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Adaptive cloud-based virtual reality for corporate training using bespoke machine learning

Adaptive cloud-based virtual reality for corporate training using bespoke machine learning
使用定制机器学习进行企业培训的基于云的自适应虚拟现实
批准号:
830167
负责人:
金额:
$127.42万
依托单位:
依托单位国家:
英国
项目类别:
Innovation Loans
财政年份:
2020
资助国家:
英国
项目状态:
已结题
起止时间:
2020 至 --

项目摘要

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中文摘要
翻译
与面对面学习相比,E-Learning具有显著优势,包括可伸缩性、一致性、降低成本和应用灵活性。然而,e-Learning的缺点包括缺乏用户学习轨迹的特殊性,以及通过计算机监视器提供的学习材料的局限性。OBRIZUM集团的项目旨在扩展其市场领先的电子学习平台技术,以创建一个高度复杂和独特的系统,该系统将首次在主流平台内同时利用人工智能(AI)和虚拟现实(VR)。虽然现有的e-Learning课程会对学生进行评估,然后将他们放在预定义的课程中,但新平台将使用真正的人工智能功能来做出关于学习者需求的即时决定,就像私立教师一样。这是通过使用人工智能来分析单个视频/音频文件并预测和使用高度相关的片段来实现的;将这些片段结合在一起,使学习路径更加流畅和个性化。同时,项目团队还将开发制作客户环境的360°视频内容的能力,然后将这些内容渲染到完全身临其境的虚拟现实环境中,以适应用户的实时需求(AVR)。
英文摘要
E-Learning offers significant benefits over face-to-face learning, including scalability, consistency, reduced cost, and flexibility of application. However, e-Learning suffers from drawbacks including a lack of specificity to a user's learning trajectory, and the limitations of learning materials delivered through a computer monitor. The OBRIZUM Group's project aims to extend its market-leading e-Learning platform technology to create a highly sophisticated and unique system which will, for the first time, harness Artificial Intelligence (AI) and Virtual Reality (VR) simultaneously within a mainstream platform. While available e-Learning curricula evaluates students and then puts them on a pre-defined curriculum, the new platform will use true AI capabilities to make on-the-go decisions about a learner's needs, much like a private teacher would. This is achieved by using AI to analyse individual videos/audio files and to predict and use only highly relevant segments; combining these to make a much more streamlined and personalised learning pathway. In tandem, the project team will also develop the capabilities to produce 360° video content of customers' environments, and then render this content into a fully immersive Virtual Reality environment that adapts to user needs in real-time (AVR).
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