AI, Big Data, and Graph Computing Take Center Stage at ACM TURC 2017

The ACM TURC 2017 conference was held in Shanghai, bringing together Turing Award laureates such as Vinton Cerf, John Hopcroft, Yao Qizhi, Chinese Academy of Sciences academician Chen Runsheng, and Peking University professor Gao Wen, along with Alibaba vice president and Alibaba Cloud chief scientist Zhou Jingren and several Alibaba technical experts.

In his invited keynote, Academician Gao Wen presented Hangzhou’s “city data brain” as China’s first AI‑driven urban governance project, illustrating how big data and AI are applied in practice, and praised Alibaba’s achievements in AI research and applications. Zhou Jingren emphasized Alibaba’s focus on coupling technology research with real‑world scenarios, highlighting ongoing work in streaming computing, graph computing, and large‑scale machine learning. He noted breakthroughs in streaming fault tolerance and large‑scale graph computing deployments in search recommendation, anti‑fraud, and knowledge‑graph systems, and outlined future efforts to integrate graph computing with machine learning using online user behavior.

During the special forum, Alibaba iDST researcher Hua Xiansheng shared an AI‑based medical imaging diagnosis method that learns from massive datasets to automatically locate and classify lesions in CT, X‑ray, MRI, and cell‑slide images, generating diagnostic reports and significantly improving early‑cancer detection accuracy. Algorithm expert Tang Haihong described large‑scale machine‑learning models that combine learning and decision components to evolve search capabilities, enhancing response speed and precision. Alibaba technology experts also engaged in in‑depth discussions on network security and cloud databases with the attending scholars.

Previously disclosed, Alibaba iDST head Jin Rong was invited to serve as an executive director of the ACM China Council. Alibaba is actively establishing collaborative platforms with leading universities in graph computing and large‑scale machine learning to jointly accelerate research progress in these fields.