Human Machine Intelligence Research Articles

Cognitive Models for Machine Theory of Mind

AbstractSome of the required characteristics for a true machine theory of mind (MToM) include the ability to (1) reproduce the full diversity of human thought and behavior, (2) develop a personalized model of an individual with very limited data, and (3) provide an explanation for behavioral predictions grounded in the cognitive processes of the individual. We propose that a certain class of cognitive models provide an approach that is well suited to meeting those requirements. Being grounded in a mechanistic framework like a cognitive architecture such as ACT‐R naturally fulfills the third requirement by mapping behavior to cognitive mechanisms. Exploiting a modeling paradigm such as instance‐based learning accounts for the first requirement by reflecting variations in individual experience into a diversity of behavior. Mechanisms such as knowledge tracing and model tracing allow a specific run of the cognitive model to be aligned with a given individual behavior trace, fulfilling the second requirement. We illustrate these principles with a cognitive model of decision‐making in a search and rescue task in the Minecraft simulation environment. We demonstrate that cognitive models personalized to individual human players can provide the MToM capability to optimize artificial intelligence agents by diagnosing the underlying causes of observed human behavior, projecting the future effects of potential interventions, and managing the adaptive process of shaping human behavior. Examples of the inputs provided by such analytic cognitive agents include predictions of cognitive load, probability of error, estimates of player self‐efficacy, and trust calibration. Finally, we discuss implications for future research and applications to collective human–machine intelligence.

Advancing Collective Intelligence in Human–AI Collaboration: Foundations for the COHUMAIN Framework

Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capabilities in many ways, how can we ensure that the sociotechnical system as a whole—comprising a complex web of hundreds of human–machine interactions—is exhibiting collective intelligence? Research on human–machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Integrating these diverse perspectives and methods is crucial at this juncture. To truly advance our understanding of this important and rapidly evolving area, we need frameworks to facilitate research that bridges disciplinary boundaries. This paper advocates for establishing an interdisciplinary research domain—Collective Human-Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. To illustrate the approach we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, which articulates the critical processes underlying the emergence and functioning of collective intelligence in human–AI collaborations.

University 4.0: Contours of Post-Industrial Manifestations In The Optics Of The Triple Helix Model

Relevance. Universities, as institutional organizations, are subject to the greatest reform in the transforming matrix of goals, values and norms of building the Future. In this regard, the problem of conceptualizing the ultra-new model of the University of the fourth generation (4.0) is the most relevant for science and social sciences.The purpose of this study is to consider and analyze the conceptual foundations reflected in the scientific manifestations of the formation of the fourth generation University model (4.0) as a subject of the “triple helix".Objectives. To analyze the concepts, ideologems and other units of the theoretical basis of the University 4.0 model presented in the scientific media space; to reflect the emerging concept of University 4.0 in the optics of the “triple helix” model, implemented in the context of the formation of a post-industrial society.Methodology. The study was carried out on the basis of synergetic principles of complexity theory, general provisions of system theory and interinstitutional and transinstitutional communication using system-structural and convergent network methods.Results. Today, new ontological continuums of social multi-subject practices are objectively “unfolding” based on the ethics of subjective integration and synergy of individual and collective, as well as machine and hybrid intelligences. Flexible models of collective management of the future should be synthesized on such university cognitive platforms 4.0, where even transinstitutional forms of relations, such as the “triple helix”, begin to be perceived as tools, rather than platforms for the implementation of new ideas and solutions that overcome historical and cultural predestination. The platform of the “cognitive turn of postindustriality” is the Universities of the fourth generation.Conclusions. The platform of the "cognitive turn of postindustriality" in the future is the Universities of the fourth generation. Then, in university activities, existing traditional subject (for example, institutional) ontologies act only as a tool for generating on their basis “packages” of understanding posteconomics in the spirit of “meta-subject forms of thinking”, such as collective, including hybrid human-machine intelligence. New ethics (bio-, techno-, etc.) are also being institutionalized and progressing.

