Research Pillars
- AI for Science
- Artificial General Intelligence
- Resilient & Safe AI
- Sustainable AI
- Theory and Optimisation in AI
AI for Science
Despite significant advancements in scientific research and development in the past centuries, the exploration of new scientific frontiers is still a time-consuming and resource-intensive undertaking. The typical process of scientific discovery often involves multiple iterations of empirical data collection and rigorous analysis, taking years or even decades to unveil meaningful insights. Moreover, the escalating volume and complexity of data produced by modern experimental means presents further challenges for researchers to discern patterns and extract insightful conclusions from their findings. These challenges suggest the need for new tools and approaches to accelerate the scientific process.
AI for Science represents a transformative leap in how we approach and solve complex scientific challenges. AI-driven understanding and modelling of natural phenomena across multiple discipline could enable faster, more efficient, and more intricate scientific discoveries, thereby revolutionising scientific research into areas like drug and materials design, protein 3D structure prediction and knowledge discovery.
Fig 1. AI for Science can be applied to scenarios of multiple scales ranging from microscopic level (e.g. small chemical molecules and larger macromolecules like proteins) to larger-scale systems (e.g. urban environment and human mobility), across both physical and biomedical sciences.
Research Focus
While AI holds immense potential, multiple challenges remain.
Fig 2. Challenges of AI include limited availability of data (potentially due to high annotation costs), complex, non-linear relationships, prevalence of multimodal data, and need for consistency with natural laws.
To address the above limitations, we focus on the development of generalisable generative AI models and optimisation algorithms with the following key characteristics:
- Interpretability and controllability in generation (including with human preferences)
- Consistency with domain expertise, including adherence to physical laws (e.g. governing partial differential equations) and physical constraints
- Cross-modal capabilities in models to maximise use of different kinds of available data
Applications
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