Human Intelligence Research Articles

Artificial mechano-nociceptive system based on transparent ITO/AlN/ITO memristor nociceptor neuron

Artificial nociceptors demonstrate significant potential in emerging fields such as intelligent prosthetics, humanoid robotics, and electronic skin, capable of transducing external noxious stimuli to the central nervous system. Unlike common sensory neurons, nociceptors exhibit unique characteristics, including “no adaptation,” “relaxation,” “threshold firing,” and “sensitization of allodynia/hyperalgesia.” This study presents a forming-free volatile transparent ITO/AlN/ITO memristor that emulates biological nociceptor behaviors. Leveraging this artificial nociceptor, an artificial mechano-nociceptive system is developed by integrating the ITO/AlN/ITO memristor into a piezoelectric force sensor system for pain sensing and noxious stimuli warning. This research contributes to the advancement of human cognitive capability emulation and artificial intelligence systems, particularly in the domain of pain perception and response.

‘Journeys in the Dark’ - Towards Game Master AI in Complex Board Games

The Game Master is a player role synonymous with many tabletop games. The asymmetric gameplay of the role provides different opportunities compared to other players, and can be both cooperative and competitive with the other players in the same game. Though complex environments for exploring human and Artificial Intelligence collaboration exist, few focus on the Game Master role's semi-cooperative play. Here, we propose a new complex environment based on the board game `Descent: Journeys in the Dark (Second Edition)', as part of the Tabletop Games Framework, showcasing one-versus-many play, tactical combat, and large, dynamic action and state spaces. We include baseline AI player performance of Monte Carlo Tree Search agents in this game, finding them to be well-adept at considering multiple possible end-game conditions compared to the greedy One Step Look Ahead agents. In-depth analysis reveals interesting behaviours and Hero synergies, with the aim of informing the design of games and AI models to enhance human experience in semi-cooperative environments.

Deep learning of structural MRI predicts fluid, crystallized, and general intelligence.

Can brain structure predict human intelligence? T1-weighted structural brain magnetic resonance images (sMRI) have been correlated with intelligence. However, the population-level association does not fully account for individual variability in intelligence. To address this, studies have emerged recently to predict individual subject's intelligence or neurocognitive scores. However, they are mostly on predicting fluid intelligence (the ability to solve new problems). Studies are lacking to predict crystallized intelligence (the ability to accumulate knowledge) or general intelligence (fluid and crystallized intelligence combined). This study tests whether deep learning of sMRI can predict an individual subject's verbal, comprehensive, and full-scale intelligence quotients (VIQ, PIQ, and FSIQ), which reflect fluid and crystallized intelligence. We performed a comprehensive set of 432 experiments, using different input image channels, six deep learning models, and two outcome settings, in 850 healthy and autistic subjects 6-64 years of age. Our findings indicate a statistically significant potential of T1-weighted sMRI in predicting intelligence, with a Pearson correlation exceeding 0.21 (p < 0.001). Interestingly, we observed that an increase in the complexity of deep learning models does not necessarily translate to higher accuracy in intelligence prediction. The interpretations of our 2D and 3D CNNs, based on GradCAM, align well with the Parieto-Frontal Integration Theory (P-FIT), reinforcing the theory's suggestion that human intelligence is a result of interactions among various brain regions, including the occipital, temporal, parietal, and frontal lobes. These promising results invite further studies and open new questions in the field.

The role and impact of new technologies on healthcare systems

AbstractThe integration of new technologies into healthcare systems is revolutionizing the industry by significantly enhancing the capabilities of healthcare providers, improving patient outcomes, and optimizing administrative operations. This comprehensive examination critically assesses the impact of these technologies on various stakeholders within the healthcare ecosystem, highlighting both the benefits and challenges. Emerging technologies such as artificial intelligence (AI), which refers to the simulation of human intelligence by machines, the Internet of Things (IoT), which involves interconnected devices that collect and exchange health-related data in real time, Blockchain, which is a decentralized and tamper-proof ledger technology used to securely manage patient data, and wearable devices are explored for their potential applications in diagnostics, treatment, and patient monitoring. Despite their transformative potential, these technologies also bring forth a range of limitations and challenges, including ethical concerns regarding patient privacy, legal implications of data security, and technical hurdles related to interoperability and implementation. By addressing these issues, the healthcare industry can better navigate the complexities of technological advancement, ensuring that innovation contributes positively to patient care and operational efficiency.

