Professor Anthony Tung, Head of AI for Urban Sustainability at NUS AI Institute
As businesses around the world continue to explore artificial intelligence (AI) solutions, organizations often face several barriers to successfully adopting AI technologies.
Professor Anthony Tung, Head of AI for Urban Sustainability to the NUS AI Institute in Singapore, and a faculty member, Department of Computer Science, NUS School of Computing in an exclusive interview with iTNews Asia said these challenges stem from factors such as a lack of understanding of AI, limited talent, data constraints and insufficient IT resources.
“Many companies do not fully understand what AI can or cannot achieve, leading to a lack of motivation to explore its potential,” Professor Tung said. “They also struggle to find qualified talent, as AI expertise is in high demand across all industries.”
In addition to limited staff, many companies face a “small data” problem. Instead of having clean, labeled datasets to train AI models, they often collect data only for operational needs, which is not ideal for AI and analytics.
“Good data management is a prerequisite for AI adoption. Without it, even the most advanced AI models will struggle to operate effectively,” explained Professor Tung.
He also highlighted the financial burden of acquiring the computing resources needed to train and maintain AI models.
Ongoing AI Projects at NUS
Professor Tung said the National University of Singapore (NUS) Singapore has made significant progress in AI research and applications.
Apache SINGA, an open source distributed deep learning system, is one of the scalable AI systems for large-scale data analysis.
The other initiative, RETINA, a collaboration between NUS and National Healthcare Group Polyclinics, improves screening of retinal images for medical conditions. The project has already deployed a prototype in several health centers, he added.
NUS also helps organizations assess their AI readiness with the AI Maturity Model, which tracks progress from initial awareness to fully integrated AI systems.
When it comes to adopting AI, Professor Tung highlighted the importance of having a comprehensive AI roadmap integrating business objectives, data quality, infrastructure readiness and workforce development. “AI projects should not be seen as stand-alone initiatives but should be integrated into the broader company strategy,” he said.
For companies looking to deploy AI solutions, he recommended preparing the IT infrastructure. Organizations should evaluate their current hardware and software capabilities, investing in scalable cloud solutions and high-performance computing resources. This will ensure that AI workloads can be managed effectively, he added.
Professor Tung said the NUS team is currently working on creating “white box AI”, which aims to make AI systems more transparent, controllable, verifiable, resilient and antifragile.
Looking ahead, he believes the next wave of AI innovation will focus on reliability. “As AI systems become increasingly integrated into our daily lives, transparency, controllability and resilience will be essential to ensure their safe and effective use.”
With continued investment in AI research, partnerships with industry leaders, and a strong ethical framework, he hopes that AI can be trusted to have a transformative and positive impact.