Hybrid Intelligence Systems Combining Human Expertise and AI/RPA for Complex Problem Solving

Hybrid Intelligence Systems (HIS) represent a paradigm shift in problem-solving methodologies by integrating human expertise with Artificial Intelligence (AI) and Robotic Process Automation (RPA). This paper explores the mechanisms, applications, benefits, challenges, and future directions of HIS in the context of complex problem-solving. Through collaborative synergies between human cognition and machine intelligence, HIS enhances decision-making accuracy, efficiency, and innovation. Human experts contribute domain knowledge, contextual understanding, and ethical reasoning, while AI algorithms and RPA systems offer data-driven insights, computational power, and process automation capabilities. HIS fosters inclusivity, diversity, and democratization in problem-solving processes by harnessing the collective intelligence of diverse teams and stimulating interdisciplinary collaboration. However, challenges such as privacy concerns, data security risks, and algorithmic biases must be addressed to realize the full potential of HIS. Looking ahead, the integration of Explainable AI (XAI), Edge AI, and Neuro symbolic AI holds promise for enhancing transparency, interpretability, and robustness in HIS architectures. Human-centered design principles and interdisciplinary research collaborations will shape the development and deployment of HIS, ensuring alignment with human values, preferences, and needs. Ultimately, HIS will continue to serve as a beacon of collaboration, creativity, and collective intelligence in shaping a better world for generations to come.

CfCV: Towards algorithmic debiasing in machine learning experiment

Algorithmic failure in application leading to unfairness or bias typically results from data-inconsistent, diversity biases, inclusion biases, and under-representation, among many others, leading to imbalances in representation for training and validation. Cross-validation (CV) techniques are relevant in addressing this inconsistency from training to validation point. As a result, the essence of CV is to validate algorithm abilities to predict new data that are not part of the training set and to prompt issues such as overfitting or selection bias. This study considered the linkage of Inclusion, Participation, and Reciprocity (IPR) in data splitting for a sensitive attribute and caters for population grouping representativeness in splitting. The study remodified the Pre-In-Post (P-I-P) processing approach to accommodate various sensitive attribute levels within any training set and performed simulation experiments and real-life applications. It then conducted a comparative performance analysis with the two most notable CV techniques. The study innovative approach (CfCV) outperformed the existing CVs - Vfold and HoldOut; in experiments [RMSECfCV = 0.88; RMSEVfold = 0.98; RMSEHoldout = 4.69 | AccuracyCfCV = 99.75%; AccuracyVfold = 99.50%; AccuracyHoldOut = 85.50%] and applications [RMSECfCV = 0.59; RMSEVfold = 1.61; RMSEHoldout = 1.96 | AccuracyCfCV = 84%; AccuracyVfold = 81%; AccuracyHoldOut = 52%]. This study recommends the adoption of IPR in data splitting for machine experiments built for human-machine intelligence systems and concludes that machine learning experiments would be fairer if the concept of IPR formed the foundation of the human-machine intelligence framework.

AI AND IMAGINATION: BRIDGING THE GAP BETWEEN CREATIVITY AND MACHINES

Abstract—The convergence of artificial intelligence and human imagination is reshaping the creative landscape. This exploration examines how AI has evolved from its traditional role to become a catalyst for innovation. Through advanced generative models and neural networks, AI goes beyond automation, allowing creativity to flourish without limitations. However, ethical considerations and societal implications arise as AI-infused imagination explores new frontiers in artistry and problem-solving. The evolving partnership between human ingenuity and machine intelligence opens up collaborative opportunities that unveil exciting possibilities in creativity and innovation. Keywords—Artificial Intelligence, Human imagination, neural networks, generative model, automation.