Artificial intelligence in fracture detection on radiographs: a literature review.

Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be misdiagnosed during radiologic examination. Accurate and timely diagnosis of fracture is crucial for patients, and artificial intelligence that uses algorithms to imitate human intelligence to aid or enhance human performs is a promising solution to address this issue. In the last few years, numerous commercially available algorithms have been developed to enhance radiology practice and a large number of studies apply artificial intelligence to fracture detection. Recent contributions in literature have described numerous advantages showing how artificial intelligence performs better than doctors who have less experience in interpreting musculoskeletal X-rays, and assisting radiologists increases diagnostic accuracy and sensitivity, improves efficiency, and reduces interpretation time. Furthermore, algorithms perform better when they are trained with big data on a wide range of fracture patterns and variants and can provide standardized fracture identification across different radiologist, thanks to the structured report. In this review article, we discuss the use of artificial intelligence in fracture identification and its benefits and disadvantages. We also discuss its current potential impact on the field of radiology and radiomics.

A text analytics study on the alignment of an Indonesian Muslim community’s municipal corporation policy with Sustainable Development Goals

PurposeThis study analyses the supervision policy of Municipal Corporations (MCs) in the Indonesian Muslim community regarding its alignment with the United Nations Sustainable Development Goals (SDGs).Design/methodology/approachAs a single case study, purposive sampling was adopted to select a municipal corporation policy issued by Ciamis Regency, West Java, Indonesia. A novel mixed methods approach, combining computer analytics and human intelligence, was introduced to perform text analytics.FindingsText mining identified the most frequently occurring words — e.g., ‘perumda’ (municipal corporation), ‘daerah’ (regional), ‘pengawas’ (supervisor), ‘peraturan’ (regulation) — from the sample but found no single word indicating business alignment with SDGs. Further qualitative inductive analysis was conducted, revealing the critical role of MCs’ supervisory boards in business planning, execution, and reporting to align the MC businesses with local SDG initiatives.Originality/valueAligning MC business activities with local SDG actions is entirely within the authority of supervisory boards which demands transformational leadership. This research pioneered an innovative blend of computer-assisted techniques and human reasoning to investigate the supervision policies of MCs concerning local SDG actions, with evidence from a Muslim community in Indonesia.

Computational diplomacy: how 'hackathons for good' feed a participatory future for multilateralism in the digital age.

This article explores the role of hackathons for good in building a community of software and hardware developers focused on addressing global sustainable development goal (SDG) challenges. We theorize this movement as computational diplomacy: a decentralized, participatory process for digital governance that leverages collective intelligence to tackle major global issues. Analysing Devpost and GitHub data reveals that 30% of hackathons since 2010 have addressed SDG topics, employing diverse technologies to create innovative solutions. Hackathons serve as crucial kairos moments, sparking innovation bursts that drive both immediate project outcomes and long-term production. We propose that these events harness the neurobiological basis of human cooperation and empathy, fostering a collective sense of purpose and reducing interpersonal prejudice. This bottom-up approach to digital governance integrates software development, human collective intelligence and collective action, creating a dynamic model for transformative change. By leveraging kairos moments, computational diplomacy promotes a more inclusive and effective model for digital multilateral governance of the future. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'.

The Sloth of Luddite Sorrow in Thomas Pynchon’s Writings

The technological inventions have been dominating the present times. The world is in awe of the way AI is replacing human intelligence but not without a remorse fear of the threat of apocalypses this AI and Scientific expansion could cause. The scientific and technological advancement has made the imperialism, subjugation and exploitation seamless and transcendent. The public fail to recognize this transcendent exploitation as their vision is outshined by miraculous sophistication these inventions ducks human life with. Some good minds might envision the ramifications but fear to be branded as luddites. Almost thirty nine years ego Pynchon foresees the paradoxical paranoia and reassures to be fine be a luddite. He professes that a person with the sloth of luddite sorrow is a real human defeating the merciless omniscient exploitation. Though this vision of Pynchon pervades all his novels, my article refers to his magna Opus Gravity’s Rainbow and his mature novel Vineland to illustrate his vision.

