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16,543 papers
The study finds that the technical leader must be prepared to constantly learn, adapt, and understand the technological and human factors to be effective in the AI era.
This study delves into the notion of AI agentic architectures for autonomous data engineering pipelines and investigates the potential benefits of intelligent agents in enhancing automation, resilience, and decision-making processes in contemporary data ecosystems.
The paper provides a comprehensive analytical tool to make sense of micro and macro evidence, unpacks scenarios when AI agents will drive inclusive productivity growth, and outlines a policy roadmap focused on complementary investments, incentives for task-redesign, and workforce transition support measures.
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It is concluded that future research should prioritize interdisciplinary collaboration, robust regulatory frameworks, and continuous monitoring to promote the ethical use of AI.
AI-driven cybersecurity in the software engineering field is discussed, where machine learning, deep learning, natural language processing, and reinforcement learning can be applied throughout the software development lifecycle to provide increased security.
A new scientific object – the Autonomous Recovery Efficiency Score (ARES) – is introduced – a quantitative measure of autonomous resilience, as well as a supporting foundation for future autonomous self-healing AI agentic infrastructure.
The speed of cloud computing and artificial intelligence, which have transformed the way software applications are designed and deployed. The cloud-based AI systems provide a scalable, adaptable, and cost-efficient solution to build intelligent systems capable of processing large amounts of data and running complicated...
This study offers new means to design a resilient multi-agent system that is able to resist emergent threats by formalizing trust as a dynamic security vector and introduces an Agentic Trust Dynamics Theory as a conceptual theory to model the evolution, loss, and impact of trust on emerging security vulnerabilities in...
Artificial intelligence is usually analysed as a productivity technology, a labour-market shock, or a governance problem. This article argues that its deeper political significance may arise from a fourth channel: ontological displacement. Ontological displacement occurs when a technology weakens the socially recognise...
A person-centred framework of hermeneutic sovereignty for generative AI is developed and concludes that trustworthy AI must preserve not only a human role in decisions, but a meaningful human standing in the production of the interpretations on which decisions depend.
The emergence of artificial intelligence as a creative force has fundamentally challenged traditional conceptions of authorship, creativity, and intellectual property ownership. This analysis presents six novel theoretical frameworks that transcend current academic discourse on AI art ownership, drawing from extensive...
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.
Announcing Gemini 4 Argon, our frontier model for real-world coding, enterprise knowledge work and cyber defense, rolling out soon.
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.
Machine Intelligence
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