Advances In Artificial Intelligence Technology Research Articles

Outsourcing, Augmenting, or Complicating: The Dynamics of AI in Fact-Checking Practices in the Nordics

The practice of fact-checking involves using technological tools to monitor online disinformation, gather information, and verify content. How do fact-checkers in the Nordic region engage with these technologies, especially artificial intelligence (AI) and generative AI (GAI) systems? Using the theory of affordances as an analytical framework for understanding the factors that influence technology adoption, this exploratory study draws on insights from interviews with 17 professionals from four Nordic fact-checking organizations. Results show that while AI technologies offer valuable functionalities, fact-checkers remain critical and cautious, particularly toward AI, due to concerns about accuracy and reliability. Despite acknowledging the potential of AI to augment human expertise and streamline specific tasks, these concerns limit its wider use. Nordic fact-checkers show openness to integrating advanced AI technology but emphasize the need for a collaborative approach that combines the strengths of both humans and AI. As a result, AI and GAI-based solutions are framed as “enablers” rather than comprehensive or end-to-end solutions, recognizing their limitations in replacing or augmenting complex human cognitive skills.

Advice from artificial intelligence: a review and practical implications.

Despite considerable behavioral and organizational research on advice from human advisors, and despite the increasing study of artificial intelligence (AI) in organizational research, workplace-related applications, and popular discourse, an interdisciplinary review of advice from AI (vs. human) advisors has yet to be undertaken. We argue that the increasing adoption of AI to augment human decision-making would benefit from a framework that can characterize such interactions. Thus, the current research invokes judgment and decision-making research on advice from human advisors and uses a conceptual "fit"-based model to: (1) summarize how the characteristics of the AI advisor, human decision-maker, and advice environment influence advice exchanges and outcomes (including informed speculation about the durability of such findings in light of rapid advances in AI technology), (2) delineate future research directions (along with specific predictions), and (3) provide practical implications involving the use of AI advice by human decision-makers in applied settings.

СВІТОВІ ТРЕНДИ У АВТОМОБІЛЕБУДІВНІЙ ГАЛУЗІ: УРОКИ ДЛЯ РОЗВИТКУ АВТОМОБІЛЕБУДУВАННЯ УКРАЇНИ В УМОВАХ ЕКОНОМІЧНОЇ ГЛОБАЛІЗАЦІЇ

The article demonstrates that the automotive industry is a significant component of the global economy due to its overall market volume, contribution to GDP, and the number of employed workers. The importance of the automotive industry has prompted an exploration of current global trends in the automotive industry. The trends identified include the development of 'green' technologies in the context of addressing climate change, the rise of 'green' protectionism, and the intensification of economic rivalry between China and Western countries. Other trends encompass the proliferation of autonomous vehicles based on the advancement of artificial intelligence technologies, the integration of cars into the broader network of connected devices within the Internet of Things and cloud technologies, the increased concentration of global value chains, the growth of the sharing economy, urbanization, and the development of urban transport. Furthermore, there is a heightened emphasis on the safety of vehicles, along with regulatory frameworks addressing these requirements. The emergence and development of individual flying devices as alternative modes of transportation are also noteworthy trends. With the aim of assessing the relevance of these trends for Ukraine and their implementation, manifestations of these trends in Ukraine have been evaluated. It has been shown that the most pronounced manifestations include the increased concentration of global value chains and urbanization along with the development of urban transport. To assess the prospects for further development of the examined trends in Ukraine, an evaluation of the readiness of the Ukrainian economy to implement these trends in the automotive industry is presented. The analysis reveals that the Ukrainian economy is most prepared for trends such as the development of 'green' technologies in the context of combating climate change and the growth of the sharing economy. It is shown that the trends in automotive engineering both present opportunities and pose threats to domestic car manufacturers. The composition of these opportunities and threats is outlined. Explored opportunities are mainly associated with the emergence of additional market segments, a stable demand pattern, and the creation of additional economic advantages. The presented threats are mostly linked to changes in demand structure and the insufficient competitiveness of Ukrainian automobile manufacturers in the global market. Factors limiting the opportunities of Ukrainian automotive manufacturers are identified, and the consequences of these factors are demonstrated, ultimately leading to the insufficient competitiveness of Ukrainian producers, an inability to scale production, and difficulties in technical upgrading. The results of the article are of interest for shaping the development of the automotive industry in Ukraine.

