HKU School of Future Media to host AI & Filmmaking Week 2026
Business Announcement
This month, we’re focusing on artificial intelligence (AI), a topic that continues to capture attention everywhere. Here, you’ll find the latest research news, insights, and discoveries shaping how AI is being developed and used across the world.
Updates every hour. Last Updated: 12-May-2026 00:15 ET (12-May-2026 04:15 GMT/UTC)
Chinese scientists proposed an anti-interference diffractive deep neural network (AI D2NN) designed for object recognition in multi-object scenarios. Unlike conventional optical neural networks built for single-object classification, AI D2NN achieves robust target recognition while effectively suppressing interference from other objects. By incorporating optical multi-dimensional multiplexing, the system enables flexible, high-capacity parallel recognition of multiple targets. This approach is expected to accelerate the practical deployment of optical neural networks in autonomous driving, medical diagnostics, and security monitoring.
Optical neural networks (ONNs) offer a pathway toward low-latency, energy-efficient artificial intelligence (AI); however, their scalability in terms of parameter count remains constrained. Addressing this challenge, a research team from The Chinese University of Hong Kong has developed a metasurface-based optical learning machine that integrates 41 million parameters, achieving performance comparable to state-of-the-art AI models. This approach experimentally enables highly scalable machine vision, thereby offering a practical pathway toward large-scale, high-performance optical AI computing.
A research team from the Shenyang Institute of Automation, Chinese Academy of Sciences, together with Peking University and collaborating institutions, proposes the Embodied Context Protocol (ECP). Centered on semantic interfaces and declarative workflows, ECP is an interface protocol for orchestrating embodied systems, connecting simulation platforms, data acquisition, model training, and inference execution into a reusable and auditable workflow.
For the first time, an international research team has harnessed artificial intelligence (AI) to decode the rules of an ancient board game, pioneering a new way to reveal long-lost historical secrets.Flinders University computer scientist, Dr Matthew Stephenson, says that using modern AI techniques can bridge the gap between historical and computational studies of games.
The promise of AI lies not in making machines smarter in isolation, but in making human–AI collaboration work better. That alignment, not raw intelligence, is what turns AI from a source of frustration into a source of value.