Computational and Human Intelligence Methods for Constructing Practical Risk Prediction Models: An Application to Cardio-Renal Outcomes in Non-Diabetic CKD Patients

AbstractThe current investigation aimed to develop a novel approach for risk prediction modeling of clinical outcomes in common diseases based on computational and human intelligence techniques with no a priori input on risk factors using real-world individual patient-level data from administrative claims. Bootstrapping multivariable Cox regression and ant colony optimization were employed to develop time-to-first-event risk prediction models of cardio-renal outcomes in patients with non-diabetic chronic kidney disease (CKD) as a demonstration case. A cohort of 504,924 non-diabetic CKD stage 3 or 4 patients enrolled from 2008 to 2018 were identified in the US administrative de-identified claims database, Optum Clinformatics® Data Mart. Initial set of potential risk factors was derived from patient-level data at baseline and included more than 540,000 variables. Risk prediction models of hospitalization for heart failure, worsening of CKD stage from baseline and a renal composite outcome of end-stage kidney disease, kidney failure or need for dialysis in non-diabetic CKD stage 3 or 4 were built. Final model optimization was conducted using human intelligence to combine clinically similar features and build equivalence classes to ensure that risk factors included in the final model were routinely collected and easily interpretable by healthcare providers. Demonstrated validity of our approach in non-diabetic CKD offers opportunities for application in other therapeutic areas, with the potential to improve overall prognosis and decrease the clinical and economic burden of common diseases. The approach enables developing practical prediction models for risk estimation in routine clinical practice.

Unveiling the power of artificial intelligence for image-based diagnosis and treatment in endodontics: An ally or adversary?

Artificial intelligence (AI), a field within computer science, uses algorithms to replicate human intelligence tasks such as pattern recognition, decision-making and problem-solving through complex datasets. In endodontics, AI is transforming diagnosis and treatment by applying deep learning algorithms, notably convolutional neural networks, which mimic human brain function to analyse two-dimensional (2D) and three-dimensional (3D) data. This article provides an overview of AI applications in endodontics, evaluating its use in 2D and 3D imaging and examining its role as a beneficial tool or potential challenge. Through a narrative review, the article explores AI's use in 2D and 3D imaging modalities, discusses their limitations and examines future directions in the field. AI significantly enhances endodontic practice by improving diagnostic accuracy, workflow efficiency, and treatment planning. In 2D imaging, AI excels at detecting periapical lesions on both periapical and panoramic radiographs, surpassing expert radiologists in accuracy, sensitivity and specificity. AI also accurately detects and classifies radiolucent lesions, such as radicular cysts and periapical granulomas, matching the precision of histopathology analysis. In 3D imaging, AI automates the segmentation of fine structures such as pulp chambers and root canals on cone-beam computed tomography scans, thereby supporting personalized treatment planning. However, a significant limitation highlighted in some studies is the reliance on in vitro or ex vivo datasets for training AI models. These datasets do not replicate the complexities of clinical environments, potentially compromising the reliability of AI applications in endodontics. Despite advancements, challenges remain in dataset variability, algorithm generalization, and ethical considerations such as data security and privacy. Addressing these is essential for integrating AI effectively into clinical practice and unlocking its transformative potential in endodontic care. Integrating radiomics with AI shows promise for enhancing diagnostic accuracy and predictive analytics, potentially enabling automated decision support systems to enhance treatment outcomes and patient care. Although AI enhances endodontic capabilities through advanced imaging analyses, addressing current limitations and fostering collaboration between AI developers and dental professionals are essential. These efforts will unlock AI's potential to achieve more predictable and personalized treatment outcomes in endodontics, ultimately benefiting both clinicians and patients alike.

