Alibaba Sets Up AI Research Facility With Singapore University

Alibaba Group and Singapore's Nanyang Technological University (NTU) have inked a five-year agreement to jointly develop artificial intelligence (AI) applications through a new research institute.

The facility would look to tap NTU's own AI technology, which had focused on areas such as healthcare, ageing, homes, and communities, and integrate these with Alibaba's technologies including natural language processing (NLP), machine learning, and cloud computing.

The research institute would begin operating with a team of 50 researchers from both organisations, and marked the first time Alibaba had set up a research facility with a partner outside of China.

The collaboration would run for an initial five-year period and worth "multimillion dollar per year", according to Alibaba. The two partners would look to apply AI to improve efficiencies and daily lives in various environments such as home, retail, community, urban transportation, and hospitals.

NTU's president Subra Suresh said: ""Using AI technologies, we can address fundamental societal challenges such as aging population which is a huge issue for cities with a rapidly aging population such as Singapore. For example, virtual AI assistants can be deployed to improve work productivity and smart sensors can be used to watch the health of the elderly, with data stored on the cloud for continuous monitoring."

Suresh added that such AI and cloud platforms could be created and tested on NTU's campus to showcase the applications.

Students and staff members from the university as well as Alibaba employees would have opportunities to go on exchange programmes to the other's facilities.

Alibaba last month launched its smart city AI platform in Kuala Lumpur, Malaysia, marking the Chinese vendor's first such implementation outside its domestic market. The platform was touted to be able to analyse large data volumes extracted from various sources in an urban environment, through video, image, and speech recognition. The system then used machine learning to provide insights for city administrators to improve operational efficiencies and monitor security risks.