Generative AI can help scale climate services to meet growing demand. This approach is illustrated here with two prototype AI systems designed to enhance access to climate data, support decision-making, and improve efficiency. Key concerns are identified around responsibility, quality, and trustworthiness of AI outputs. Emerging best practices in system development are linked to core principles of salience, credibility, and legitimacy, to ensure alignment with user needs and societal values.
The adoption of generative artificial intelligence among communication practitioners and researchers surged after the launch of ChatGPT in November 2022, urging practitioners to critically engage in exploring pathways for fostering socially responsible and environmentally sustainable AI practices.
Clinical nurses' GenAI learning needs are currently oriented toward practical, application-focused skills, and curriculum development may benefit from a phased approach that prioritizes high-impact practical skills while progressively incorporating foundational, ethical, and advanced competencies.
Yeru Xia, Jingbang Liu, Kaili Wang et al.· Nurse Education Today· 1 citation· ⚡1
It is argued that both AI and bullshitters are untrustworthy informants, and for similar reasons, it is natural to describe AI’s informational outputs as bullshit, as it signals their distinctive kind of epistemic deficiencies, which they share with bullshit.
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.