Page 14: Research news on AI alignment

AI alignment examines how artificial systems acquire, represent, and act on goals, values, and social norms, and why their behavior often diverges from human expectations. Work in this area studies systematic failures such as bias, sycophancy, hallucinations, deceptive or selfish reasoning, and cultural or linguistic inequities, as well as limitations in commonsense, emotion, and social understanding. It also develops methods for preference learning, norm-following, interpretability, and reliability guarantees to better align AI behavior with human values and societal constraints.

Consumer & Gadgets

Apertus: A fully open, transparent, multilingual language model

In July, EPFL, ETH Zurich, and CSCS announced their joint initiative to build a large language model (LLM). Now, this model is available and serves as a building block for developers and organizations for future applications ...

Machine learning & AI

Amplifying AI's impact by making it understandable

As AI becomes a ubiquitous part of everyday life, people increasingly understand how it works. Whereas traditional computer programs operate on clear logic—"If A happens, then do B"—AI models, especially neural networks, ...

Security

Fairness tool catches AI bias early

Machine learning software helps agencies make important decisions, such as who gets a bank loan or what areas police should patrol. But if these systems have biases, even small ones, they can cause real harm. A specific group ...

page 14 from 28