388,926 research outputs found
Artificial Intelligence in Civil Infrastructure Health Monitoring—historical Perspectives, Current Trends, and Future Visions
Over the past 2 decades, the use of artificial intelligence (AI) has exponentially increased toward complete automation of structural inspection and assessment tasks. This trend will continue to rise in image processing as unmanned aerial systems (UAS) and the internet of things (IoT) markets are expected to expand at a compound annual growth rate of 57.5% and 26%, respectively, from 2021 to 2028. This paper aims to catalog the milestone development work, summarize the current research trends, and envision a few future research directions in the innovative application of AI in civil infrastructure health monitoring. A blow-by-blow account of the major technology progression in this research field is provided in a chronological order. Detailed applications, key contributions, and performance measures of each milestone publication are presented. Representative technologies are detailed to demonstrate current research trends. A road map for future research is outlined to address contemporary issues such as explainable and physics-informed AI. This paper will provide readers with a lucid memoir of the historical progress, a good sense of the current trends, and a clear vision for future research
Towards artificial intelligence : advances, challenges, and risks
This text contains some reflections on artificial intelligence (AI). First, we distinguish between strong and weak AI, as well as the concepts related to general and specific AI. Following this, we briefly describe the main current AI models and discuss the need to provide common-sense knowledge to machines in order to advance towards the goal of a general AI. Next, we talk about the current trends in AI based on the analysis of large amounts of data, which has recently allowed experts to make spectacular progress. Finally, we discuss other topics which, now and in the future, will continue to be key in AI, before closing with a brief reflection on the risks of AI
Multi-agent systems in industry: current trends & future challenges
This paper introduces the multi-agent systems paradigm and presents some industrial applications of this AI approach, namely in manufacturing, handling and logistics domains. The road-blockers for the current weak adoption of this technology in industry are also discussed, and finally the current trends and several future challenges are pointed out to increase the wider dissemination and acceptance of the multi-agent technology in industry
AI (r)evolution -- where are we heading? Thoughts about the future of music and sound technologies in the era of deep learning
Artificial Intelligence (AI) technologies such as deep learning are evolving
very quickly bringing many changes to our everyday lives. To explore the future
impact and potential of AI in the field of music and sound technologies a
doctoral day was held between Queen Mary University of London (QMUL, UK) and
Sciences et Technologies de la Musique et du Son (STMS, France). Prompt
questions about current trends in AI and music were generated by academics from
QMUL and STMS. Students from the two institutions then debated these questions.
This report presents a summary of the student debates on the topics of: Data,
Impact, and the Environment; Responsible Innovation and Creative Practice;
Creativity and Bias; and From Tools to the Singularity. The students represent
the future generation of AI and music researchers. The academics represent the
incumbent establishment. The student debates reported here capture visions,
dreams, concerns, uncertainties, and contentious issues for the future of AI
and music as the establishment is rightfully challenged by the next generation
Promotional Flyer: The History and Future of Artificial Intelligence
Text on flyer: Hosted by Ruth Wolfish (IEEE), this session will help you discover current literature and trends in AI — its history, development and forecast for the future. It will include tips on how to find an internship or position in AI and how to make a good impression in your interview, and you’ll have a chance to chat with UD’s IEEE Xplore representative. This event is open to the whole campus. We will have free pizza, giveaways and IEEE T-shirts
Advancing mental health care with AI-enabled precision psychiatry tools: A patent review
The review provides an overview of patents on AI-enabled precision psychiatry tools published between 2015 and mid-October 2022. Multiple analytic approaches, such as graphic network analysis and topic modeling, are used to analyze the scope, content, and trends of the retained patents. The included tools aim to provide accurate diagnoses according to established psychometric criteria, predict the response to specific treatment approaches, suggest optimal treatments, and make prognoses regarding disorder courses without intervention. About one-third of the tools recommend treatment options or include treatment administration related to digital therapeutics, pharmacotherapy, and electrotherapy. Data sources used to make predictions include behavioral data collected through mobile devices, neuroimaging, and electronic health records. The complexity of technology combinations used in the included devices has increased until 2021. The topics extracted from the patent data illuminate current trends and potential future developments in AI-enabled precision psychiatry. The most impactful patents and associated available products reveal relevant commercialization possibilities and likely future developments. Overall, the review highlights the potential of adopting AI-enabled precision psychiatry tools in practice
Towards An Artificial Intelligence Maturity Model: From Science Fiction To Business Facts
Artificial intelligence (AI) has become increasingly prevalent in organisations in different sectors. The rapid development of AI technology has rendered it essential to understand strategies for its implementation. Despite current trends, the uncertainties in the process required to establish strong AI capabilities are the major concerns for high level management. Therefore, this research-in-progress aims to understand AI practices in organisations through the development of an organisation-level AI maturity model (AIMM). However, to the best of our knowledge, no fully developed and theoretically derived AI maturity model currently exists. This research, therefore, represents an early attempt to develop an AI maturity model at the level of organisations; the results of this research will provide organisations with insights into the successful evolution and adoption of AI and can be used as a theoretical foundation for future research
Human-Centered Responsible Artificial Intelligence: Current & Future Trends
In recent years, the CHI community has seen significant growth in research on
Human-Centered Responsible Artificial Intelligence. While different research
communities may use different terminology to discuss similar topics, all of
this work is ultimately aimed at developing AI that benefits humanity while
being grounded in human rights and ethics, and reducing the potential harms of
AI. In this special interest group, we aim to bring together researchers from
academia and industry interested in these topics to map current and future
research trends to advance this important area of research by fostering
collaboration and sharing ideas.Comment: To appear in Extended Abstracts of the 2023 CHI Conference on Human
Factors in Computing System
The Role of Medical Image Modalities and AI in the Early Detection, Diagnosis and Grading of Retinal Diseases: A Survey.
Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends
Recent advances in artificial intelligence (AI) have significantly
intensified research in the geoscience and remote sensing (RS) field. AI
algorithms, especially deep learning-based ones, have been developed and
applied widely to RS data analysis. The successful application of AI covers
almost all aspects of Earth observation (EO) missions, from low-level vision
tasks like super-resolution, denoising and inpainting, to high-level vision
tasks like scene classification, object detection and semantic segmentation.
While AI techniques enable researchers to observe and understand the Earth more
accurately, the vulnerability and uncertainty of AI models deserve further
attention, considering that many geoscience and RS tasks are highly
safety-critical. This paper reviews the current development of AI security in
the geoscience and RS field, covering the following five important aspects:
adversarial attack, backdoor attack, federated learning, uncertainty and
explainability. Moreover, the potential opportunities and trends are discussed
to provide insights for future research. To the best of the authors' knowledge,
this paper is the first attempt to provide a systematic review of AI
security-related research in the geoscience and RS community. Available code
and datasets are also listed in the paper to move this vibrant field of
research forward
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