Optimizing Aesthetic Perception through Human-AI Teaming for Subtle Dimension Identification in Art Annotation

Image aesthetics assessment (IAA) has attracted growing interest in recent years but is still challenging due to its highly abstract nature. Nowadays, more and more people tend to comment images shared on the social networks, which can provide rich aesthetics-aware semantic information from different aspects. Therefore, user comments of an image can be exploited as supplementary information for enhancing aesthetic representation learning. Previous researches have demonstrated that aesthetic attributes make significant effect on image aesthetic quality and humans’ aesthetic perception. Typically, people are used to give comments on an image from the perspective of aesthetic attributes, based on which the aesthetic quality of images can be inferred. Motivated by this, this paper presents an Attribute-assisted Multimodal Memory Network (AMM-Net) for image aesthetics assessment, which utilizes aesthetic attributes to model the interactions between visual and textual modalities. Specifically, we design two memory networks to capture the attribute-aware information most related to the image and associated comments respectively. Further, with multiple memory hops, attribute semantics shared by the two modalities are refined and cross-modal interactions are enhanced progressively. Finally, more discriminative aesthetic representations can be obtained for IAA. The experimental results and comparisons on two public multimodal IAA datasets demonstrate the superiority of the proposed model over the state-of-the-art methods. The source code is available at https://github.com/zhutong0219/AMM-Net.

Ensuring safe and effective use of artificial intelligence (AI) requires understanding and anticipating its performance on new tasks, from advanced scientific challenges to transformed workplace activities1-3. So far, benchmarking has guided progress in AI but has offered limited explanatory and predictive power for general-purpose AI systems4-8, attributed to limited transferability across specific tasks9-11. Here we introduce general scales for AI evaluation that elicit demand profiles explaining what capabilities common AI benchmarks truly measure, extract ability profiles quantifying the general strengths and limits of AI systems and robustly predict AI performance for new task instances. Our fully automated methodology builds on 18 rubrics, capturing a broad range of cognitive and intellectual demands, which place different task instances on the same general scales, illustrated on 15 large language models (LLMs) and 63 tasks. Both the demand and the ability profiles on these scales bring new insights such as construct validity through benchmark sensitivity and specificity and explain conflicting claims about whether AI has reasoning capabilities. Ultimately, high predictive power at the instance level becomes possible using the general scales, providing superior estimates over strong black-box baseline predictors, especially in out-of-distribution settings (new tasks and benchmarks). The scales, rubrics, battery, techniques and results presented here constitute a solid foundation for a science of AI evaluation, underpinning the reliable deployment of AI in the years ahead.

Type 2 diabetes mellitus (T2DM) poses a significant global public health challenge, with its prevalence escalating continuously and disproportionately affecting low- and middle-income countries (LMICs), imposing a substantial burden on healthcare systems. Traditional management models have limitations in disease prediction, personalized treatment, and public health intervention. Artificial intelligence (AI) and digital health technologies provide novel insights for precise prediction and intelligent management of T2DM. This review systematically summarizes research progress in AI's role in deciphering T2DM pathogenesis, personalized treatment, and public health management. By integrating multi-omics and environmental data, AI reveals key mechanisms including gene-environment (G × E) interactions, β-cell dysfunction, and inflammatory pathways, significantly enhancing early screening and risk prediction. In clinical management, AI combined with digital health tools [e.g., continuous glucose monitoring (CGM), wearable devices, and mobile health (mHealth) apps] facilitates remote monitoring, medication optimization, and personalized interventions, improving treatment adherence and health management efficiency. At the public health level, AI optimizes resource allocation and disease burden assessment, promoting chronic disease prevention and control model transformation. Future efforts should prioritize developing low-resource-adapted tools, strengthening data privacy protection tailored to LMICs, and addressing algorithmic fairness and the digital divide to ensure safe, equitable, and sustainable AI application in global T2DM management. Overall, AI and digital health integration is driving T2DM management towards an intelligent and precision-based era, with the potential to reduce disparities in LMICs.