Technology Acceptance and Innovation Diffusion: Are Users More Inclined Toward AIGC-Assisted Design?

Artificial Intelligence Generated Content (AIGC) has shown significant potential in design, driven by advancements in artificial intelligence technology. However, understanding designers’ willingness to embrace this technology and the factors influencing their decision-making requires further research. In this study, we develop a theoretical model of user behavioral intention in AIGC-assisted design, drawing upon the Diffusion of Innovations theory and the Unified Theory of Acceptance and Use of Technology. Through empirical analysis using the PLS-SEM structural equation model, we investigate the mechanisms behind various influencing factors on behavioral intention. Our findings highlight the relative advantage as the most significant positive factor, emphasizing the importance of the innovation’s benefits in the Diffusion of Innovations theory. Designers prioritize the innovation and assistance provided by AIGC technology in the design process and ideas, recognizing the advantages of the innovation itself over mere performance improvement. This study provides valuable insights into the psychological and behavioral mechanisms guiding designers’ decision-making regarding the application of AIGC technology in design.

Research on the Recognition and Characterization of Fractures in Coal Rock Based on CT Scanning

Abstract Coal rock fractures play a vital role as key channels for gas migration and storage in the exploration and development of coalbed methane. The characteristics of these fractures, such as porosity distribution, specific surface area, and connectivity, directly impact the adsorption, diffusion, and permeability behaviors of coal reservoirs. Therefore, a detailed analysis of coal rock fracture characteristics is vital in assessing the extractability of coalbed methane and during its exploration and development process. Currently, techniques like imaging logging, seismic detection, and micro-CT are employed for characterizing and studying fractures at various scales. Among these, high-resolution non-destructive CT scanning technology, by constructing three-dimensional data models of core samples, enables a quantitative analysis of fracture distribution features and size parameters. Traditional digital image processing methods, such as threshold segmentation and binary segmentation, though advantageous in computational speed, have limitations in the accuracy of fracture identification. With the advancement of artificial intelligence technology, deep learning-based image processing techniques have increasingly become significant tools for fracture detection due to their powerful feature extraction capabilities. This study conducted micrometer-level CT scanning experiments on coal rock samples, using CT image processing technology to construct a digitalized coal rock core model. Deep learning methods were applied for precise fracture identification, establishing an appropriate deep learning architecture and optimized parameters for coal rock fracture recognition. Furthermore, this paper provides a detailed quantitative characterization of the precisely identified fractures, offering robust technical support for the exploration and development of coalbed methane. The high-resolution non-destructive CT scanning technology and three-dimensional fracture network reconstruction method used in this study have been proven to be effective tools for investigating the micro-features of rocks, enabling the three-dimensional visualization and detailed characterization of rock microstructures.

NAVIGATION IN E-GOVERNMENT: THE ROLE OF ARTIFICIAL INTELLIGENCE IN THE FORMATION OF THE LEGAL FRAMEWORK FOR THE PROTECTION OF INTELLECTUAL PROPERTY RIGHTS

The integration of artificial intelligence (AI) into electronic government (e-government) systems is revolutionizing public administration by enhancing efficiency and improving service delivery. However, the adoption of AI technologies in this context also raises complex legal challenges, particularly concerning intellectual property (IP) rights. Traditional IP laws, which were developed with human authorship in mind, struggle to accommodate the unique characteristics of AI-generated content. This article examines how AI is reshaping the legal framework for IP protection within e-government systems, highlighting the implications and challenges that arise from this technological shift. The primary aim of this study is to explore the role of AI in the formation of IP law frameworks within e-government, focusing on how current laws address—or fail to address—the challenges of AI-generated content. The methodology includes a comprehensive literature review, analysis of legislative documents, case studies, and a benchmarking analysis to compare approaches across jurisdictions. Additionally, expert interviews provide insights into practical considerations and emerging trends in the field. The results indicate that while some jurisdictions, such as the European Union, are actively adapting their IP laws to address AI's impact, most existing frameworks remain inadequate for protecting AI-generated works. Divergent approaches across countries reveal a lack of international harmonization, which complicates cross-border collaboration and legal enforcement. The analysis also highlights the importance of public-private partnerships and sector-specific IP protections, which can address the unique needs of different e-government applications. From a forward-looking perspective, the study underscores the need for flexible, AI-specific IP protections that promote innovation while safeguarding IP rights. International cooperation will be essential for establishing consistent standards, facilitating global e-government initiatives, and supporting the responsible use of AI in public services. By fostering a balanced and adaptive IP framework, policymakers and stakeholders can help build a resilient digital ecosystem that accommodates future advancements in AI technology.

