Human Intelligence Research Articles

ARTIFICIAL INTELLIGENCE IN THE 21ST CENTURY: ITS IMPACTS, CHALLENGES, PRESENT AND FUTURE PREDICTIONS IN NIGERIA

Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Artificial intelligence in the last two decades has greatly improved performance of the manufacturing and service systems in many countries and Nigeria should not be left behind. The impacts, challenges, present and future predictions of Artificial intelligence in Nigeria is the focus of this paper. This paper had actually checked out the future forecasts for artificial intelligence in Nigeria and also based upon which prospective remedy will certainly be suggested to solve it within following decades.

Holographic Leadership Redefining Corporate Presence

Leadership is the backbone of any organization’s success. Normally, traditional leadership styles rely on rigid hierarchical structures along with centralized decision making. This limits autonomy, creativity and innovation. In today’s date, leadership has transformed into a more flexible, adaptive style that encourage collaboration and innovation and decentralization. In this fast-paced, technology-driven world, holographic leadership is redefining how organizations work. This approach emphasizes shared decision-making, team autonomy, and self-organization rather than centralized authority. This study explores the concept of holographic leadership. It examines the significant benefits such as fostering innovation, empowering employees and improving overall business performance. By encouraging a flat hierarchy and data-driven decision-making, it allows organizations to navigate change and digital transformation more effectively. However, while this leadership style presents many advantages, it also comes with challenges and limitations which have been discussed in the paper. This paper aims to explore the impact leadership styles on Job Satisfaction and Employee Performance through correlation and Binary logistic regression. A plan to adopt a holographic leadership style has also been discussed. Lastly, the role of AI in leadership is also stated. As leadership continues to evolve, integrating AI with holographic leadership can create a balanced model that blends technology with human empathy, cultural intelligence, and strategic foresight. This approach ensures that organizations remain adaptable, innovative, and competitive in and ever-changing corporate landscape.

The Role of Artificial Intelligence and Machine Learning in Modern Healthcare and Technology

AI is becoming an integral part of medicine, enhancing patient care by optimizing processes and increasing accuracy, ultimately leading to improved healthcare outcomes. Machine learning assists in analyzing radiological images, pathology slides, and electronic medical records (EMRs), complementing clinicians’ expertise in diagnosis and treatment. Effective AI management requires strong communication, leadership, coordination, and oversight in a rapidly evolving field that leverages human intelligence to address complex decision-making challenges. As a multidisciplinary domain, artificial intelligence has evolved at the intersection of computer science, cybernetics, information theory, psychology, linguistics, and neurophysiology While AI is on the rise, many people are still less familiar with its concepts. This comprehensive overview from Axis 1 aims to demystify AI for policymakers, opinion leaders, and the public, highlighting its growing influence and the social, economic, and governance challenges it presents. While most current AI research focuses on specific scientific and engineering challenges, often distinct from cognitive science, the concept of general AI has long served as a guiding vision. To advance AI in therapeutic science, initiatives such as the Therapeutics Data Commons are helping to access and evaluate AI performance across a variety of therapeutic approaches and discovery stages. Developing safe and effective medicines is essential to meeting the healthcare needs of billions of people around the world. Deep learning algorithms, a subset of machine learning, focus on understanding multiple layers of distributed representations

Artificial Intelligence Versus Human Intelligence in Presurgical Implant Planning: A Preclinical Validation.

To validate an innovative artificial intelligence (AI)-driven tool for automated virtual implant placement by comparing its accuracy, implant dimension selection, time efficiency, and consistency with a human intelligence (HI)-based approach for single posterior tooth replacement. A dataset of 50 time-matched cone-beam computed tomography and intraoral scans with a single missing posterior mandibular tooth was selected to validate a pre-trained AI model for virtual implant placement against a HI-based approach. A quantitative comparison of implant location and implant dimension selection was conducted between AI and HI, and a qualitative three-dimensional evaluation was conducted by three implant dentistry specialists using a visual analog scale and a Turing test to assess and distinguish between AI and HI. Additionally, time consumption and consistency were evaluated. Experts found that approximately 89% of AI-planned and 93% of HI-planned implants were clinically acceptable, with the planning method unidentifiable in 58% of AI cases. AI selected implant dimensions of 11.7 mm (1.3) in length and 4.0 mm (0.3) in diameter, close to experts' selections of 11.5 mm (1.3) and 4.2 mm (0.4). AI was over twice as fast, reducing planning time to 187 s (34) compared to 406 s (68) for HI (p < 0.0001), and demonstrated high consistency with a median surface deviation (MSD) of zero, while intra- and inter-operator MSDs were 0.33 mm (0.14) and 0.56 mm (0.34), respectively (p < 0.0001). Artificial intelligence is reliable for virtual implant placement in missing mandibular (pre)molars, producing clinically acceptable plans comparable to human experts while operating faster and much more consistently than implant clinicians.

