将预测分析带入 Agentic AI 时代
英文标题：Bringing predictive analytics to the agentic AI era
深度学习和生成式 AI 等智能分析技术，正推动预测系统从事后回顾转向更具实践性的前瞻决策。实时训练使模型能够持续演进，而非等待按季度更新；新一代预测引擎也开始利用非结构化交互数据等更广泛的信息来源。随着 AI 覆盖预测建模、数据准备、分析流程、结果解读和决策应用，预测分析正成为企业 Agentic AI 能力的重要基础。对开发者和企业而言，关键挑战已从提升预测准确性，转向让系统能够基于预测自主行动，同时始终符合业务意图。
57.2 KB
193.3 KB
70.5 KB
中文
由深度学习和生成式 AI 等技术驱动的智能分析，正在让这一目标成为可能。实时训练使 AI 能够持续演进，而不必等待按季度进行更新。此外，较新的预测引擎所依赖的数据范围已经扩大，不再局限于规整的数值记录，也涵盖了杂乱、非结构化但富含洞见的交互信息来源。因此，AI 驱动的分析正在推动企业从被动的事后回顾，转向务实的前瞻预测。
AI 正将预测分析——这一涵盖预测建模、数据准备、分析工作流、结果解读及决策应用的广泛学科——提升到新的高度。Gupta 表示：“在许多方面，我认为‘分析’这个词正在被 AI 取代。一切都正在成为 AI。”
本内容由《MIT Technology Review》的定制内容部门 Insights 制作，而非由其编辑团队制作。内容由人工调研和撰写；如使用任何 AI 工具，其用途仅限于在人类监督下的生产流程。
English / 原文
Intelligent analytics, powered by technologies like deep learning and generative AI, are making this possible. Real-time training allows AI to evolve continuously instead of waiting for quarterly refreshes. In addition, the data that newer predictive engines rely upon has expanded to encompass not just neat, numerical records but also messy, unstructured sources of insight-rich interactions. As a result, AI-powered analytics are moving enterprises from passive hindsight to pragmatic foresight.
AI takes predictive analytics—a broad discipline that includes predictive modeling, data prep, analysis workflows, interpretation of results, and decision-making applications—to new heights. “In many ways I think the word ‘analytics’ is giving way to AI,” says Gupta. “Everything is becoming AI.”
This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.