Embodied artificial intelligence introduces distinctive privacy risks through data collection modalities such as continuous sensing and imperceptible capture in physical spaces. The static data-compliance framework anchored in notice and consent proves inadequate in addressing these risks. Drawing on the theory of contextual integrity, this article examines China's legislative, judicial, and regulatory practices in privacy protection for embodied AI, and identifies two systemic deficiencies: the lack of context-specific rules and weak contextual adaptability. It then proposes a governance paradigm shift from data compliance to contextual compliance, which entails context-specific legislation, a full-lifecycle regulatory architecture spanning ex ante, intra-process, and ex post stages, refined technical standards for context-based risk classification, and strengthened international cooperation, thereby establishing a differentiated and dynamic privacy protection mechanism. The study aims to provide an institutional framework that reconciles technological innovation with the protection of personality rights, while offering insights from Chinese practice for the global governance of embodied AI.
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...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
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