Advanced Algorithm Training on European Languages and Edge-Cases in Logistics to Drive Widespread AI Adoption to Build Pandemic Resilience

关于欧洲语言和物流边缘案例的高级算法培训，以推动人工智能的广泛采用，以建立流行病抵御能力

• 批准号: 103139
• 金额: $ 18.83万
• 依托单位: VECTOR AI LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=103139
• 关键词: Advanced; Algorithm; Training; European; Languages

The World Economic Forum (WEF) highlighted that COVID-19 exposed systemic weakness in logistics' physical and manual data-processing operations, primarily email and trade-document driven workflows. The pandemic compounds existing inefficiencies and lack of scrutiny, costing Fortune 500 companies $81 billion of unnecessary supply chain costs each year (JPMorgan 2017 Trade Outlook).Whilst the WEF recommendation of digitisation innovations offer remedies to improve desperately needed business resiliency, there are challenges to effective adoption. The logistics industry has been notably slow to adopt AI, only 12% of organisations currently leverage AI (MHI Industry report,2020). One major reason for the lack of AI adoption is that the logistics sector has ever-changing, non-standard and complex information, which poses massive algorithm scalability challenges.This project aims to meaningfully enhance our research and development of an automated algorithm-training pipeline in-built into our existing logistics' machine learning workflow automation platform. The no-code pipeline operates in the background, automatically re-training our data-extraction algorithms to customers' evolving email and document content. This project's research will thoroughly and specifically explore academic application of extraction techniques on multi-lingual and edge-case data-samples to bolster our basic training pipeline, thus increasing applicability, encouraging rapid adoption via scaling ease and efficiency.

世界经济论坛 (WEF) 强调，COVID-19 暴露了物流物理和手动数据处理操作（主要是电子邮件和贸易文件驱动的工作流程）的系统性弱点。这种流行病加剧了现有的低效率和缺乏审查，每年给财富 500 强公司造成 810 亿美元的不必要的供应链成本（摩根大通 2017 年贸易展望）。虽然世界经济论坛关于数字化创新的建议为提高迫切需要的业务弹性提供了补救措施，但仍面临挑战有效采用。物流行业采用人工智能的速度明显缓慢，目前只有 12% 的组织利用人工智能（三菱重工行业报告，2020 年）。人工智能缺乏采用的一个主要原因是物流领域的信息不断变化、非标准和复杂，这给算法可扩展性带来了巨大的挑战。该项目旨在有意义地加强我们对自动化算法训练管道的研发内置到我们现有的物流机器学习工作流程自动化平台中。无代码管道在后台运行，根据客户不断变化的电子邮件和文档内容自动重新训练我们的数据提取算法。该项目的研究将彻底而具体地探索提取技术在多语言和边缘情况数据样本上的学术应用，以加强我们的基础培训管道，从而提高适用性，通过扩展的简便性和效率鼓励快速采用。