Novel Approach to Protect Red Revolutionary Heritage Based on Artificial Intelligence Algorithm and Image-Processing Technology

The red revolutionary heritage is a valuable part of China’s historical and cultural legacy, with the potential to generate economic benefits through its thoughtful development. However, challenges such as insufficient understanding, lack of comprehensive planning and layout, and limited protection and utilization methods hinder the full realization of the political, cultural, and economic value of red heritage. To address these problems, this paper thoroughly examines the current state of red revolutionary heritage protection and identifies the problems within the preservation process. Moreover, it proposes leveraging advanced artificial intelligence (AI) technology to repair some damaged image data. Specifically, this paper introduces a red revolutionary cultural relic image-restoration model based on a generative adversarial network (GAN). This model was trained using samples of damaged image and utilizes high-quality models to restore these images effectively. The study also integrates real-world revolutionary heritage images for practical application and assesses its effectiveness through questionnaire surveys. The survey results show that AI algorithms and image-processing technologies hold significant potential in the protection of revolutionary heritage.

Artificial Intelligence: Future Aspects in the Pharmaceutical Industry an Overview

Artificial intelligence (AI) has emerged as a potent tool leveraging human-like knowledge to offer swift solutions to intricate challenges. Striking advancements in AI technology and machine learning present a revolutionary opportunity in pharmaceutical drug discovery, formulation, and dosage form testing. By employing AI algorithms that scrutinize vast biological datasets encompassing genomics and proteomics, scientists can pinpoint disease-related targets and forecast their interactions with potential drug candidates. This facilitates a more precise and efficient approach to drug discovery, thereby elevating the chances of successful drug approvals. Moreover, AI holds the potential to curtail development costs by streamlining research and development processes. Machine learning algorithms aid in experimental design and can foresee the pharmacokinetics and toxicity of drug candidates, allowing for the prioritization and refinement of lead compounds, thereby reducing the necessity for extensive and expensive animal testing. Personalized medicine initiatives can be advanced through AI algorithms analyzing real-world patient data, culminating in more efficacious treatment outcomes and enhanced patient compliance. This comprehensive overview delves into the diverse applications of AI in pharmaceutical drug discovery, dosage form design for drug delivery, process refinement, testing, and pharmacokinetics/pharmacodynamics (PK/PD) investigations. It provides a glimpse into various AI-driven methodologies employed in pharmaceutical technology, shedding light on their advantages and limitations. Nonetheless, sustained investments in and exploration of AI within the pharmaceutical sector present promising avenues for enhancing drug development processes and patient care.

Toward Digital Periodontal Health: Recent Advances and Future Perspectives.

Periodontal diseases, ranging from gingivitis to periodontitis, are prevalent oral diseases affecting over 50% of the global population. These diseases arise from infections and inflammation of the gums and supporting bones, significantly impacting oral health. The established link between periodontal diseases and systemic diseases, such as cardiovascular diseases, underscores their importance as a public health concern. Consequently, the early detection and prevention of periodontal diseases have become critical objectives in healthcare, particularly through the integration of advanced artificial intelligence (AI) technologies. This paper aims to bridge the gap between clinical practices and cutting-edge technologies by providing a comprehensive review of current research. We examine the identification of causative factors, disease progression, and the role of AI in enhancing early detection and treatment. Our goal is to underscore the importance of early intervention in improving patient outcomes and to stimulate further interest among researchers, bioengineers, and AI specialists in the ongoing exploration of AI applications in periodontal disease diagnosis.

