Sep 2026· Journal of economics, finance and management studies· 0 citations
Abstract
This paper investigates if companies in India's information technology (IT) services sector and global capability centres that have adopted artificial intelligence (AI) grow at a faster pace when compared to those that have not adopted such technologies and explores the implications of this transformation for labour demand.The analysis uses the data collected through the financial reports of firms, labor market information, and public reports and It adopts graphical and comparative analysis to study the dynamics of AI adoption, revenue expansion, job creation, and recruitment. The results reveal that companies characterized by a greater degree of AI adoption have much better revenue indicators than those characterized by a lower degree of AI adoption, thus confirming the assumption that AI makes companies more productive and efficient and improves their competitive position. At the industry level, however, there is a situation where revenue expansion is much faster than job creation, which indicates weakening relationship between output growth and labor demand. Though net hiring slows down, total job roles in the industry still grow which suggests that AI does not eliminate jobs, but restructures labor demand since the trend now indicates that routine tasks are becoming less and less common. The study concludes that AI is shifting India’s technology sector from labour-cost driven growth to innovation and productivity driven expansion, making reskilling and workforce adaptability essential for inclusive and sustainable growth.
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
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.