Childhood cancer is a leading cause of death in children, and the increasing availability of digital healthcare data, coupled with rapid progress in artificial intelligence (AI), brings a transformative opportunity to revolutionise its diagnosis, treatment and ultimately improve patient outcomes by leveraging diverse data resources. However, the effective application of AI in childhood cancer requires strict adherence to regulatory and best practice guidelines for patient data preparation and AI model development. Currently, there is a lack of such regulatory and methodological guidance specifically tailored for the paediatric community. This review seeks to address this gap. Beginning with an overview of existing regulatory frameworks, it examines the types of data currently in use or with potential use in developing AI applications for childhood cancer. This encompasses data from traditional sources, such as patient data and electronic health records (EHRs), as well as emerging sources like social media data and social determinants of health. This review also outlines the rules and criteria for collecting, processing, and sharing these data. Informed consent and re-consent are required for data collection and re-use, and data quality, privacy, and security as well as data standardisation, harmonisation and interoperability are important for data processing. Additionally, this review clarifies the essential requirements and methodologies for developing AI models in childhood cancer and healthcare. It also emphasises the importance of AI being trustworthy, protecting privacy, and being accountable and validated in clinical settings. By systematically addressing these key components, this review aims to provide comprehensive knowledge and practical tools for the reliable application and implementation of AI in paediatric cancer to enhance AI acceptance and promote its widespread integration within the childhood cancer community. This, in turn, will lead to improved diagnosis, treatment and outcomes for children with cancer.

The rise of artificial intelligence (AI) heralds a significant revolution in healthcare, particularly in mental health.AI's potential spans diagnostic algorithms, data analysis from diverse sources, and real-time patient monitoring. It is essential for clinicians to remain informed about AI's progress and limitations. The inherent complexity of mental disorders, limited objective data, and retrospective studies pose challenges to the application of AI. Privacy concerns, bias, and the risk of AI replacing human care also loom. Regulatory oversight and physician involvement are needed for equitable AI implementation. AI integration and use in psychotherapy and other services are on the horizon. Patient trust, feasibility, clinical efficacy, and clinician acceptance are prerequisites. In the future, governing bodies must decide on AI ownership, governance, and integration approaches. While AI can enhance clinical decision-making and efficiency, it might also exacerbate moral dilemmas, autonomy loss, and issues regarding the scope of practice. Striking a balance between AI's strengths and limitations involves utilizing AI as a validated clinical supplement under medical supervision, necessitating active clinician involvement in AI research, ethics, and regulation. AI's trajectory must align with optimizing mental health treatment and upholding compassionate care.

Artificial intelligence (AI) applied to type 2 diabetes mellitus (T2DM) is transforming the diagnosis and management of this chronic disease, posing a significant public health challenge. Despite recent advances, there remains a gap in the systematization of knowledge regarding AI and T2DM, as well as in the identification of trends and scientific collaborations in this field. This study aims to conduct a comprehensive bibliometric analysis of academic output on AI applied to T2DM, mapping the main actors, collaboration networks, and predominant research themes from 2000 to 2024. A bibliometric analysis was conducted using the Web of Science database, focusing on scientific topics related to AI applied to T2DM from 2000 to 2024. Bibliometric tools such as Bibliometrix, VOSviewer, and CitNetExplorer were utilized to examine publication patterns, co-citation networks, and keywords. The analysis included 1,454 original articles and 134 reviews, aiming to identify the most influential authors, institutions, and countries in the field. The analysis revealed a growth rate of 1.7%, with significant increases observed between 2020 and 2024. The research highlighted the use of AI for the detection of diabetic retinopathy and continuous glucose monitoring as the primary areas of publication. China (27.4%) and India (20.5%) lead scientific production and international collaborations in this field, reflecting the globalization of health research. This study provides an overview of the current state and future opportunities in AI research applied to T2DM. The findings are valuable for researchers, healthcare professionals, and academic institutions, fostering progress in AI and T2DM through collaborative and ethical strategies. This bibliometric analysis contributes to guiding the development of health research policies and optimizing the use of AI in managing T2DM.