Artificial Neural Network Approach for Analysis and Prediction of Solar Radiation and Wind Speed in Telecommunication Systems

The use of renewable energy sources in the power grid is essential for maintaining economic growth, especially for many African countries. Therefore, these countries can also install advanced artificial intelligence technology in their airport’s power system to capture renewable energy. The paper aims to propose a real-time power management algorithm for a distributed hybrid renewable energy grid. It continues with the network modeling with the integration of solar and wind power. A multi-objective approach is proposed for the maximization of the spatial spectral efficiency and the energy efficiency as a function of the number of wind turbines and solar photovoltaic panels. Radio parameters for a Mobile Wireless inter-operability Medium Access (WiMAX) technology are considered for the simulation in MATLAB software. The results are quite mitigated but theoretically promising for the integration of green energy into the current telecommunication systems

A Survey on Penetration Path Planning in Automated Penetration Testing

Penetration Testing (PT) is an effective proactive security technique that simulates hacker attacks to identify vulnerabilities in networks or systems. However, traditional PT relies on specialized experience and costs extraordinary time and effort. With the advancement of artificial intelligence technologies, automated PT has emerged as a promising solution, attracting attention from researchers increasingly. In automated PT, penetration path planning is a core task that involves selecting the optimal attack paths to maximize the overall efficiency and success rate of the testing process. Recent years have seen significant progress in the field of penetration path planning, with diverse methods being proposed. This survey aims to comprehensively examine and summarize the research findings in this domain. Our work first outlines the background and challenges of penetration path planning and establishes the framework for research methods. It then provides a detailed analysis of existing studies from three key aspects: penetration path planning models, penetration path planning methods, and simulation environments. Finally, this survey offers insights into the future development trends of penetration path planning in PT. This paper aims to provide comprehensive references for academia and industry, promoting further research and application of automated PT path planning methods.

The Ethical Limitation of Using Artificial Intelligence (AI) in Teaching Prophetic Tradition

This research explored the relationship between Prophetic traditions and artificial intelligence (AI), focusing on the ethical implications and dynamics of human-AI interactions in the context of hadith teachings in the postmodern technological era. Involving an in-depth analysis of the Prophet's teachings regarding science and technology, this research aims to detail ethical perspectives that can guide the development and use of artificial intelligence. With an interdisciplinary approach, this research combined religious values with developments in AI technology, discussing the confluence between the traditions of the Prophet and current dynamics in the world of technology. Ethical implications from an Islamic hadith perspective were detailed, highlighting how moral and human values could be integrated into designing and implementing AI technologies. Additionally, this research explored the impact of human-AI interactions in the postmodern era, considering how AI technology could support the development of a society based on the Prophet's teachings and values. A comprehensive understanding of artificial intelligence's limitations, risks, and human responsibilities was also detailed to balance technological progress and moral values. Hopefully, the research results will contribute to a better understanding of the relationship between religious traditions, especially hadith teachings, and advances in AI technology, providing a solid ethical foundation for technological development in the postmodern era

Artificial Intelligence-Driven Diagnostic Processes and Comprehensive Multimodal Models in Pain Medicine.

Pain diagnosis remains a challenging task due to its subjective nature, the variability in pain expression among individuals, and the difficult assessment of the underlying biopsychosocial factors. In this complex scenario, artificial intelligence (AI) can offer the potential to enhance diagnostic accuracy, predict treatment outcomes, and personalize pain management strategies. This review aims to dissect the current literature on computer-aided diagnosis methods. It also discusses how AI-driven diagnostic strategies can be integrated into multimodal models that combine various data sources, such as facial expression analysis, neuroimaging, and physiological signals, with advanced AI techniques. Despite the significant advancements in AI technology, its widespread adoption in clinical settings faces crucial challenges. The main issues are ethical considerations related to patient privacy, biases, and the lack of reliability and generalizability. Furthermore, there is a need for high-quality real-world validation and the development of standardized protocols and policies to guide the implementation of these technologies in diverse clinical settings.

