In a feature interview with Croucher News, Prof. Yang Hongxia, Executive Director of the PolyU Academy for Artificial Intelligence, Associate Dean (Global Engagement) of the Faculty of Computer and Mathematical Sciences, and Professor at the Department of Computing, discussed her Co-GenAI project. The initiative is designed to democratise generative AI by significantly reducing GenAI development barriers and enabling broader participation in the AI era.
Challenging the Centralised AI Paradigm
Professor Yang describes the current race to build increasingly large AI models as a "rich people's game," exclusively pursued by a handful of well-funded companies. Her research at The Hong Kong Polytechnic University challenges this paradigm. She advocates for a future where multiple stakeholders collaborate to develop high-quality AI, comparing today's centralised labs to the era of mainframe computers before the rise of the personal device.
Key Technological Advances: Co-GenAI and Model Fusion
Her proposed solution, Collaborative Generative AI (Co-GenAI), introduces practical innovations that significantly reduce development barriers:
Advanced Model Fusion: Her team successfully fused four top-tier reasoning models using only around 160 GPU hours, a fraction of the 1-2 million GPU hours typically required to train a similar model from scratch. The resulting model achieved state-of-the-art performance, with average success rates in the mid-80% range across 11 challenging reasoning domains.
Theoretical Breakthrough: Professor Yang's team is the first to theoretically derive the "Model Merging Scaling Law." This pivotal finding suggests that decentralised, collaborative approaches are not just practical but are also a feasible pathway toward more advanced AI systems, offering a viable alternative to pure centralisation.
Real-World Impact: Empowering Healthcare and Beyond
The research has immediate applications in specialised fields such as medicine. Co-GenAI enables hospitals to train AI models on private, high-quality data without ever sharing raw
data externally. Multiple local models can then be fused to create a stronger, more knowledgeable foundation model.
Enhanced Privacy, Accuracy & Efficiency: This method ensures patient data remains completely private while reducing inaccuracies common in general-purpose models. Running models locally provides millisecond-speed responses, a critical improvement over cloud-based systems for time-sensitive decisions in clinical settings.
Professor Yang emphasizes that the goal is to support, not to replace, human experts: "The final decision-maker is still the doctor."
A Call for Collaborative and Responsible AI Development
Looking ahead, Professor Yang envisions building comprehensive "science foundation models" by integrating contributions from leading domain experts worldwide. She remains optimistic about AI's potential to revolutionize industries while advocating for smart regulation that promotes responsible use without stifling technology.
Read the full feature article on the Croucher Foundation website: Making powerful generative AI cheaper and more collaborative via https://croucher.org.hk/en/news/making-powerful-generative-ai-cheaper-and-more-collaborative
| Topics | PAAI Media Coverage |
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