Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
This book develops a systematic framework for Artificial Intelligence General Education (AIGE) in response to the cognitive and social transformations of an intelligent society. It identifies cognitive autonomy—the capacity of individuals to actively regulate belief formation, evidence use, trust allocation, the boundaries of judgment and responsibility for action in complex cognitive environments—as a central task of modern general education. Drawing on the scientific foundations of artificial intelligence and the computational transformation of intelligent and social activities, the book explains how AIGE can cultivate four interconnected capabilities: induction, generalization, judgment and self-awareness. It further presents a structured content framework, educational designs spanning primary school through higher education and lifelong learning, and corresponding principles for teaching and assessment. Intended for educators, researchers, policymakers and the wider public, the book provides both a conceptual foundation and a practical framework for advancing Artificial Intelligence General Education in the age of artificial intelligence. NOTE: This is an AI-assisted English translation of Chinese version 2.4. The Chinese edition is authoritative in cases of semantic discrepancy.
This paper investigates developing real-world perception for active agents, proposes Gibson Environment for this purpose, and showcases a set of perceptual tasks learned therein.
F. Xia, Amir Zamir, Zhi-Yang He et al.· 2018 IEEE/CVF Conference on...· 1,069 citations· ⚡112
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
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An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.