Entrepreneurship and Corporate ESG Performance—A Case Study of China’s A-Share Listed Companies

This paper examines the contemporary implications of entrepreneurship and utilizes panel data from Chinese A-share listed companies spanning 2011 to 2022. Based on the five aspects of Chinese entrepreneurship, namely “patriotism, courage to innovate, integrity and law-abiding, social responsibility, and international vision”, the findings suggest that fostering entrepreneurship enhances the environmental, social, and governance (ESG) performance of firms. Mechanism analysis indicates that green technology innovation, social performance enhancement, and governance capability optimization mediate this relationship. Furthermore, factors such as corporate market power, regional marketization processes, and advancements in artificial intelligence technology influence the link between entrepreneurship and ESG performance. Robust entrepreneurship equips firms to navigate environmental uncertainties, but entrepreneurship cannot improve corporate governance performance. This article elucidates the distinctive significance of entrepreneurship, expanding the institutional economics research perspective, offering practical insights for cultivating entrepreneurship and elucidating potential determinants of corporate ESG performance. This article also provides spiritual guidance for sustainable development.

The Influence of Artificial Intelligence on the Automation of Processes in Electronic Commerce

This study explores the transformative impact of Artificial Intelligence (AI) on automating business processes in electronic commerce (e-commerce), with a focus on enhancing efficiency and customer experience. The research employs Deep Learning (DL) and Machine Learning (ML) as primary analytical tools to process and analyze data from e-commerce transaction records and customers’ browsing histories. Techniques such as data preprocessing, normalization, sentiment analysis, and advanced predictive models using Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs) are utilized. Data collection was conducted using web scraping tools like Beautiful Soup and Scrapy, along with APIs from Amazon and Google. The application of AI in e-commerce has led to significant improvements in inventory control, fraud prevention, and customer relations. ML algorithms have enhanced the estimation of product demand and personalized customer interactions, while DL has strengthened product recommendation systems and fraud detection mechanisms. The findings indicate that AI contributes to a more secure, faster, and smarter operational environment in e-commerce. This research highlights the substantial benefits and broad potential of AI in optimizing e-commerce operations, demonstrating that the integration of advanced AI technologies not only streamlines transactions but also reinforces platforms against fraudulent activities.

A SWOT analysis of generative AI in applied linguistics: Leveraging strengths, addressing weaknesses, seizing opportunities, and mitigating threats

Background The integration of generative Artificial Intelligence (AI), particularly large language models (LLMs) like ChatGPT and Gemini, into the field of applied linguistics presents transformative opportunities alongside notable challenges. This study aims to evaluate the role of AI in applied linguistics through a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis. Methods Using a sample derived from Scopus and Web of Science, we identified relevant studies by applying specific search terms. Our qualitative research design used the SWOT framework to systematically review and analyse studies, ensuring a robust synthesis of data. Results The results of our SWOT analysis revealed the following: 1) Strengths: Enhanced educational tools and resources through AI-driven personalization and interactive learning, increased efficiency and accessibility in generating educational materials, and innovative research applications leveraging semantic similarity measures and advanced linguistic analyses. 2) Weaknesses: Quality and accuracy concerns in AI-generated content, potential over-dependence on AI tools leading to diminished student creativity and ethical issues, and technical limitations in handling complex academic writing tasks. 3) Opportunities: Potential for educational innovation and pedagogical integration, advancements in AI technology to support linguistic research, and fostering global collaboration and access through AI tools. 4) Threats: Risks to academic integrity due to AI-generated content, technological and implementation challenges, and privacy and security concerns regarding data handling. Conclusions Based on the SWOT analysis, we introduced a strategic plan to maximize strengths and opportunities while addressing weaknesses and threats. The strategy includes promoting personalized learning through AI tools, streamlining the creation of educational materials, fostering innovative research applications, ensuring human oversight to maintain content quality, developing ethical guidelines to prevent misuse, investing in necessary infrastructure and training, and implementing robust data protection measures.

Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions.

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and the need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. Addressing these challenges and fostering collaboration among AI experts, clinicians, and researchers is crucial for developing equitable and effective AI applications. The future of AI in HNC holds significant promise, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes.

