Hasil untuk "artificial intelligence"

Huimin Lu, Yujie Li, Min Chen et al.

Artificial intelligence (AI) is an important technology that supports daily social life and economic activities. It contributes greatly to the sustainable growth of Japan’s economy and solves various social problems. In recent years, AI has attracted attention as a key for growth in developed countries such as Europe and the United States and developing countries such as China and India. The attention has been focused mainly on developing new artificial intelligence information communication technology (ICT) and robot technology (RT). Although recently developed AI technology certainly excels in extracting certain patterns, there are many limitations. Most ICT models are overly dependent on big data, lack a self-idea function, and are complicated. In this paper, rather than merely developing next-generation artificial intelligence technology, we aim to develop a new concept of general-purpose intelligence cognition technology called “Beyond AI”. Specifically, we plan to develop an intelligent learning model called “Brain Intelligence (BI)” that generates new ideas about events without having experienced them by using artificial life with an imagine function. We will also conduct demonstrations of the developed BI intelligence learning model on automatic driving, precision medical care, and industrial robots.

Zoubin Ghahramani

C. Krittanawong, Hongju Zhang, Zhen Wang et al.

D. I. Patrício, Rafael Rieder

Abstract Grain production plays an important role in the global economy. In this sense, the demand for efficient and safe methods of food production is increasing. Information Technology is one of the tools to that end. Among the available tools, we highlight computer vision solutions combined with artificial intelligence algorithms that achieved important results in the detection of patterns in images. In this context, this work presents a systematic review that aims to identify the applicability of computer vision in precision agriculture for the production of the five most produced grains in the world: maize, rice, wheat, soybean, and barley. In this sense, we present 25 papers selected in the last five years with different approaches to treat aspects related to disease detection, grain quality, and phenotyping. From the results of the systematic review, it is possible to identify great opportunities, such as the exploitation of GPU (Graphics Processing Unit) and advanced artificial intelligence techniques, such as DBN (Deep Belief Networks) in the construction of robust methods of computer vision applied to precision agriculture.

Kipp W. Johnson, Jessica Torres Soto, Benjamin S. Glicksberg et al.

Artificial intelligence and machine learning are poised to influence nearly every aspect of the human condition, and cardiology is not an exception to this trend. This paper provides a guide for clinicians on relevant aspects of artificial intelligence and machine learning, reviews selected applications of these methods in cardiology to date, and identifies how cardiovascular medicine could incorporate artificial intelligence in the future. In particular, the paper first reviews predictive modeling concepts relevant to cardiology such as feature selection and frequent pitfalls such as improper dichotomization. Second, it discusses common algorithms used in supervised learning and reviews selected applications in cardiology and related disciplines. Third, it describes the advent of deep learning and related methods collectively called unsupervised learning, provides contextual examples both in general medicine and in cardiovascular medicine, and then explains how these methods could be applied to enable precision cardiology and improve patient outcomes.

S. Park, Kyunghwa Han

Jay Lee, Hossein Davari, Jaskaran Singh et al.

Abstract The recent White House report on Artificial Intelligence (AI) (Lee, 2016) highlights the significance of AI and the necessity of a clear roadmap and strategic investment in this area. As AI emerges from science fiction to become the frontier of world-changing technologies, there is an urgent need for systematic development and implementation of AI to see its real impact in the next generation of industrial systems, namely Industry 4.0. Within the 5C architecture previously proposed in Lee et al. (2015), this paper provides an insight into the current state of AI technologies and the eco-system required to harness the power of AI in industrial applications.

