concept of information isotopes and elucidate their properties in tracing training data within opaque AI systems.<n>We propose an information isotope tracing method designed to identify and provide evidence of unauthorized data usage.
  arXiv  Detail & Related papers  (2025-03-24T07:35:59Z)
• The impact of AI and peer feedback on research writing skills: a study using the CGScholar platform among Kazakhstani scholars [0.0]
  This research studies the impact of AI and peer feedback on the academic writing development of Kazakhstani scholars using the CGScholar platform.<n>The study aimed to find out how familiarity with AI tools and peer feedback processes impacts participants' openness to incorporating feedback into their academic writing.
  arXiv  Detail & Related papers  (2025-03-05T04:34:25Z)
• CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers [3.929864777332447]
  CS-PaperSum is a large-scale dataset of 91,919 papers from 31 top-tier computer science conferences.<n>Our dataset enables automated literature analysis, research trend forecasting, and AI-driven scientific discovery.
  arXiv  Detail & Related papers  (2025-02-27T22:48:35Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Data Shapley in One Training Run [88.59484417202454]
  Data Shapley provides a principled framework for attributing data's contribution within machine learning contexts.<n>Existing approaches require re-training models on different data subsets, which is computationally intensive.<n>This paper introduces In-Run Data Shapley, which addresses these limitations by offering scalable data attribution for a target model of interest.
  arXiv  Detail & Related papers  (2024-06-16T17:09:24Z)
• Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing [74.58071278710896]
  generative AI has attracted much attention from both academic and industrial fields. Secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement.
  arXiv  Detail & Related papers  (2024-05-17T04:00:58Z)
• Quantitative Analysis of AI-Generated Texts in Academic Research: A Study of AI Presence in Arxiv Submissions using AI Detection Tool [0.0]
  This study will analyze a method that can see purposely manufactured content that academic organizations use to post on Arxiv. The statistical analysis shows that Originality.ai is very accurate, with a rate of 98%.
  arXiv  Detail & Related papers  (2024-02-09T17:20:48Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• On the Evolution of A.I. and Machine Learning: Towards a Meta-level Measuring and Understanding Impact, Influence, and Leadership at Premier A.I. Conferences [0.26999000177990923]
  We present measures allowing the analyses of AI and machine learning researchers' impact, influence, and leadership over the last decades. We look at papers published at the flagship AI and machine learning conferences since the first International Joint Conference on Artificial Intelligence (IJCAI) held in 1969.
  arXiv  Detail & Related papers  (2022-05-26T03:41:12Z)
• DeepShovel: An Online Collaborative Platform for Data Extraction in Geoscience Literature with AI Assistance [48.55345030503826]
  Geoscientists need to read a huge amount of literature to locate, extract, and aggregate relevant results and data. DeepShovel is a publicly-available AI-assisted data extraction system to support their needs. A follow-up user evaluation with 14 researchers suggested DeepShovel improved users' efficiency of data extraction for building scientific databases.
  arXiv  Detail & Related papers  (2022-02-21T12:18:08Z)
• Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and Stir" [76.44130385507894]
  This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices. Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
  arXiv  Detail & Related papers  (2021-11-01T17:57:04Z)
• Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
  The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal. In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
  arXiv  Detail & Related papers  (2021-01-19T12:23:32Z)

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.