作为信任机器的区块链:生成式人工智能时代从幻灭到启迪

IF 6.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Shaokun Fan , Noyan Ilk , Akhil Kumar , Ruiyun Xu , J. Leon Zhao
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引用次数: 0

摘要

自 2015 年《经济学人》杂志将区块链誉为 "信任机器 "以来,区块链范式经历了波峰和波谷,包括最近的幻灭阶段,原因是它未能满足人们对其的高度期望,例如,未能彻底改变其早期历史所设想的记录保存、数据管理和工作流程。不过,尽管有些人对这项技术的兴趣在减弱,但在去中心化金融(DeFi)、不可兑换代币(NFT)等领域以及加密货币以外的其他应用领域,这项技术的部署却变得越来越重要。尤其是最近围绕大型语言模型(LLM)的人工智能(AI)技术的进步,为信任和可靠性变得至关重要的区块链应用提供了新的机遇。随着区块链技术从幻灭阶段过渡到启蒙阶段,人们开始期待其主流应用,并集中精力消除各种商业环境中的应用障碍,本期《区块链技术与应用》特刊中的研究就是一例。在本文中,我们首先对区块链技术的现状进行了调查,然后重点介绍了区块链技术在增强人工智能生成内容(AIGC)等新兴现象的信任度和问责制方面的潜力。最后,我们将介绍本特刊收录的论文。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Blockchain as a trust machine: From disillusionment to enlightenment in the era of generative AI

Since the Economist magazine heralded blockchain as “the trust machine” in 2015, the blockchain paradigm has experienced crests and falls, including a recent phase of disillusionment due to its failure to meet the high expectations, e.g., to revolutionize record keeping, data management, and workflow, envisioned during its early history. However, despite the waning interest in this technology in some quarters, its deployment has become ever more essential in areas such as decentralized finance (DeFi), Non-fungible Tokens (NFTs), and other application domains beyond cryptocurrencies. In particular, recent advancements in Artificial Intelligence (AI) surrounding Large Language Models (LLM) offer new opportunities for blockchain adoption where trust and reliability become critical. As the blockchain technology transitions from a stage of disillusionment to one of enlightenment, anticipation is building for its mainstream adoption, with focused endeavors towards removing adoption barriers across diverse business contexts, exemplified by studies included in this special issue on Blockchain Technology and Applications. In this paper, we first survey the current state of the blockchain technology and then highlight its potential for enhancing trust and accountability in emerging phenomena such as AI generated content (AIGC). We conclude by introducing the papers included in the special issue.

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来源期刊
Decision Support Systems
Decision Support Systems 工程技术-计算机:人工智能
CiteScore
14.70
自引率
6.70%
发文量
119
审稿时长
13 months
期刊介绍: The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).
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