Research Papers

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.

Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

As artificial intelligence (AI) systems rapidly gain autonomy, the need for robust responsible AI frameworks becomes paramount. This paper investigates how organizations perceive and adapt such frameworks amidst the emerging landscape of increasingly sophisticated agentic AI.

Employing an interpretive qualitative approach, the study explores the lived experiences of AI professionals. Findings highlight that the inherent complexity of agentic AI systems and their responsible implementation, rooted in the intricate interconnectedness of responsible AI dimensions and the thematic framework, combined with the novelty of agentic AI, contribute to significant challenges in organizational adaptation. These challenges are characterized by knowledge gaps, a limited emphasis on stakeholder engagement, and a strong focus on control. These factors ultimately compromise the potential for responsible AI and the realization of ROI.

This article analyzes how agentic artificial intelligence is revolutionizing human resource management through automated workflows, enhanced decision making, and improved employee experiences.

Agentic AI moves beyond traditional decision-support tools to autonomous decision-making in recruitment, performance evaluation, and workforce planning. The technology streamlines regulatory compliance and safety monitoring, reducing manual oversight. While AI boosts productivity, the paper emphasizes that human oversight remains essential to align with ethical standards and manage data security risks.