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

The cybersecurity concerns get increasingly intricate as the digital world progresses. In light of the increasing complexity of cyber threats, it is imperative to develop and implement advanced and flexible security strategies. Machine Learning (ML) has become a potent tool in strengthening cybersecurity, providing the capacity to scrutinise extensive information, recognise trends, and improve threat detection and defence methods. This paper examines the significance of ML in the field of cybersecurity, with a special emphasis on the identification of threats and the implementation of protective measures. By incorporating ML algorithms into cybersecurity frameworks, organisations may automate decision-making processes, facilitating prompt responses to ever-changing threats. The initial segment explores the terrain of cyber threats, highlighting the necessity for dynamic and aggressive security methods. Conventional solutions that rely on signatures are frequently inadequate when it comes to handling sophisticated, shape-shifting attacks. ML algorithms, in contrast, have exceptional proficiency in identifying nuanced patterns and irregularities within extensive datasets, therefore offering a more efficient method of detecting potential threats. The second section delves into several ML methodologies utilised in cybersecurity, including supervised and unsupervised learning, deep learning, and reinforcement learning. Every approach is assessed based on its suitability for threat detection, demonstrating its advantages and constraints. Furthermore, the relevance of feature engineering and data pretreatment in improving machine learning models for cybersecurity applications. The versatility of ML algorithms allows them to grow with emerging threats, making them a useful tool in the ever-changing arena of cyber warfare. The final segment focuses on real-world applications of machine learning in cybersecurity, presenting successful use cases across sectors. From anomaly detection to behavior analysis, ML algorithms contribute to the discovery of dangerous activity, lowering false positives and strengthening the overall security posture. Lastly, the paper covers the obstacles and ethical issues related to the adoption of ML in cybersecurity. Issues like as adversarial assaults, skewed datasets, and the interpretability of ML models are examined, highlighting the necessity for a holistic strategy that integrates modern technology with ethical considerations. The fusion of human expertise and machine intelligence offers a formidable defense against evolving cyber threats, paving the way for a more resilient and secure digital future.

Improving Information Security with Machine Learning

The study Improving Information Security with Machine Learning explores the fusion of machine learning methodologies within information security, aiming to fortify conventional protocols against evolving cyber threats. By conducting a comprehensive literature review and empirical analysis, this scholarly endeavor highlights the efficacy of machine learning in anomaly detection, threat identification, and predictive analytics within security frameworks. Through practical demonstrations, such as z-score-based anomaly detection in network traffic data and NLP-based email security systems, the study illustrates the practical applications of machine learning techniques. Additionally, it delves into the mathematical underpinnings of predictive analytics and the architecture of neural networks for malware detection. However, while showcasing the transformative potential of machine learning, the study also confronts significant challenges. Ethical, legal, and privacy considerations emerge prominently, emphasizing the need for regulations addressing algorithmic biases, ethical dilemmas, and data protection. Moreover, the study emphasizes the practical challenges of scalability, interpretability, continual adaptation to evolving threats, and the harmonious interaction between human expertise and machine intelligence. By offering practical recommendations and future research directions, this scholarly exploration aims to empower researchers, practitioners, and policymakers in navigating the complex intersection of machine learning and information security, thereby fostering innovation and comprehension in this evolving domain.

Redefining Cognitive Domains in the Era of ChatGPT: A Comprehensive Analysis of Artificial Intelligence's Influence and Future Implications.

Despite its extensive utilization, research on Chat Generative Pre-trained Transformer (ChatGPT)'s potential negative impact on specific cognitive processes is scarce. This article explores the widespread use of ChatGPT in educational, corporate, and various other sectors, focusing on its interaction with distinct cognitive domains such as attention, executive function, language, memory, visuospatial abilities, and social cognition. A literature review was conducted using PubMed, identifying 256 articles, with 29 peer-reviewed articles analyzed after screening for relevance. The review emphasizes the extraordinary capabilities of the human brain, which often go unrecognized, and argues for the importance of maintaining and enhancing natural cognitive abilities using artificial intelligence tools like ChatGPT as an aid rather than a replacement. The findings highlight the advanced reasoning capabilities of ChatGPT, blending intuitive and deliberate cognitive processes. Building a socio-cognitive architecture for collective human-machine intelligence has significant potential. While ChatGPT offers impressive capabilities, over-reliance on it for cognitive tasks can lead to the erosion of essential skills. It is crucial to find a balance between leveraging artificial intelligence's advantages and preserving our natural cognitive abilities, ensuring continuous practice and engagement in traditional cognitive exercises.

