Objectives The purpose of this article is to critically examine the use of artificial intelligence (AI) in supporting the planning, coordination, and execution of multi-domain operations. It considers the applicability of systemic and functional approaches and qualitative methods to the analysis of AI-enabled military capabilities. Material and methods A critical review of literature, doctrinal and conceptual documents, institutional materials and selected case studies was conducted. Results The reviewed evidence indicates that AI can support operational planning, information processing, decision support and selected forms of coordination across land, air, sea, space and cyberspace. At the same time, the evidence varies in maturity: some applications are conceptual or experimental, whereas others represent more established operational capabilities. Conclusions The study employed qualitative research methods, including a critical literature review, analysis of doctrinal and conceptual documents, institutional and policy materials and selected case studies of AI-related military applications. Sources were selected purposively according to their relevance to AI-enabled planning, command, control, information processing, autonomous, unmanned systems, interoperability, cybersecurity and legal or governance questions. Priority was given to peer-reviewed academic publications, official governmental and international-organization documents, recognized research institutions and documented military or industrial cases.
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