Artificial intelligence changes what organizations can do; human leadership must decide what should be done, what remains accountable, and what must never be surrendered to machines. This working paper presents FILE: The Five Intelligences of Leadership Evolution as a human-centered management theory and diagnostic framework for responsible leadership and decision-making in the age of AI. FILE argues that the most important human skills for the AI era and the future of work require the integrated exercise of five meta-intelligences: Augmented Intelligence, Emotional Intelligence, Cultural Intelligence, Political Intelligence, and Adaptive Intelligence. The paper defines the FILE formula, maps the framework onto the human hand, presents the 70 nested intelligences of FILE, explains five emergent outcomes, and develops RC: The Relational Commons as the shared human ecosystem that leadership must protect, cultivate, and renew. It also clarifies boundaries among adjacent intelligences, offers a diagnostic application to AI-supported employee performance evaluation, and states limitations and future research directions. Its purpose is to establish a public, citable, and timestamped record of FILE as a management theory of human leadership in the age of 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
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.