Research progress of brain-computer interface application paradigms based on rapid serial visual presentation

Rapid serial visual presentation (RSVP) is a type of psychological visual stimulation experimental paradigm that requires participants to identify target stimuli presented continuously in a stream of stimuli composed of numbers, letters, words, images, and so on at the same spatial location, allowing them to discern a large amount of information in a short period of time. The RSVP-based brain-computer interface (BCI) can not only be widely used in scenarios such as assistive interaction and information reading, but also has the advantages of stability and high efficiency, which has become one of the common techniques for human-machine intelligence fusion. In recent years, brain-controlled spellers, image recognition and mind games are the most popular fields of RSVP-BCI research. Therefore, aiming to provide reference and new ideas for RSVP-BCI related research, this paper reviewed the paradigm design and system performance optimization of RSVP-BCI in these three fields. It also looks ahead to its potential applications in cutting-edge fields such as entertainment, clinical medicine, and special military operations.

Revolutionizing Dental Imaging: A Comprehensive Study on the Integration of Artificial Intelligence in Dental and Maxillofacial Radiology.

Recent advancements in deep learning and artificial intelligence (AI) have profoundly impacted various fields, including diagnostic imaging. Integrating AI technologies such as deep learning and convolutional neural networks has the potential to drastically improve diagnostic methods in the fieldof dentistry and maxillofacial radiography. A systematic study that adhered to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards was carried out to examine the efficacy and uses of AI in dentistry and maxillofacial radiography. Incorporating cohort studies, case-control studies, and randomized clinical trials, the study used an interdisciplinary methodology. A thorough search spanning peer-reviewed research papers from 2009 to 2023 was done in databases including MEDLINE/PubMed and EMBASE. The inclusion criteria were original clinical research in English that employed AI models to recognize anatomical components in oral and maxillofacial pictures, identify anomalies, and diagnose disorders. The study looked at numerous research that used cutting-edge technology to show how accurate and dependable dental imaging is. Among the tasks covered by these investigations were age estimation, periapical lesion detection, segmentation of maxillary structures, assessment of dentofacial abnormalities, and segmentation of the mandibular canal. The study revealed important developments in the precise definition of anatomical structures and the identification of diseases. The use of AI technology in dental imaging marks a revolutionary development that will usher in a time of unmatched accuracy and effectiveness. These technologies have not only improved diagnostic accuracy and enabled early disease detection but have also streamlined intricate procedures, significantly enhancing patient outcomes. The symbiotic collaboration between human expertise and machine intelligence promises a future of more sophisticated and empathetic oral healthcare.

Constructing Ethical AI Based on the “Human-in-the-Loop” System

The Human-in-the-Loop (HITL) system was first proposed by Robert Monarch, a machine learning expert. It adopted a “hybrid” strategy combining human intelligence and machine intelligence, aiming to improve the accuracy of machine learning models and assist human learning. At present, there have been a number ethical design attempts based on the HITL system, and some progress has been made in the ethical choices of disaster rescue robots and nursing robots. However, there is no analysis of why the HITL system can serve as an effective path in constructing ethical AI and how it can implement the efficiency of AI in ethical scenarios. This paper draws on the feasibility of the HITL system and analyzes how ethical AIs are possible when using the HITL system. We advocate for its application to the entire process of ethical AI design.

