A review published in Smart Construction examines how reinforcement learning (RL) could help optimize steel structures by learning design strategies that may be reused across related structures. Focusing on section selection in steel moment-resisting and braced frames, the review compares RL-based approaches with traditional and surrogate-assisted methods and emphasizes that practical use will require robust code-compliance assurance, manageable computational cost, validated cross-structure generalization, and independent engineering verification.

Computed tomography (CT) is widely used in medical diagnosis and image-guided treatment, but high-density metallic objects such as dental fillings, orthopedic implants, and vascular stents can cause beam hardening and photon starvation, producing severe streak artifacts that obscure surrounding anatomy. Existing methods often restore corrupted projections through interpolation or deep neural networks, but discontinuities between estimated and measured data may introduce secondary artifacts. Supervised approaches also rely on paired metal-corrupted and metal-free images, which are difficult to obtain clinically and may generalize poorly across different metal shapes, anatomical regions, and scanning protocols.

Patients increasingly turn to artificial intelligence for health information. But how reliable are the answers? A new study from Dalhousie University, published in UroPrecision, provides the first systematic evaluation of ChatGPT‑4.0 against Canadian Urological Association (CUA) guidelines.

For the first time, researchers have demonstrated that the “ability to forget” can serve as a new computational function for artificial intelligence.

- KAIST and Germany’s Max Planck Institute analyzed 110 million Naver News comments spanning 20 years (2006–2025), identifying approximately 24,000 accounts suspected of foreign-linked influence operations

- Analysis found that the suspected accounts attacked both progressive and conservative camps rather than favoring a particular side, revealing activity patterns aimed at amplifying conflict and polarization within Korean society

- The “explainable AI” shows which phrases in comments and account behaviors informed its judgments, offering potential use in rapidly detecting suspicious public-opinion interference during elections and major national issues and supporting expert review