A comprehensive review of model compression techniques in machine learning

This paper critically examines model compression techniques within the machine learning (ML) domain, emphasizing their role in enhancing model efficiency for deployment in resource-constrained environments, such as mobile devices, edge computing, and Internet of Things (IoT) systems. By systematically exploring compression techniques and lightweight design architectures, it is provided a comprehensive understanding of their operational contexts and effectiveness. The synthesis of these strategies reveals a dynamic interplay between model performance and computational demand, highlighting the balance required for optimal application. As machine learning (ML) models grow increasingly complex and data-intensive, the demand for computational resources and memory has surged accordingly. This escalation presents significant challenges for the deployment of artificial intelligence (AI) systems in real-world applications, particularly where hardware capabilities are limited. Therefore, model compression techniques are not merely advantageous but essential for ensuring that these models can be utilized across various domains, maintaining high performance without prohibitive resource requirements. Furthermore, this review underscores the importance of model compression in sustainable artificial intelligence (AI) development. The introduction of hybrid methods, which combine multiple compression techniques, promises to deliver superior performance and efficiency. Additionally, the development of intelligent frameworks capable of selecting the most appropriate compression strategy based on specific application needs is crucial for advancing the field. The practical examples and engineering applications discussed demonstrate the real-world impact of these techniques. By optimizing the balance between model complexity and computational efficiency, model compression ensures that the advancements in AI technology remain sustainable and widely applicable. This comprehensive review thus contributes to the academic discourse and guides innovative solutions for efficient and responsible machine learning practices, paving the way for future advancements in the field.Graphical abstract

Mapping the Landscape of VFX and AI: A PRISMA-guided Systematic Review

Objective: This systematic literature review aims to comprehensively synthesize existing evidence on Visual Effects (VFX) and Artificial Intelligence (AI) using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The primary objective is to map the landscape of VFX and AI, examining the historical development of VFX techniques and practices in the context of AI advancements. Methods: Following PRISMA guidelines, a thorough search of five electronic databases—IEEE Xplore, Scopus, Taylor & Francis Online, ACM Digital Library, and ScienceDirect—was conducted to identify articles published up to April 18, 2024. Inclusion criteria encompassed English-language journal articles. Independent reviewers screened articles for eligibility, resolving discrepancies through consensus. Data extraction and synthesis were performed systematically. Results: A total of 41 studies met the inclusion criteria and were included in the review. These studies covered a wide range of topics related to VFX and AI, with various study designs. The findings are presented in a narrative synthesis. Discussion: The review indicates that AI development in VFX is comprehensive, covering topics such as image processing, inpainting, video processing, 3D modelling, rendering, deepfake technology, style transfer, tracking, animation, simulation, rigging, texture compression, and depth map enhancement. These advancements highlight the transformative role of AI in enhancing creative expression, production efficiency, and the quality of visual effects. Conclusion: This systematic literature review provides a comprehensive overview of the current state of knowledge on AI and VFX. The integration of AI into VFX is profoundly transforming the industry, enhancing creative expression, production efficiency, and the quality of visual effects. Continued advancements in AI technologies promise to further revolutionize VFX practices, offering new possibilities and efficiencies for future productions.

Hippocampal Cognitive Function Based on Deep Learning

Abstract This research focuses on the study of agent behavior decision-making based on hippocampal cognitive functions, aiming to enhance the decision-making capabilities of agents in complex task environments by deeply exploring the crucial role of the hippocampus in learning, memory, and cognitive processes. By drawing inspiration from the biological structure and functional characteristics of the hippocampus, researchers are dedicated to designing and developing more intelligent and adaptive decision-making models to enhance agents' behavioral performance, problem-solving abilities, and adaptability to new situations. To achieve this goal, the research integrates advanced artificial intelligence technologies such as reinforcement learning and deep learning to simulate the complex functions of the hippocampus in memory encoding, storage, retrieval, and cognitive reasoning. This research not only contributes to advancing intelligent systems towards higher levels of intelligence and personalization but also plays a significant role in improving the interaction between intelligent agents and humans, providing intelligent services that better meet user needs. We found that the neural network trained in multi-task learning benefits from a loss term that promotes relevant and irrelevant representations. Therefore, the complementary coding we found in CA3 can provide extensive computational advantages for solving complex tasks. Furthermore, the study emphasizes the importance of further elucidating the functional mechanisms of the hippocampus, with the expectation of providing a more solid theoretical foundation for the optimization and refinement of agent decision-making models in the future.