Managing human-AI collaborations within Industry 5.0 scenarios via knowledge graphs: key challenges and lessons learned

In this paper, we discuss technologies and approaches based on Knowledge Graphs (KGs) that enable the management of inline human interventions in AI-assisted manufacturing processes in Industry 5.0 under potentially changing conditions in order to maintain or improve the overall system performance. Whereas KG-based systems are commonly based on a static view with their structure fixed at design time, we argue that the dynamic challenge of inline Human-AI (H-AI) collaboration in industrial settings calls for a late shaping design principle. In contrast to early shaping, which determines the system's behavior at design time in a fine granular manner, late shaping is a coarse-to-fine approach that leaves more space for fine-tuning, adaptation and integration of human intelligence at runtime. In this context we discuss approaches and lessons learned from the European manufacturing project Teaming.AI, https://www.teamingai-project.eu/, addressing general challenges like the modeling of domain expertise with particular focus on vertical knowledge integration, as well as challenges linked to an industrial KG of choice, such as its dynamic population and the late shaping of KG embeddings as the foundation of relational machine learning models which have emerged as an effective tool for exploiting graph-structured data to infer new insights.

The Convergence of Artificial Intelligence and Human Marketing: A Framework for Enhanced Customer Insights and Personalization

This article examines the transformative integration of artificial intelligence and human expertise in modern marketing analytics, focusing on customer insight generation and personalization strategies. We present a comprehensive framework for human-AI collaboration in marketing operations through a mixed-methods approach combining quantitative analysis of customer data from 127 retail organizations and qualitative interviews with 34 marketing professionals. The findings reveal that organizations implementing AI-enhanced customer analytics in conjunction with human-driven creative strategy achieved a 47% improvement in customer engagement metrics and a 31% increase in conversion rates compared to traditional approaches. The article demonstrates that while AI excels at real-time pattern recognition and predictive modeling of customer behavior, human marketers provide crucial emotional intelligence and contextual interpretation that significantly enhance the effectiveness of personalization efforts. The article introduces the Dual Intelligence Marketing Framework (DIMF), which outlines optimal integration points between AI capabilities and human expertise throughout the customer journey. The framework addresses critical challenges in implementation, including technical infrastructure requirements, talent adaptation, and ethical considerations. This article contributes to both theoretical understanding and practical application of AI in marketing, offering actionable insights for organizations seeking to leverage the complementary strengths of artificial and human intelligence in their marketing operations.

Exploring the Role of Artificial Intelligence in Revolutionizing Microbial Diagnostics

Identifying the microorganisms responsible for illnesses and infections is crucial for accurate diagnosis, which is an essential aspect of providing medical care. Artificial intelligence (AI) systems can enhance drug discovery, epidemiological monitoring, prediction of antibiotic resistance, and disease management in the field of microbiological diagnosis. Artificial intelligence (AI) is a branch of science and technology that uses computers to simulate human intelligence. AI has recently shown tremendous promise as a powerful computational tool for the detection and management of bacterial infections. These machines can imitate human thought processes and cognitive capacities. Today, pathogen identification and Antimicrobial Susceptibility Testing (AST) in clinical laboratories often rely on culturing and isolating pathogens. With the rapid advancement of technology, Artificial Intelligence (AI) has become a vital tool for bacterial AST, providing numerous fast and efficient methods for testing drug susceptibility. AI systems offer enhanced diagnostic methods and early detection of antibiotic resistance. Moreover, they can rapidly and accurately identify diseases, including those that are novel or drug-resistant. In addition, AI can be applied to identify cells infected with malaria, detect drug resistance in pathogens like tuberculosis (TB) and HIV, understand and tackle antimicrobial resistance (AMR), and predict outbreaks. The primary goals of applying AI to bacterial diagnosis are the rapid and precise identification of pathogens and the prediction of drug resistance.

Ethical considerations in telehealth and artificial intelligence for work related musculoskeletal disorders: A scoping review.