The progress in artificial intelligence (AI) technology has greatly changed various facets of society. This study aimed to explore aspects that need to be considered in developing AI curriculum for senior high schools in Indonesia. The qualitative approach employed in this study. The researchers utilized focus group discussions with schools’ management and students at seven cities and group interviews with students at three cities. The results show that some schools want AI as an extracurricular activity, while others want it as a mandatory subject. School management and teachers aim for 2-3 competent AI instructors in each school. If no teachers are available, training will be provided to ICT, mathematics, or physics teachers for about a year to become AI educators. All participants agree on the importance of teaching students about AI applications and discussing ethical issues related to AI.

This investigation aims to evaluate the progress in artificial intelligence (AI) threat detection systems, to strengthen information-system security, which has been achieved by guaranteeing integrity, confidentiality, and availability of information. The results further highlight the use of AI to proactively detect and mitigate unseen attacks before they cause any real damage. Using a qualitative methodology as an investigatory framework, this research reflects an interpretative fine-tooth analysis of the literature to provide evaluation of how AI has been used in prevention and containment. The study also discusses different AI methodologies like machine learning, deep learning, neural networks and how they provide a boost in threat detection. A literature review provides a critical examination and analysis of former studies, highlighting patterns, trends, and new methodologies that underscore the ongoing maturation and intricacy of Internet related threats balancing cybercrime AI countermeasures.High-level takeaways from the results Here are some of the chief findings about how AI can protect your network: General adaptation: When attacking patterns shift away from traditional deterministic rules detection, cyberthreats remain hidden from the human eye. The report also explores the use and implementation of AI along with other security systems including encryption and blockchain to establish multiple defence layers. The integration noticeably enhances the resilience of information systems, making them stronger against APTs and Zero-day attacks.It also focuses attention on the wealth of qualitative data with which to explore more nuanced aspects of information security. This complete view helps to bring further clarity to both the issues and potential of AI-driven security solutions. These results underscore the importance of addressing how to use AI as a critical component in building successful digital security information protection strategies and should argue for its wider implementation throughout industries. The research concludes by recommending further exploration of AI’s role in predictive analytics and automated incident response, areas where its potential remains underutilized

The rapid development of Internet of Things (IoTs) has posed many complex problems that are difficult to be solved with traditional techniques/algorithms. Recently, the explosive progress in artificial intelligence, especially machine learning, provides new dimensions to design and develop learning-based solutions for many IoT problems. This workshop provides a forum for academic researchers and industry practitioners to exchange the most recent progress in artificial intelligence applications in Internet of Things.

We discuss how over the last 30 to 50 years, Artificial Intelligence (AI)\nsystems that focused only on data have been handicapped, and how knowledge has\nbeen critical in developing smarter, intelligent, and more effective systems.\nIn fact, the vast progress in AI can be viewed in terms of the three waves of\nAI as identified by DARPA. During the first wave, handcrafted knowledge has\nbeen at the center-piece, while during the second wave, the data-driven\napproaches supplanted knowledge. Now we see a strong role and resurgence of\nknowledge fueling major breakthroughs in the third wave of AI underpinning\nfuture intelligent systems as they attempt human-like decision making, and seek\nto become trusted assistants and companions for humans. We find a wider\navailability of knowledge created from diverse sources, using manual to\nautomated means both by repurposing as well as by extraction. Using knowledge\nwith statistical learning is becoming increasingly indispensable to help make\nAI systems more transparent and auditable. We will draw a parallel with the\nrole of knowledge and experience in human intelligence based on cognitive\nscience, and discuss emerging neuro-symbolic or hybrid AI systems in which\nknowledge is the critical enabler for combining capabilities of the\ndata-intensive statistical AI systems with those of symbolic AI systems,\nresulting in more capable AI systems that support more human-like intelligence.\n

If artificial intelligence (AI) were achievable, what would the consequences be for human society? Perhaps surprisingly, the answer to this question is already at hand. We are achieving rapid and accelerating success in our quest to build AI. That very success—and the slowness with which both the academic community and the general public have come to recognise it—has shown how little we understand our own intelligence, and its role in our lives and culture. Here I attempt to address this problem of understanding, exploiting a variety of scientific evidence, including social simulation. I begin by reviewing current progress in AI, which is profound but underestimated. I suggest this lack of recognition is due to the mistaken belief that intelligence implies agency. I next examine the related question of human uniqueness: why do only we have language and extensive built culture? I use models and data to show that the propensities to use culture, share information and behave altruistically are neither unique to humans nor inexplicable to biology, but rather our uniqueness hinges on the extent of our capacities for communication and memory. Finally, I apply the impact of AI on extending our intelligence to these theories, to predict—and observe—consequences of AI on human societies and individual human lives. I make and support policy recommendations based on these predictions.