IndusSynthe: Synthetic data using human-machine intelligence hybrid for enhanced industrial surface defect detection through self-updating with multi-view filtering

Artificial intelligence (AI) has made significant strides in automating defect detection for industrial products. However, its real-time application in manufacturing, especially with limited data, is not extensively explored. Current AI systems predominantly focus on post hoc analysis, encountering challenges of data scarcity, diversity, and scalability of unknown data. In this study, we proposed a novel method, IndusSynthe, which is a zero-data detection method with self-updating using human–machine intelligence hybrid (HMIH) and multi-view filtering (MVF). Firstly, generate diverse data enriched with expert knowledge, achieving the model training in zero-data scenarios. Subsequently, an MVF strategy is employed as a self-updating learning mechanism, addressing challenge associated with declining model accuracy in dynamic information environments. In subsequent iterations, only this second phase is executed to ensure a consistent and stable self-updating process. Two emblematic industrial datasets were employed to validate the efficacy and feasibility of the proposed method. Experimental outcomes indicate that the method enables initial training in a zero-data environment by integrating human–machine knowledge, negating the need for extensive auxiliary data. Furthermore, it empowers machines to autonomously handle self-screening and updating in dynamic industrial settings, thereby establishing robust and efficient anomaly detection mechanisms. The proposed method expedites the development and assessment of an intelligent system tailored for industrial surface defect detection, it offers a comprehensive solution, from inception to application, for incorporating AI technologies in industrial manufacturing. We envisage that this method may inspire future research avenues and broaden its relevance to additional vision-centric tasks.

Understanding Collective Intelligence: Investigating the Role of Collective Memory, Attention, and Reasoning Processes

As society has come to rely on groups and technology to address many of its most challenging problems, there is a growing need to understand how technology-enabled, distributed, and dynamic collectives can be designed to solve a wide range of problems over time in the face of complex and changing environmental conditions—an ability we define as “collective intelligence.” We describe recent research on the Transaction Systems Model of Collective Intelligence (TSM-CI) that integrates literature from diverse areas of psychology to conceptualize the underpinnings of collective intelligence. The TSM-CI articulates the development and mutual adaptation of transactive memory, transactive attention, and transactive reasoning systems that together support the emergence and maintenance of collective intelligence. We also review related research on computational indicators of transactive-system functioning based on collaborative process behaviors that enable agent-based teammates to diagnose and potentially intervene to address developing issues. We conclude by discussing future directions in developing the TSM-CI to support research on developing collective human-machine intelligence and to identify ways to design technology to enhance it.

Self-beliefs, Transactive Memory Systems, and Collective Identification in Teams: Articulating the Socio-Cognitive Underpinnings of COHUMAIN.

Socio-cognitive theory conceptualizes individual contributors as both enactors of cognitive processes and targets of a social context's determinative influences. The present research investigates how contributors' metacognition or self-beliefs, combine with others' views of themselves to inform collective team states related to learning about other agents (i.e., transactive memory systems) and forming social attachments with other agents (i.e., collective team identification), both important teamwork states that have implications for team collective intelligence. We test the predictions in a longitudinal study with 78 teams. Additionally, we provide interview data from industry experts in human-artificial intelligence teams. Our findings contribute to an emerging socio-cognitive architecture for COllective HUman-MAchine INtelligence (i.e., COHUMAIN) by articulating its underpinnings in individual and collective cognition and metacognition. Our resulting model has implications for the critical inputs necessary to design and enable a higher level of integration of human and machine teammates.

Fostering Collective Intelligence in Human-AI Collaboration: Laying the Groundwork for COHUMAIN.

Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human-machine interactions, is exhibiting collective intelligence? Research on human-machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Bringing together these different perspectives and methods at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries. This paper advocates for establishing an interdisciplinary research domain-Collective Human-Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. In illustrating the kind of approach, we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, that articulates the critical processes underlying the emergence and maintenance of collective intelligence and extend it to human-AI systems. We connect this with synergistic work on a compatible cognitive architecture, instance-based learning theory and apply it to the design of AI agents that collaborate with humans. We present this work as a call to researchers working on related questions to not only engage with our proposal but also develop their own sociocognitive architectures and unlock the real potential of human-machine intelligence.