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to perform tasks that typically require human intelligence. The integration of AI and telehealth applications in healthcare raises ethical concerns such as bias, transparency, data privacy, and accountability for errors. Several studies have assessed this topic, particularly with regard to musculoskeletal disorders, which will be the focus of this manuscript. We will examine key ethical concepts including informed consent, data protection, confidentiality, physician malpractice, liability, and telemedicine regulations. Ethical issues pertaining to the topic were explored through a review paper. The primary objective of this scoping review was to map and synthesize the existing literature concerning ethical considerations in telehealth and AI for work-related musculoskeletal disorders. Research demonstrates that medication effectiveness, patient and physician satisfaction, and accessibility costs are higher with telemedicine and AI methods compared to in-person approaches, particularly for work-related musculoskeletal disorders. Therefore, addressing ethical issues, including patient data privacy and security, is crucial in this field. By considering these factors, the adoption of emerging AI and telemedicine applications, especially for work-related musculoskeletal disorders, is likely to increase. AI and telemedicine offer significant advantages, particularly in addressing work-related musculoskeletal disorders. However, ethical and legal issues surrounding their practice require standardized rules to ensure equitable access, quality care, sustainable costs, professional liability, patient privacy, data protection, and confidentiality. Further practical research studies are needed to address these considerations more effectively.

EDUCATION AS A MAJOR CAPITAL AND THE MAIN BASIS FOR THE ECONOMIC POTENTIAL OF THE COUNTRY

Based on the works of philosophers of the ancient world and scientists of our time, the article analyzes the need for the existence of two components of its development in any country, namely human capital and financial and political opportunities. Attention is paid to the fact that intellectual potential is a set of resources that include human intelligence and creative ability, with its characteristic educational and qualification indicators. It is proved that higher education graduates are the intellectual core that forms the market of qualified specialists capable of becoming the economic potential of the country. It is shown that the rational use of this potential, in combination with other scientific achievements of society, contributes to the achievement of a new quality of economic development, raising living standards and preserving the environment for future generations. That is, the level of awareness and education of each individual is the molecule that lays the foundation for consolidating the country's competitive influence in the international market. To implement the above, the need for more active use of public administration in the educational process at all levels is determined, the expediency of applying legislative regulation of the process of interaction between higher education institutions and business structures is investigated, both for the establishment of joint projects and processes of commercialization and reform of education in different countries, including in modern Ukraine, and for achieving positive changes in strategic planning and economic development.

Beyond Human and Machine: An Architecture and Methodology Guideline for Centaurian Design

The concept of the centaur, symbolizing the fusion of human and machine intelligence, has intrigued visionaries for decades. Recent advancements in artificial intelligence have made this concept not only realizable but also actionable. This synergistic partnership between natural and artificial intelligence promises superior outcomes by leveraging the strengths of both entities. Tracing its origins back to early pioneers of human–computer interaction in the 1960s, such as J.C.R. Licklider and Douglas Engelbart, the idea initially manifested in centaur chess but faced challenges as technological advances began to overshadow human contributions. However, the resurgence of generative AI in the late 2010s, exemplified by conversational agents and text-to-image chatbots, has rekindled interest in the profound potential of human–AI collaboration. This article formalizes the centaurian model, detailing properties associated with various centaurian designs, evaluating their feasibility, and proposing a design methodology that integrates human creativity with artificial intelligence. Additionally, it compares this model with other integrative theories, such as the Theory of Extended Mind and Intellectology, providing a comprehensive analysis of its place in the landscape of human–machine interaction.