Harnessing Medicinal Chemical Intuition from Collective Intelligence.

Over the past decade, collective intelligence, i.e., the intelligence that emerges from collective efforts, has transformed complex problem-solving and decision-making. In drug discovery, decision-making often relies on medicinal chemistry intuition. The present study explores the application of collective intelligence in drug discovery, focusing on lead optimization. Ninety-two Sanofi researchers with diverse expertise participated anonymously in an exercise centered on ADMET-related questions. Their feedback was used to build a collective intelligence agent, which was compared to an artificial intelligence model. The study led to three major conclusions: first, collective intelligence improves decision-making in optimizing ADMET endpoints, compared to individual decisions. Second, collective intelligence outperforms artificial intelligence for all other endpoints but hERG inhibition. Finally, we observe complementarity between collective human and artificial intelligence. Overall, this research highlights the potential of collective intelligence in drug discovery and the importance of a synergistic approach combining human and artificial intelligence in project decision making.

Artificial intelligence versus collective intelligence

Abstract The ontological presupposition of artificial intelligence (AI) is the liberal autonomous human subject of Locke and Kant, and the ideology of AI is the automation of this particular conception of intelligence. This is demonstrated in detail in classical AI by the work of Simon, who explicitly connected his work on AI to a wider programme in cognitive science, economics, and politics to perfect capitalism. Although Dreyfus produced a powerful Heideggerian critique of classical AI, work on neural networks in AI was ultimately based on the individual as the locus of intelligence. Yet this conception of AI both fails to grasp the essence of large language models, which are a statistical model of human language on the Web. The training data that enables AI is the surveillance and capture of data, where the data creates a model to approximate the entire world. However, there is a more hidden ideology inherent in AI where the goal is not to perfect a model but to control the world. As prompted by an argument between Mead and Bateson, social change is prevented by the application of cybernetics to society as a whole. The goal of AI is not just to replace human beings, but to manage humans to preserve existing power relations. As the source of intelligence in AI is distributed cognition between humans and machines, the alternative to AI is collective intelligence. As theorized by Licklider and Engelbart at the dawn of the Internet, collective intelligence explains how computers weave together both human and non-human intelligence. Rather than replace human intelligence, this produces ever more complex collective forms of intelligence. Rather than meta-stabilize a society of control, collective intelligence can go outside individualist capitalist ontology by incorporating the open world of the pluriverse, as theorized by Escobar. Collective intelligence then stands as an alternative ontological path for AI which puts intelligence at the service of humanity and the world rather than a technocratic elite.

Освіта і університет в епоху штучного інтелекту

The article analyzes the problem of education development in the context of the formation of a society in which modern technologies, and in particular, artificial intelligence (AI) developments, play a key role. Since fast learning machines are being introduced into all spheres of life (production, services, entertainment) of society, and its development depends on the intensity of the introduction of AI technologies, virtual spaces are increasingly influencing the human life, everyday life and the labour market, the problem of the development of education, including university education, its purpose, tasks, content and nature is relevant. The point is that AI is actively involved in the learning process, and it is here that its use threatens to displace humans and interpersonal communication. Automation of assessment, generation of AI answers create a situation of a kind of iconic fetishism, and the educational process turns into a simulacrum. This prompts the search for new forms of human activity, which should be developed by the educational process aimed at stimulating thinking. Therefore, it is necessary to abandon the model of objective factual knowledge that can be automated and executed by machines. The development of AI will lead to the disappearance of linear thinking practices shaped by the culture of writing, cognitive functions will also be transformed, and humans will rapidly move beyond subject-centred vision. In the posthuman mindset, interaction with any non-human beings/entities creates prospects for new forms of human activity. In a world where there are more and more available resources, when each autonomous subject determines the trajectory of learning, the university as a centre of social development must be rethought. University education should be aimed at interpersonal communication, focusing on project-based activities, interpretive practices, problematisation, and creativity. The educational process should be shaped by a balance between AI technologies and the development of human intelligence, which increases the importance of interpersonal communication in the educational process.

