Page 19: 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.

Computer Sciences

A new 'blueprint' for advancing practical, trustworthy AI

A new "blueprint" for building AI that highlights how the technology can learn from different kinds of data—beyond vision and language—to make it more deployable in the real world, has been developed by researchers at the ...

Business

How to make 'smart city' technologies behave ethically

As local governments adopt new technologies that automate many aspects of city services, there is an increased likelihood of tension between the ethics and expectations of citizens and the behavior of these "smart city" tools. ...

Consumer & Gadgets

AI models often fail to identify ableism across cultures

The artificial intelligence models underlying popular chatbots and content moderation systems struggle to identify offensive, ableist social media posts in English—and perform even worse in Hindi, new Cornell research finds.

Machine learning & AI

Multimodal AI learns to weigh text and images more evenly

Just as human eyes tend to focus on pictures before reading accompanying text, multimodal artificial intelligence (AI)—which processes multiple types of sensory data at once—also tends to depend more heavily on certain types ...

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