INTELLIGENCE GATHERING AND ITS CONTRIBUTION TO NIGERIA'S NATIONAL SECURITY: A CRITICAL ASSESSMENT

The Nigerian intelligence sector faces institutional limitations that hinder the successful implementation of intelligence collecting, which is widely acknowledged as a crucial tool in tackling these issues. This paper provides a thorough evaluation of the function of intelligence gathering within Nigeria's national security framework, analysing its effectiveness in dealing with current security challenges. This study seeks to offer a thorough examination of the intelligence cycle in the Nigerian setting, including the gathering, analysis, and use of intelligence in national security strategy. The research used a mixed-methods methodology, combining quantitative data obtained from a questionnaire with qualitative insights obtained from an empirical literature analysis. The Network Theory (TNT) functions as the conceptual framework, providing a perspective to examine the intricate connections and transmission of information within the intelligence network. Statistical analysis is conducted on the questionnaire results using descriptive and inferential methods, while the findings of the literature review are consolidated to offer context and depth. The study results highlight a significant dependence on Human Intelligence (HUMINT) and source intelligence (OSINT) in Nigeria, with a lack of implementation of Geospatial Intelligence (GEOINT) and a disregard for Measurement and Signature Intelligence (MASINT). Furthermore, the investigation exposes inadequate levels of trust and collaboration between intelligence agencies and other security organizations. The major obstacles mentioned are insufficient financial resources, insufficient training and knowledge, technical constraints, competition among different agencies, and legal and bureaucratic restrictions. The research findings indicate that although intelligence gathering is of utmost importance for Nigeria's national security, its efficacy is hindered by structural and operational obstacles. personnel growth and comprehensive strategy to improve intelligence capabilities, which involves bolstering inter- agency collaboration, allocating resources to technical progress and skilled personnel growth, and creating a strong legal and ethical structure.

Human intelligence: is the human brain reasonable?

The human brain is a remarkable organ which, in addition to many vital functions, is also extremely adaptable and capable of learning. As a result, the brain is a highly individual organ that is shaped by culture. This complicates the search for a universally valid reason that must be anchored in the brain.

Animation, Automata, Biomimesis

The animation-effect of the movement-image constitutes not only a form of life but also a machinic analogue for Husserl's and Kant's accounts of temporality, engendering what Bernard Stiegler calls ‘a cinematography of consciousness’. If autonomous movement is the hallmark of the living being as well as the inanimate mechanism, the movement-image likewise subtends scenes of human and artificial intelligence. Pairing a cybernetic reading of Lacan's mirror stage essay with reflections on the self-recognition dance of Machina Speculatrix (an early example of a biomimetic robot), this essay considers how exosomatic feedback loops enable a kind of ipseity for machinic life as well as the human being as technical being. Despite adopting Derrida's ideas of originary prostheticity and automaticity, rendering living beings as always-already machinic life, Stiegler sets arche-cinema against arche-writing.

Bridging Human and Machine Intelligence: Reverse-Engineering Radiologist Intentions for Clinical Trust and Adoption

In the rapidly evolving landscape of medical imaging, the integration of artificial intelligence (AI) with clinical expertise offers unprecedented opportunities to enhance diagnostic precision and accuracy. Yet, the "black box" nature of AI models often limits their integration into clinical practice, where transparency and interpretability are important. This paper presents a novel system leveraging the Large Multimodal Model (LMM) to bridge the gap between AI predictions and the cognitive processes of radiologists. This system consists of two core modules, Temporally Grounded Intention Detection (TGID) and Region Extraction (RE). The TGID module predicts the radiologist's intentions by analyzing eye gaze fixation heatmap videos and corresponding radiology reports. Additionally, the RE module extracts regions of interest that align with these intentions, mirroring the radiologist’s diagnostic focus. This approach introduces a new task, radiologist intention detection, and is the first application of Dense Video Captioning (DVC) in the medical domain. By making AI systems more interpretable and aligned with radiologist’s cognitive processes, this proposed system aims to enhance trust, improve diagnostic accuracy, and support medical education. Additionally, it holds the potential for automated error correction, guiding junior radiologists, and fostering more effective training and feedback mechanisms. This work sets a precedent for future research in AI-driven healthcare, offering a pathway towards transparent, trustworthy, and human-centered AI systems. We evaluated this model using NLG(Natural Language Generation), time-related, and vision-based metrics, demonstrating superior performance in generating temporally grounded intentions on REFLACX and EGD-CXR datasets. This model also demonstrated strong predictive accuracy in overlap scores for medical abnormalities and effective region extraction with high IoU(Intersection over Union), especially in complex cases like cardiomegaly and edema. These results highlight the system's potential to enhance diagnostic accuracy and support continuous learning in radiology. We are also releasing the source code for our project, available here.