SWOT Analysis of AI Integration in Islamic Education: Cognitive, Affective, and Psychomotor Impacts

Artificial Intelligence (AI) has emerged as a transformative force in education, including Islamic religious education, where it offers new opportunities to enhance learning methodologies. This study aims to analyze the integration of AI in Islamic education by evaluating its strengths, weaknesses, opportunities, and threats (SWOT analysis) while categorizing its impact on cognitive, affective, and psychomotor domains based on Bloom’s Taxonomy. A qualitative approach using library research methodology was employed, with data collected from academic journals, books, and research reports, analyzed through a SWOT framework. The findings indicate that AI significantly enhances cognitive and psychomotor learning in Islamic education. AI-based tools, such as ClassPoint AI, AI Chatbots, and Squirrel AI, contribute to knowledge retention, adaptive learning, and skill-based training in areas like Quranic recitation, prayer practices, and Islamic jurisprudence. However, AI remains limited in fostering affective learning, as it lacks human emotional intelligence and the ability to provide moral and ethical guidance, which are essential in Islamic education. The study also reveals challenges such as ethical concerns, technological disparities, and socio-cultural resistance in integrating AI into religious studies. Despite these limitations, AI presents significant opportunities, particularly in remote learning, personalized education, and accessibility for underserved communities. This research provides a structured evaluation of AI’s role within Bloom’s Taxonomy, offering insights into AI’s potential and limitations in Islamic education. The study contributes theoretically by linking AI-driven education with pedagogical principles, while practically, it guides educators and policymakers in strategically implementing AI while preserving Islamic ethical values. The study concludes that while AI enhances knowledge acquisition and skill-based learning, human educators remain essential for moral and ethical development. Future research should focus on developing ethical AI models, hybrid AI-human teaching approaches, and AI-driven affective learning systems to bridge gaps in AI-assisted moral and spiritual education.

The Feasibility of Using Artificial Intelligence in Hadith Research

This study examines the feasibility of using artificial intelligence (AI) in hadith research to identify areas within hadith sciences where AI can offer more efficient and reliable outcomes. The significance of the study lies in its exploration of the potential benefits and challenges of integrating AI into hadith scholarship, especially in addressing contemporary issues. It evaluates how AI could help tackle modern challenges in hadith studies and assesses the risks associated with AI-generated fabrications of hadiths, which may present new threats as AI becomes more widespread. The main problem addressed in this research is determining the feasibility of AI in hadith studies by identifying potential opportunities and risks. The primary objective is to provide current hadith scholars with a foundational understanding of AI's potential in this field while also identifying areas for future research. Methodologically, the study begins with a theoretical overview of AI’s core characteristics, particularly its capabilities in textual analysis and pattern recognition. This is followed by practical experiments using ChatGPT-4 to analyze hadith narrations. The study concludes that while AI has considerable potential in hadith research, particularly in preliminary evaluations and classifications, its current role is limited to imitating human intelligence and functioning within existing scholarly frameworks. The misuse of AI could result in the unintentional dissemination of fabricated hadiths, posing a significant threat to hadith scholarship. In the final evaluation, it is recommended that AI be viewed as a tool to support, rather than replace, human scholars in hadith studies.

Noise in Cognition: Bug or Feature?

Noise in behavior is often considered a nuisance: Although the mind aims for the best possible action, it is let down by unreliability in the sensory and response systems. Researchers often represent noise as additive, Gaussian, and independent. Yet a careful look at behavioral noise reveals a rich structure that defies easy explanation. First, in both perceptual and preferential judgments sensory and response noise may potentially play only minor roles, with most noise arising in the cognitive computations. Second, the functional form of the noise is both non-Gaussian and nonindependent, with the distribution of noise being better characterized as heavy-tailed and as having substantial long-range autocorrelations. It is possible that this structure results from brains that are, for some reason, bedeviled by a fundamental design flaw, albeit one with intriguingly distinctive characteristics. Alternatively, noise might not be a bug but a feature. Specifically, we propose that the brain approximates probabilistic inference with a local sampling algorithm, one using randomness to drive its exploration of alternative hypotheses. Reframing cognition in this way explains the rich structure of noise and leads to the surprising conclusion that noise is not a symptom of cognitive malfunction but plays a central role in underpinning human intelligence.

