Researchers have developed "MetaRL," a meta reinforcement learning model that provides near-optimal, individualized investment strategies for goals-based wealth management, i.e., the portfolio composition and the decision to take a goal or not. Pre-trained on thousands of scenarios, MetaRL achieves 97.8% of optimal utility while operating over 100 times faster than traditional methods. The model delivers immediate "zero-shot" solutions in milliseconds and remains robust against changing market regimes and stochastic inflation, enabling high-throughput, sophisticated financial planning for a broad range of investors.

NUS CDE student Ryan Loh along with Alfred Ben built LearnKata to help students learn with more structure, clarity and confidence, leading to 40,000 users worldwide.

Traditional computing architectures struggle to support the complex, real-time demands of modern autonomous robots. A team of researchers has published a comprehensive review in SmartBot, detailing how neuromorphic electronic devices that mimic the biological nervous system can provide the necessary hardware foundation for the next generation of "embodied intelligence".