This article investigates the structural links between historical text canonization through religious language policies and modern algorithmic governance. Utilizing the theoretical framework of religiolinguistics, it indicates that modern Large Language Models (LLMs) perform a functionally similar cultural-political gatekeeper role to manual sacred text compilations, translations, and canonizations. While scriptural codification historically stabilized linguistic standards to achieve political integration, automated data infrastructures replicate these patterns of control, driving linguistic and political homogenization. Situated in the current proto-commercial "Golden Age of AI," this paper presents the Canada Bible anthology project—a multilingual and interfaith digital humanities initiative inspired by Marshall McLuhan's media theory. To counter algorithmic compression, the Canada Bible operationalizes intersectional data curation through programmatic "Typographic Equity Rules," strict Indigenous Data Sovereignty protocols, and algorithmic de-escalation, including "Talmudic-style" documented debate. By deploying practice-driven epistemic benchmarking (e.g. the RISE Humanities Data Benchmark), this project bridges open-source digital infrastructure with a physical book, the cover layout of which resolves linguistic friction via a single visual ideogram. Ultimately, this project offers a scalable blueprint for leveraging digital humanities methodologies to maximize social impact and advance linguistic pluralism in Canada and globally. Keywords: Digital Humanities, Artificial Intelligence, Religiolinguistics, Large Language Models, Indigenous Data Sovereignty, Canonization, Language Standardization, Multilingualism, Sacred Texts, Social Impact, Canada Bible Project.
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.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17