Large Language Models and the Enhancement of Human Cognition: Some Theoretical Insights

This essay explores the possible contribution of Large Language Models (LLMs) to human cognition. It investigates whether human cognition can be enhanced by advanced AI systems such as LLMs. Can LLMs make people as learners smarter, or, on the contrary, make them reason/think less? The author discusses the concepts of human and artificial intelligence and examines LLMs as advanced AI systems, which use deep learning techniques and can be considered as excelling in neural network architectures, data volume, generalisation and scalability. The author suggests that while LLMs can assist in facilitating numerous cognitive tasks, more research and philosophical inquiry is needed to understand whether such kind of AI assistance would make people cultivate human intelligence more, and not less. Presumably, Large Language Models (LLMs) can contribute to human intelligence and cognition just under strict (addressed existing limitations, questioning prompting, time-sensitivity, etc.) conditions. However, it is important that these theoretical considerations could be verified by experimental research.

Artificial Intelligence for MSMEs in Product Display of Typical Jakarta Food

Artificial Intelligence (AI) is a technology that enables machines to mimic human intelligence, such as recognizing patterns, making decisions, and predicting specific outcomes. In the context of Micro, Small, and Medium Enterprises (MSMEs), AI serves as a highly potential tool to help businesses adapt to the digital era. AI can be applied in various business aspects, ranging from marketing and inventory management to customer service. This technology not only enhances operational efficiency but also provides opportunities to understand customer needs more deeply through data analysis. For instance, an MSME producing Betawi traditional foods, such as cassava chips and Betawi-style nasi kuning serundeng, has utilized AI-based applications to edit product images automatically. This technology enables background removal, the addition of decorative elements, and lighting adjustments in just a few simple steps. As a result, product photos become more appealing, increasing visual attractiveness on e-commerce platforms and social media, which directly impacts sales growth. The approach in this activity involves four stages: (1) conducting surveys and interviews with MSME owners in the SKIES Indonesia community to identify their main challenges, (2) introducing the technology to MSME partners, (3) providing classical guidance through discussions, hands-on practices, and AI technology demonstrations, and (4) evaluating through Forum Group Discussions (FGD), questionnaires, and assessment forms. The outcomes of this community service indicate that MSME actors can understand the use of AI technology to improve the visual presentation of Betawi-style snacks and rice dishes. The application of AI contributes to creating innovative, attractive, and competitive packaging while fostering high and sustainable creativity.

Just a little human intelligence feedback! Unsupervised learning assisted supervised learning data poisoning based backdoor removal

Just a little human intelligence feedback! Unsupervised learning assisted supervised learning data poisoning based backdoor removal

Unveiling the EEG signatures of extrasensory perception during spiritual experiences: A single-case study with a well-renowned channeler.

Unveiling the EEG signatures of extrasensory perception during spiritual experiences: A single-case study with a well-renowned channeler.

An opportunity lost? The declining role of authorised covert human intelligence sources in combating organised crime

An opportunity lost? The declining role of authorised covert human intelligence sources in combating organised crime

Generative AI in Strategic Business Planning: Boosting Efficiency and Competitive Advantage in Business Organizations

Aim: This study investigates the application of generative AI in strategic business planning, with a particular focus on its potential to enhance operational efficiency and economic competitiveness in businesses organizations. Problem Statement: Before the advent of generative AI, businesses relied on traditional methods for routine tasks, business planning, and decision-making. These methods were often associated with significant challenges that hindered overall business efficiency. Significance of the Study: The application of generative AI plays a vital role in strategic planning and decision making of businesses. Recently, generative AI has transformed from a revolutionary concept to a transformative dynamism across various industries. This technology has shown incomparable competences in all ways. Methodology: This review article is based on an analysis of information gathered from high-impact journal publications. Discussion: This study critically examines the role of AI in strategic business planning and the techniques required for its effective implementation. Generative AI enhances competitiveness by facilitating product development and innovation, optimizing sales and marketing strategies, and enabling data-driven decision-making. However, several challenges must be addressed, including legal and ethical considerations, implementation and integration hurdles, and the need for continuous adaptation and learning. The future of generative AI in strategic business decisions is expected to include advancements in AI technologies, increased collaboration between AI and human intelligence, industry-specific AI applications, and the development of ethical AI governance frameworks. Businesses that integrate AI technologies—such as operational optimization, personalized customer interactions, and predictive analytics—stand to achieve significant improvements in customer satisfaction, innovation, and overall efficiency. Conclusion: The application of generative AI has demonstrated a positive impact on strategic business planning and has proven essential for businesses striving to remain competitive in dynamic markets. However, addressing the associated challenges is imperative to ensure the successful implementation and integration of AI into business strategies for sustainable growth and decision-making.