Info-Autopoiesis and the Limits of Artificial General Intelligence

Recent developments, begun by the ascending spiral of the anticipated endless prospects of ChatGPT, promote artificial intelligence (AI) as an indispensable tool and commodity whose time has come. Yet the sinister specter of a technology that has hidden and unmanageable attributes that might be harmful to society looms in the background, as well as the likelihood that it will never deliver on the purported promise of artificial general intelligence (AGI). Currently, the prospects for the development of AI and AGI are more a matter of opinion than based on a consistent methodological approach. Thus, there is a need to take a step back to develop a general framework from which to evaluate current AI efforts, which also permits the determination of the limits to its future prospects as AGI. To gain insight into the development of a general framework, a key question needs to be resolved: what is the connection between human intelligence and machine intelligence? This is the question that needs a response because humans are at the center of AI creation and realize that, without an understanding of how we become what we become, we have no chance of finding a solution. This work proposes info-autopoiesis, the self-referential, recursive, and interactive process of self-production of information, as the needed general framework. Info-autopoiesis shows how the key ingredient of information is fundamental to an insightful resolution to this crucial question and allows predictions as to the present and future of AGI.

Multitask-Oriented Brain-Controlled Intelligent Vehicle Based on Human–Machine Intelligence Integration

Brain-controlled intelligent vehicles (BCIVs) refer to intelligent vehicles, where brain-computer interfaces (BCIs) are applied to help a person operate (or teleoperate) a vehicle by decoding human intention from brain signals. Existing studies on BCIVs are focused on the single-task operation scenario. Considering that the multitask operation is common in practice, in this article, we design a multitask-oriented BCIV system for the first time by integrating a novel neural decoding method of driver-secondary-task intention with an adaptive brain–machine collaborative controller. We build an experimental platform of the proposed multitask-oriented BCIV system and test the performance of both the primary and secondary tasks by human-and-hardware-in-the-loop experiments. Experimental results show that the proposed multitask-oriented BCIV system performs well. This work has essential values in moving the exploration of brain-controlled systems toward a new step of the multitask operation and opens a new avenue for cognitive neuroscience to be applied to intelligent systems and human–machine integration.

The Inner Loop of Collective Human-Machine Intelligence.

With the rise of artificial intelligence (AI) and the desire to ensure that such machines work well with humans, it is essential for AI systems to actively model their human teammates, a capability referred to as Machine Theory of Mind (MToM). In this paper, we introduce the inner loop of human-machine teaming expressed as communication with MToM capability. We present three different approaches to MToM: (1) constructing models of human inference with well-validated psychological theories and empirical measurements; (2) modeling human as a copy of the AI; and (3) incorporating well-documented domain knowledge about human behavior into the above two approaches. We offer a formal language for machine communication and MToM, where each term has a clear mechanistic interpretation. We exemplify the overarching formalism and the specific approaches in two concrete example scenarios. Related work that demonstrates these approaches is highlighted along the way. The formalism, examples, and empirical support provide a holistic picture of the inner loop of human-machine teaming as a foundational building block of collective human-machine intelligence.

Cognitive approach to the formation of a robotic system in education

The article deals with the features of modeling systems for managing educational processes using robot assistants based on a cognitive approach. The main objective of the study is the use of hybrid human-machine intelligence in the education system. The authors propose a new version of the use of fuzzy cognitive maps and the capabilities of the corresponding software systems to solve a number of practical problems using examples of automated learning. Within the framework of the cognitive approach, which allows to take into account the various linguistic features of people, it is possible to use appropriate fuzzy cognitive maps that allow you to determine cause-and-effect networks that reflect the subjective view of researchers about the system in the form of a set of semantic categories called factors or concepts.The study shows that taking into account the NON-factors that take place in the educational process, as well as the possibilities of their application for the formation of individual learning trajectories in the context of the use of human-machine intelligence, it is possible to obtain fundamentally new opportunities for training personnel.The authors propose a fuzzy cognitive model of strategic management with the participation of a robotic system, identify the main concepts for its construction and analysis. The parameters of the cognitive map are identified using the principles of intelligent generation of the best alternatives based on the modified method of pair comparisons. The study is aimed at employees of educational institutions responsible for the digitalization of education and teachers.