Niladri B. Syam, Arun Sharma

J. Howard

Artificial intelligence (AI) is a broad transdisciplinary field with roots in logic, statistics, cognitive psychology, decision theory, neuroscience, linguistics, cybernetics, and computer engineering. The modern field of AI began at a small summer workshop at Dartmouth College in 1956. Since then, AI applications made possible by machine learning (ML), an AI subdiscipline, include Internet searches, e-commerce sites, goods and services recommender systems, image and speech recognition, sensor technologies, robotic devices, and cognitive decision support systems (DSSs). As more applications are integrated into everyday life, AI is predicted to have a globally transformative influence on economic and social structures similar to the effect that other general-purpose technologies, such as steam engines, railroads, electricity, electronics, and the Internet, have had. Novel AI applications in the workplace of the future raise important issues for occupational safety and health. This commentary reviews the origins of AI, use of ML methods, and emerging AI applications embedded in physical objects like sensor technologies, robotic devices, or operationalized in intelligent DSSs. Selected implications on the future of work arising from the use of AI applications, including job displacement from automation and management of human-machine interactions, are also reviewed. Engaging in strategic foresight about AI workplace applications will shift occupational research and practice from a reactive posture to a proactive one. Understanding the possibilities and challenges of AI for the future of work will help mitigate the unfavorable effects of AI on worker safety, health, and well-being.

Shilin Qiu, Qihe Liu, Shijie Zhou et al.

In recent years, artificial intelligence technologies have been widely used in computer vision, natural language processing, automatic driving, and other fields. However, artificial intelligence systems are vulnerable to adversarial attacks, which limit the applications of artificial intelligence (AI) technologies in key security fields. Therefore, improving the robustness of AI systems against adversarial attacks has played an increasingly important role in the further development of AI. This paper aims to comprehensively summarize the latest research progress on adversarial attack and defense technologies in deep learning. According to the target model’s different stages where the adversarial attack occurred, this paper expounds the adversarial attack methods in the training stage and testing stage respectively. Then, we sort out the applications of adversarial attack technologies in computer vision, natural language processing, cyberspace security, and the physical world. Finally, we describe the existing adversarial defense methods respectively in three main categories, i.e., modifying data, modifying models and using auxiliary tools.

Heike Felzmann, E. F. Villaronga, C. Lutz et al.

Transparency is now a fundamental principle for data processing under the General Data Protection Regulation. We explore what this requirement entails for artificial intelligence and automated decision-making systems. We address the topic of transparency in artificial intelligence by integrating legal, social, and ethical aspects. We first investigate the ratio legis of the transparency requirement in the General Data Protection Regulation and its ethical underpinnings, showing its focus on the provision of information and explanation. We then discuss the pitfalls with respect to this requirement by focusing on the significance of contextual and performative factors in the implementation of transparency. We show that human–computer interaction and human-robot interaction literature do not provide clear results with respect to the benefits of transparency for users of artificial intelligence technologies due to the impact of a wide range of contextual factors, including performative aspects. We conclude by integrating the information- and explanation-based approach to transparency with the critical contextual approach, proposing that transparency as required by the General Data Protection Regulation in itself may be insufficient to achieve the positive goals associated with transparency. Instead, we propose to understand transparency relationally, where information provision is conceptualized as communication between technology providers and users, and where assessments of trustworthiness based on contextual factors mediate the value of transparency communications. This relational concept of transparency points to future research directions for the study of transparency in artificial intelligence systems and should be taken into account in policymaking.

J. Górriz, J. Ramírez, A. Ortiz et al.

Abstract Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.

E. Neri, F. Coppola, V. Miele et al.

Hannah Hargrove, Bei Tong, Amr Hussein Elkabanny et al.

In the field of antiviral peptide (AVP) design, one of the most prominent limiting factors is the time and material cost required to perform the initial screening of novel AVPs. In particular, traditional target identification as well as traditional preclinical screening of novel drug candidates can be a very lengthy and expensive process. In recent decades, target identification and initial screening of AVPs has been increasingly carried out using machine learning (ML). The use of ML to initially screen potential interactions reduces the financial cost and lengthy time scale of preclinical AVP development, allowing for candidate peptides to be identified and screened faster, at a lower cost to both manufacturer and consumer. Additionally, the use of ML in generating and screening AVP candidates allows a more diverse chemical space to be explored than high-throughput screening methodologies allow. In silico generation and validation of AVP candidates also limits researcher contact with high BSL-rated viruses, thereby increasing the safety and accessibility of AVP design. This review seeks to provide a broad overview of the current uses of ML in early-stage AVP design, and to shed some light on the future direction of the field.

