Artificial Intelligence (AI) is rapidly advancing—and with it, the promise of solving some of academia's toughest problems. But while many researchers focus on using AI to find answers, Associate Professor Hiroshi Kera of the Institute for Advanced Academic Research/Graduate School of Informatics is asking a different question: How can we design better problems for AI to solve?

A research team led by Professor Daehee Park of the Department of Electrical Engineering and Computer Science at DGIST, in collaboration with a research team from KAIST, has developed a learning technique that enables a single compact AI model to simultaneously predict the movements of nearby people and plan safe navigation paths for robots while reducing performance degradation in both tasks. The research was presented at the 19th European Conference on Computer Vision (ECCV 2026) held in Malmö, Sweden, Sept. 8–12. The paper is available on the arXiv preprint server.

A new approach could help make future AI chips smaller and more energy-efficient. A KAIST-led research team has used a single material to address one of the major obstacles facing atomically thin semiconductors: the difficulty of efficiently injecting charge. The technology could contribute to next-generation AI and low-power semiconductor devices in which multiple ultrathin layers are vertically integrated to increase device density and performance.

A machine-learning system can identify fake, or spoofed, website addresses, according to research published in the International Journal of Electronic Security and Digital Forensics. The work offers a new tool to protect users against phishing attacks, in which attackers direct someone to a deceptive website to steal information such as bank logins or personal and private data.

The Canadian computer scientist Yoshua Bengio, considered one of the founders of AI, told AFP that humanity is "losing control" of the technology and that the world needs guardrails similar to nuclear arms controls.