Older adults and technology haven’t always had the smoothest relationship. From learning to use email to operating smartphones, each new wave has brought fresh challenges and frustrations. Now, mastering QR codes is the latest hurdle.

The Hong Kong Polytechnic University (PolyU) and CLP Power Hong Kong Limited (CLP Power) have jointly developed a smart “Generator Inspection Robot”. By integrating robotics with fibre-optic sensing technology for generator inspections, the project overcomes traditional manual inspection limitations, significantly enhancing the efficiency of inspections and operational safety. The project won a Gold Medal and a special prize—Best International Invention & Innovation, from the National Research Council of Thailand—at the 51st International Exhibition of Inventions Geneva. This international recognition testifies to the achievements of PolyU and CLP Power in fostering industry-academia-research collaboration, injecting momentum into Hong Kong’s development in innovative technology and research talent.

New research examines Finnish lower secondary special needs math instruction via a survey of teachers, focusing on topics taught, preparedness, and instructional practices. Teachers report high confidence overall, but gaps remain in data processing, statistics, and probability. While guided practice and feedback are common, high-impact strategies such as mastery learning, scaffolding, peer tutoring, and structured sequences are used inconsistently. Findings reveal a disconnect between preparation and classroom practice, highlighting the need for targeted training and support.

Target identification is a critical and challenging step in drug discovery, with only a small fraction of human genes considered druggable and even fewer successfully targeted by approved therapies. Traditionally, this process has been slow and complex, but artificial intelligence (AI) is transforming it into a more systematic, data-driven approach. 

A recent review by Insilico Medicine highlights how AI is accelerating target discovery by integrating vast multimodal datasets—including omics data, clinical records, imaging, and scientific literature—to uncover novel disease mechanisms and therapeutic targets. 

Advanced machine learning methods, such as supervised and unsupervised learning, graph neural networks, and generative AI models, enable researchers to prioritize targets, simulate biological systems, and generate new hypotheses with greater precision. Platforms like PandaOmics and emerging “virtual biologist” AI agents further enhance this capability by synthesizing complex biological knowledge. 

Clinical progress demonstrates the real-world impact of these approaches, with AI-enabled platforms accelerating timelines and contributing to promising therapies, such as the TNIK inhibitor rentosertib for idiopathic pulmonary fibrosis. 

Looking ahead, the field is moving toward AI-driven closed-loop systems that integrate computational predictions with automated experimentation, aiming to significantly improve efficiency, success rates, and the delivery of new treatments to patients.