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2,518 papers
This workshop aims to explore the topic from a human-centered perspective of the XR+AI combination from a human-centered perspective to build a network for the area and foster future collaborations.
Artificial intelligence (AI) is increasingly embedded in daily life, offering convenient support in many tasks like inspiration for text production or answering everyday questions. However, its risks often remain less visible, ranging from data security concerns to more subtle effects like growing dependence, negative...
Object detection, a task, in the field of computer vision faces obstacles when dealing with weather conditions such as fog, rain, snow, and low light situations. This paper provides an overview of advancements in the realm of object detection under challenging weather conditions. It delves into groundbreaking research...
Cloud robotics is an innovative field that leverages cloud technologies-including cloud computing (CC), cloud storage, deep learning, big data, and the Internet of Things to augment the capabilities of robotics. This integration facilitates the execution of robotic functions through a converged infrastructure and share...
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There's no denying that Artificial Intelligence (AI), Machine Learning (ML), and Big Data technologies are profoundly changing the face of software engineering and organizational leadership. As these technologies keep evolving, the design, deployment, and management of software systems are undergoing unprecedented chan...
An integrated conceptual framework is presented which maps layers of the MAS architecture to decision postures in the enterprise, a cross domain performance synthesis, and a research agenda for the next generation of enterprise-scale autonomous agent systems are presented.
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.
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.
Intent-Based Security (IBS), a structured approach built on foundational ideas from access control, zero-trust models, and principal-agent dynamics, shows why trusting identities fails against invisible threats.
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.
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
As director, Bourg has focused on digital access, open and equitable scholarly publishing, and expanded support for data-intensive research.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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