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

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的系统性弱点，主要是电子邮件和贸易文件驱动的工作流程。疫情加剧了现有的低效率和缺乏审查，财富500强企业每年花费810亿美元的不必要供应链成本（摩根大通2017年贸易展望）。虽然世界经济论坛建议的数字化创新提供了改善迫切需要的业务弹性的补救措施，但有效采用仍然存在挑战。物流行业采用人工智能的速度非常缓慢，目前只有12%的组织利用人工智能（MHI Industry report，2020）。人工智能应用不足的一个主要原因是，物流行业的信息不断变化，非标准化和复杂化，这对算法的可扩展性提出了巨大的挑战。该项目旨在有意义地加强我们现有的物流机器学习工作流程自动化平台内置的自动算法训练管道的研究和开发。无代码管道在后台运行，自动重新训练我们的数据提取算法，以适应客户不断变化的电子邮件和文档内容。该项目的研究将深入而具体地探索提取技术在多语言和边缘案例数据样本上的学术应用，以加强我们的基本训练管道，从而提高适用性，通过扩展易用性和效率鼓励快速采用。

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.