An international research team involving the University of Bayreuth has, for the first time, analyzed the "inner workings" of AI language models when predicting political voting decisions. To do so, the researchers examined six national elections and AI-based election forecasts and developed a new method for making more precise predictions. They presented their findings at the International Conference on Machine Learning (ICML 2026) in Seoul, South Korea.

Artificial intelligence isn't just capable of translating between existing languages—it can also create entirely new ones.

The era of building "personalized AI" by training AI models on individual or corporate documents and data is beginning. However, while such customization can improve task performance, it can also weaken a model's existing safety safeguards. KAIST researchers have developed a core AI technology that preserves customized performance while further strengthening safety.