What tools for a governance of AI solutions to support Official Statistics?

Artificial Intelligence (AI) refers to the ability of computers to perform repetitive tasks that would usually require the presence of human intelligence. AI can become a powerful tool in support of Official Statistics, innovating traditional approaches to statistical information production processes throughout the entire data life cycle. To fully exploit the potential of AI initiatives, it is not enough to adopt advanced technology solutions but it becomes strategic to integrate them into a reliable, transparent and consistent framework. While the speed at which AI solutions are being developed augurs an increasingly rich future of opportunities, it must also find producers of official statistics ready not only to embrace the innovative stimuli but also to know how to guide and govern them to take into account the ethical demands that such a challenge entails. This translates into the need to adequately increase the skills required in the various areas affected by AI while stimulating a reflection on how to equip oneself with a standardized approach and processes to develop AI-based solutions that are in line with the agency's strategic objectives. Istat has activated several experimental projects for the use of artificial intelligence to support the production of statistical information. In parallel, through dedicated workshops, training moments and monitoring of the initiatives, it is pursuing a path to build a specific framework for the introduction of AI in the Institute, with the aim of aligning the ethical priorities of AI, ensuring its inclusiveness and sustainability. In this paper, we present the Institute's experience in the conception, development, and early AI-based use cases, highlighting its challenges and most significant milestones. We also illustrate how, through the integration of use cases into a reliable and effective monitoring and governance ecosystem, AI applications can be overseen to mitigate their risks without reducing their innovative scope.

Beyond the brush: Human versus artificial intelligence creativity in the realm of generative art.

Beyond the brush: Human versus artificial intelligence creativity in the realm of generative art.

Human-AI Collaboration in Workflow Optimization: A Framework for Hybrid Decision Systems in Automation-Heavy Industries

This article presents a comprehensive framework for human-AI collaborative workflow optimization in automation-heavy industries, addressing the limitations of fully automated approaches while leveraging the complementary strengths of human judgment and artificial intelligence. We introduce the Collaborative Workflow Intelligence Framework (CWIF), which establishes structured information flows and decision authority boundaries between human operators and AI components across manufacturing, logistics, and financial services domains. Through industry-specific applications, we demonstrate how this collaborative approach enhances production scheduling, quality control, supply chain efficiency, transportation optimization, and financial risk assessment while maintaining appropriate human oversight. Our methodology provides practical guidance for system architecture design, data integration, performance evaluation, and phased implementation, with particular attention to ethical considerations including worker autonomy and skills development. The framework balances operational efficiency with human expertise, creating systems that suggest process improvements and identify inefficiencies while preserving human decision authority in complex and consequential domains. This collaborative paradigm represents a significant advance over traditional automation approaches, offering organizations a path to workflow optimization that enhances rather than replaces human capabilities while addressing the technical, organizational, and ethical challenges of AI implementation.

Application of Resource Orchestration Theory in the Research of AI and HI to Achieve Value Creation

This paper grasps the research theme of artificial intelligence (AI) and human intelligence (HI) synergy to create value and analyzes the development status of AI and HI in the current context of digital intelligence, as well as the significance of their synergy to empower value creation. At the same time, the theory of resource arrangement is introduced, and the connotation and composition of the theory are summarized, as well as the development in the field of research and application. This paper focuses on revealing the intrinsic relationship between resource orchestration theory and AI and HI collaborative work, aiming to fully explore the potential of resource orchestration theory in the collaborative innovation of AI and HI, and put forward practical suggestions based on this.