Artificial intelligence (AI) systems paradoxically combine high levels of certain types of intelligence and cognitive capacities (pattern recognition, reasoning, learning, memory, perception, etc.) with an absence of understanding and sentience (feeling, emotion). Apparently, it is possible to make great progress in modeling smartness without making progress towards genuinely understanding what all the clever reasoning is about. This is relevant when dealing with AI programs that produce potentially convincing propositional output on religious topics. This article suggests that smartness without genuine understanding cannot amount to authentic religiosity. Comparing ourselves with other entities, (in)animate or (super)natural, has always been a way for humans to understand ourselves better. Throughout the ages, many different types of beings and agents have functioned as tools for self-examination, presenting us with mirrors that reflect at least some of our characteristics, capacities, and (im)perfections. The recent progress in AI provides exciting, though sometimes worrisome, cases for a newly informed look at ourselves. Thus, AI may have profound effects on how we regard others and ourselves. The proud claim that humans are the smartest species on the planet may turn out not to mean all that much. Inspired by the example of Thomas Aquinas, the comparison of humans to our nearest neighbors in a newly extended great chain of being—namely, animals, angels, and AI—may deepen our appreciation of the features of homo sapiens that we share with many other organisms.

Cooperation at scale is critical for achieving a sustainable future for humanity. However, achieving collective, cooperative behavior—in which intelligent actors in complex environments jointly improve their well-being—remains poorly understood. Complex systems science (CSS) provides a rich understanding of collective phenomena, the evolution of cooperation, and the institutions that can sustain both. Yet, much of the theory in this area fails to fully consider individual-level complexity and environmental context—largely for the sake of tractability and because it has not been clear how to do so rigorously. These elements are well captured in multiagent reinforcement learning (MARL), which has recently put focus on cooperative (artificial) intelligence. However, typical MARL simulations can be computationally expensive and challenging to interpret. In this perspective, we propose that bridging CSS and MARL affords new directions forward. Both fields can complement each other in their goals, methods, and scope. MARL offers CSS concrete ways to formalize cognitive processes in dynamic environments. CSS offers MARL improved qualitative insight into emergent collective phenomena. We see this approach as providing the necessary foundations for a proper science of collective, cooperative intelligence. We highlight work that is already heading in this direction and discuss concrete steps for future research.

Artificial Intelligence (AI) is taking on a growing significance in reshaping the education sector, offering automation, personalized learning, and intelligent support systems. This paper examines the recent progress in AI, with particular attention to the role of Large Language Models (LLMs) and their application within both K- 12 and higher education environments. Adopting a systematic literature review approach, the study identifies c ore domains benefiting from AI , such as adaptive learning platforms, automated assessment tools, and virtual tutoring systems. The analysis shows that AI has the potential to greatly enhance teaching methods and improve student learning experiences by offering more tailored and efficient educational solutions. However, the merging of AI systems technologies also brings several Issues requiring resolution. Ethical issues around data privacy, algorithmic bias, and issues related to equal access to technology are prominent. Technological limitations, such as the need for robust infrastructure and skilled human support, are also critical factors to consider.

The chapter investigates the impact of artificial intelligence (AI) on marketing strategies in the hospitality industry. It discusses how AI technologies like machine learning, natural language processing, and computer vision have transformed customer interactions. The chapter emphasizes the importance of AI in learning and adapting to customer behavior and standards, highlighting its significant role in the industry. This chapter explores the practical applications of AI in the hospitality industry, highlighting its potential in creating unique customer profiles and providing personalized tips. Predictive analytics reveals AI's ability to anticipate vacation patterns and enable dynamic pricing, enabling businesses to adapt to market changes. The chapter emphasizes the marketing significance of augmented and virtual reality, highlighting their potential to provide immersive experiences and influence customer decisions through virtual tours. The chapter concludes by highlighting the industry's progress in AI and the need for further research.