New research could empower people without AI expertise to help create trustworthy AI applications
Involving people without AI expertise in the development and evaluation of artificial intelligence applications could help create better, fairer, and more trustworthy automated decision-making systems, new research suggests. After enlisting members of the public to evaluate the potential impacts of two real-world applications, researchers from UK universities will present a paper at a major international computing conference which suggests how "participatory AI auditing" could improve AI decision-making in the future.
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I’ve recently gone through several materials, including Antonio Gulli’s AI Agentic Design Patterns, Sam Bhagwat’s Principles of Building AI Agents and Patterns for Building AI Agents, as well as the courses from LangGraph Academy and some content on DataCamp. This space is evolving very quickly, so I’m curious how others here are approaching learning. What resources, courses, papers, or hands-on approaches have you found most useful while building AI agents? Comments URL: https://news.ycombinator.com/item?id=47637083 Points: 2 # Comments: 3
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Multi-fidelity approaches for general constrained Bayesian optimization with application to aircraft design
Aircraft design relies heavily on solving challenging and computationally expensive Multidisciplinary Design Optimization problems. In this context, there has been growing interest in multi-fidelity models for Bayesian optimization to improve the MDO process by balancing computational cost and accuracy through the combination of high- and low-fidelity simulation models, enabling efficient exploration of the design process at a minimal computational effort. In the existing literature, fidelity selection focuses only on the objective function to decide how to integrate multiple fidelity levels, — Oihan Cordelier, Youssef Diouane, Nathalie Bartoli
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