Related papers: From Cloud to Edge: Rethinking Generative AI for L…
AI for supporting designers needs to be rethought. It should aim to cooperate, not automate, by supporting and leveraging the creativity and problem-solving of designers. The challenge for such AI is how to infer designers' goals and then…
Increasing rate of progress in hardware and artificial intelligence (AI) solutions is enabling a range of software systems to be deployed closer to their users, increasing application of edge software system paradigms. Edge systems support…
This study looks at how generative artificial intelligence (AI) can revolutionize marketing, product development, and research. It discusses the latest developments in the field, easy-to-use resources, and moral and social hazards. In…
The traditional role of the network layer is to create an end-to-end route, through which the intermediate nodes replicate and forward the packets towards the destination. This role can be radically redefined by exploiting the power of…
As Generative AI (GenAI) capabilities expand, understanding how to preserve and develop human expertise while leveraging AI's benefits becomes increasingly critical. Through empirical studies in two contexts -- survey article authoring in…
Artificial intelligence (AI) technologies, and particularly deep learning systems, are traditionally the domain of large-scale cloud servers, which have access to high computational and energy resources. Nonetheless, in Internet-of-Things…
The advent of edge devices dedicated to machine learning tasks enabled the execution of AI-based applications that efficiently process and classify the data acquired by the resource-constrained devices populating the Internet of Things. The…
Generative AI tools are becoming more prevalent in 3D modeling, enabling users to manipulate or create new models with text or images as inputs. This makes it easier for users to rapidly customize and iterate on their 3D designs and explore…
Generative Artificial Intelligence (GenAI) is transforming how firms create, process, and apply knowledge, yet little is known about the heterogeneity of its productivity effects across users. We report results from a randomized controlled…
Modern cloud-native systems increasingly rely on multi-cluster deployments to support scalability, resilience, and geographic distribution. However, existing resource management approaches remain largely reactive and cluster-centric,…
As designers become familiar with Generative AI, a new concept is emerging: Agentic AI. While generative AI produces output in response to prompts, agentic AI systems promise to perform mundane tasks autonomously, potentially freeing…
The integration of Generative Artificial Intelligence (AI) into autonomous machines represents a major paradigm shift in how these systems operate and unlocks new solutions to problems once deemed intractable. Although generative AI agents…
Generative AI (GenAI) provides new opportunities for creativity support, but the phenomenon of GenAI design fixation remains underexplored. While human design fixation typically constrains ideas to familiar or existing solutions, our…
Next-generation (xG) wireless networks, with their complex and dynamic nature, present significant challenges to using traditional optimization techniques. Generative AI (GAI) emerges as a powerful tool due to its unique strengths. Unlike…
Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-augmented generation (RAG) provides a potential solution,…
Cloud computing is an increasingly popular computing paradigm, now proving a necessity for utility computing services. Each provider offers a unique service portfolio with a range of resource configurations. Resource provisioning for cloud…
Future AI applications require performance, reliability and privacy that the existing, cloud-dependant system architectures cannot provide. In this article, we study orchestration in the device-edge-cloud continuum, and focus on edge AI for…
High throughput experimentation tools, machine learning (ML) methods, and open material databases are radically changing the way new materials are discovered. From the experimentally driven approach in the past, we are moving quickly…
Edge computing is the practice of placing computing resources at the edges of the Internet in close proximity to devices and information sources. This, much like a cache on a CPU, increases bandwidth and reduces latency for applications but…
Generative artificial intelligence (Generative AI) is transforming healthcare. With this evolution comes optimism regarding the impact it will have on mental health, as well as concern regarding the risks that come with generative AI…