Yanyan Zhang, Xin Wan, Suping Yi et al.

With the rapid integration of Generative AI in education, understanding students' ethical perspectives is crucial for effective AI ethics education. Five hundred and ninety four middle school students' agreement levels on five AI ethical principles (beneficence, non-maleficence, justice, autonomy, explicability) adapted from previous research, and the rationales underlying their choices were investigated using a questionnaire. Results showed that students expressed the highest agreement with “beneficence” and “autonomy,” though overall responses leaned toward neutrality. Independent AI use and family discussions predicted higher agreement; urban-rural differences were non-significant. Qualitative analysis identified themes in students' ethical reasoning. These findings offer evidence-based guidance for adolescent AI ethics education.

Shyon Parsa, Sulaiman Somani, Albert J. Rogers et al.

Background Atrial fibrillation (AF) is the most common arrhythmia worldwide, and patient perceptions significantly influence shared treatment decisions. Artificial intelligence–driven analysis of social media may offer valuable insights into contemporary public attitudes toward AF outside clinical settings. Methods This qualitative study used large language modeling and advanced artificial intelligence topic modeling techniques to analyze public perceptions of AF from Reddit discussions between April 2006 and November 2023. Results We curated 86 323 AF‐related conversations (18 754 posts, 67 569 comments) across 38 183 unique users by searching terms related to AF. Our topic modeling identified 65 distinct discussion topics organized into 9 thematic groups, with topics including personal experiences with treatments (eg, ablation, rate versus rhythm control), roles of health care providers and community support, AF triggers (diet, illicit substances, supplements, stress, caffeine), and anecdotes highlighting the difficulties of living with AF. Discussions commonly reflected 3 main themes: (1) advantages and limitations of wearable devices for AF monitoring, (2) hesitancy and misconceptions about AF treatment, and (3) patient‐centered challenges following an AF diagnosis. Conclusions The artificial intelligence–enabled analysis underscored substantial public discourse around patient experiences with AF detection and management. Leveraging social media data to understand patient perspectives on cardiovascular health may inform patient‐centered resources and future research directions to better support patients living with AF.

Nitay Alon, Joseph M. Barnby, Reuth Mirsky et al.

This volume includes a selection of papers presented at the 2nd Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2026 in Singapore on 26th January 2026. The purpose of this volume is to provide an open access and curated anthology for the ToM and AI research community.

F. Lopez‐Jimenez, Z. Attia, Adelaide M. Arruda-Olson et al.

Artificial intelligence (AI) is a nontechnical, popular term that refers to machine learning of various types but most often to deep neural networks. Cardiology is at the forefront of AI in medicine. For this review, we searched PubMed and MEDLINE databases with no date restriction using search terms related to AI and cardiology. Articles were selected for inclusion on the basis of relevance. We highlight the major achievements in recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take center stage in the field. Artificial intelligence requires a close collaboration among computer scientists, clinical investigators, clinicians, and other users in order to identify the most relevant problems to be solved. Best practices in the generation and implementation of AI include the selection of ideal data sources, taking into account common challenges during the interpretation, validation, and generalizability of findings, and addressing safety and ethical concerns before final implementation. The future of AI in cardiology and in medicine in general is bright as the collaboration between investigators and clinicians continues to excel.

Prof. K. R. Chowdhary

R. Perez‐Vega, V. Kaartemo, Cristiana Raquel Lages et al.

Abstract As new applications of artificial intelligence continue to emerge, there is an increasing interest to explore how this type of technology can improve automated service interactions between the firm and its customers. This paper aims to develop a conceptual framework that details how firms and customers can enhance the outcomes of firm-solicited and firm-unsolicited online customer engagement behaviors through the use of information processing systems enabled by artificial intelligence. By building on the metaphor of artificial intelligence systems as organisms and taking a Stimulus-Organism-Response theory perspective, this paper identifies different types of firm-solicited and firm-unsolicited online customer engagement behaviors that act as stimuli for artificial intelligence organisms to process customer-related information resulting in both artificial intelligence and human responses which, in turn, shape the contexts of future online customer engagement behaviors.