Although deep learning–based image recognition technology is rapidly advancing, it still remains difficult to clearly explain the criteria AI uses internally to observe and judge images. In particular, technologies that analyze how large-scale models combine various concepts (e.g., cat ears, car wheels) to reach a conclusion have long been recognized as a major unsolved challenge.

Artificial intelligence can write award-winning essays and diagnose disease with remarkable accuracy, but biological brains still excel at something AI struggles with: flexibility. Snapping together “cognitive Legos” — reusable patterns of brain activity — lets primates combine and recombine these blocks across related tasks, quickly and efficiently assembling new behaviors. This gives monkeys — and possibly people as well — an advantage when learning new tasks in ways current AI systems cannot match.

Image reconstruction—the process of recovering clear images from incomplete or noisy data—has been advancing rapidly through deep learning. Yet most existing approaches rely on costly supervised training and lack theoretical transparency. A new survey maps the rise of unsupervised deep learning for image reconstruction, from traditional denoising-based priors to modern diffusion models. These methods learn structured visual information directly from unlabeled data, and have achieved impressive performance across various fields, including biomedical imaging and remote sensing. The study shows how unsupervised learning based image reconstruction unites neural network efficiency with solid mathematical foundations to achieve both interpretability and flexibility, offering a blueprint for next-generation imaging systems.

Researchers at the University of Melbourne have developed a new AI-based traffic signal control system called M²SAC that improves both fairness and efficiency at urban intersections. Unlike traditional systems focused only on cars, M²SAC accounts for pedestrians, buses, and other users. A key innovation is the phase mask mechanism, which dynamically adjusts green light timings to reduce delays. Tested on real Melbourne traffic data, the model outperformed existing methods, cutting congestion and balancing traffic flow more equitably. The approach supports smarter, fairer, and more inclusive transport systems for modern cities.