Integrating Cognitive Principles From ACT-R Into Probabilistic Conditional Reasoning by Taking the Example of Maximum Entropy Reasoning

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

The quest to develop Artificial Intelligence (AI) systems that possess human-like consciousness necessitates a deep dive into both theoretical and practical aspects underpinning this ambitious goal. This article builds on initial philosophical explorations of AI consciousness by examining the intricate and often hidden structures that may facilitate conscious experiences in AI. Drawing from concepts in cognitive science and neuroscience, the article elucidates how AI systems can be designed to replicate the structural and functional aspects of human consciousness. The discussion includes the Hierarchy of Spatial Belongings proposed by Forti (2024), frameworks like the Integrated Information Theory (IIT), and models linking consciousness with metacognitive processes. Through case studies of advanced AI systems such as IBM Watson, AlphaGo, GPT-3, and Sophia the Robot, the article explores practical implementations and their alignment with theoretical models of consciousness. The potential for AI to achieve states analogous to human consciousness raises profound ethical, societal, and legal considerations. Ethical guidelines and legal frameworks are urgently needed to address the moral status and rights of conscious AI systems, ensure their ethical treatment, and delineate accountability. The societal impacts of conscious AI, including job displacement and the need for equitable access to AI technologies, are also examined. Future research directions highlight the necessity for developing sophisticated theoretical models, enhancing practical implementations, establishing comprehensive ethical frameworks, fostering interdisciplinary collaboration, and engaging the public. By addressing these areas, the scientific community can significantly advance the development of conscious AI, ensuring it is both technically feasible and ethically sound

The major AI problems that arise in designing a consultation program involve choices of knowledge representations, diagnostic interpretation strategies, and treatment planning strategies. The need to justify decisions and update the knowledge base in the light of new research findings places a premium on the modularity of a representation and the ease with which its reasoning procedures can be explained. In both diagnosis and treatment decisions, the relative advantages and disadvantages of different schemes for quantifying the uncertainty of inferences raises difficult issues of a formal logical nature, as well as many specific practical problems of system design. An important insight that has resulted from the design of several artificial intelligence systems is that robustness of performance in the presence of many uncertainty relationships can be achieved by eliciting from the expert a segmentation of knowledge that will also provide a rich network of deterministic relationships to interweave the space of hypotheses.

Artificial Intelligence (AI) systems involve diverse components, such as data, models, users and predicted outcomes. To elucidate these different aspects of AI systems, multifaceted explanations that combine diverse explainable AI (XAI) methods are beneficial. However, popularly adopted user-centric XAI evaluation methods do not measure these explanations across the different components of the system. In this position paper, we advocate for an approach tailored to evaluate XAI methods considering the diverse dimensions of explainability within AI systems using a normalised scale. We argue that the prevalent user-centric evaluation methods fall short of facilitating meaningful comparisons across different types of XAI methodologies. Moreover, we discuss the potential advantages of adopting a standardised approach, which would enable comprehensive evaluations of explainability across systems. By considering various dimensions of explainability, such as data, model, predictions, and target users, a standardised evaluation approach promises to facilitate both inter-system and intra-system comparisons for user-centric AI systems.

With the constant development of computer technology and the increasing complexity of software, the issues of efficient generation of program code are becoming more and more important. The use of modern models and methods of artificial intelligence (AI) to automate the process of creating program code is one of the promising areas of research in the field of software engineering. With the advent and development of neural networks and deep learning, the possibilities for automating the process of creating program code have expanded significantly. In addition, the growing complexity of software and the increased requirements for its quality and performance emphasize the relevance of researching the possibilities of using modern AI methods to generate program code. The purpose of the work is to analyze the quality of application of AI models in the generation of program code intended for the development of complex software systems by students of the “Software Engineering” training direction. In the course of the study, the subject area related to the application of the capabilities of modern AI models to generate program code was studied. Various approaches and technologies of AI were considered, as well as their use in the training of future software engineers. The study included the organization of queries to the ChatGPT-4o, Copilot, Google-gemini-1.5-pro, and YandexGPT models and subsequent comparative analysis of the software codes generated by these models. For sending and subsequent processing of requests, an automated client-server application was developed, which was used in the study of the potential of AI models for generating program code. The results obtained can be useful for teachers and students of the direction of training “Software Engineering”, as well as for software developers using AI models to automate programming processes and improve the quality of professional training.

Artificial intelligence (AI) systems are increasingly being used in consumer as well and business applications. Companies are using them, Governments are using them, public security, medical researchers, you and I – everybody seems to be somehow touched. As it becomes more advanced and ubiquitous in our lives, it is critical that AI developers and users understand the ethical implications and challenges posed by this powerful technology. This paper explores the key ethical skills and considerations that are essential for navigating the new world of AI. Artificial Intelligence (AI) presents significant ethical dimensions across various sectors, including research, business, and daily life. It is important to appreciate the major ethical dimensions of AI and how they impact AI systems. Policies and guidelines needs to be laid out for development and usage of such systems. Automated Decisions/Responsibility: No matter how intelligent a AI system might perceived to be, it is always prone to making errors. So, AI system that make automated decisions will always be dangerous. Therefore, it is important to ensure that there is always "human intervention" or "human review" for outcomes/decisions made by AI systems. Determining accountability for AI-generated actions or content remains a significant challenge, emphasizing the need for ethical governance frameworks. There has to be human accountability.Fairness/Bias: AI systems can perpetuate existing biases present in training data, leading to discriminatory outcomes.Transparency: The opaque nature of many AI algorithms complicates accountability, making it difficult to understand decision-making processes.Privacy: AI's capability to process vast datasets can infringe on individual privacy rights, necessitating stringent data protection measuresHuman Dignity: The risk of AI-generated content resembling existing works raises ethical issues regarding originality and authorship.

Although the categorization of ultrasound using the Breast Imaging Reporting and Data System (BI-RADS) has become widespread worldwide, the problem of inter-observer variability remains. To maintain uniformity in diagnostic accuracy, we have developed a novel artificial intelligence (AI) system in which AI can distinguish whether a static image obtained using a breast ultrasound represents BI-RADS3 or lower, or BI-RADS4a or higher, to determine the medical management that should be performed on a patient whose breast ultrasound shows abnormalities. To establish and validate the AI system, a training dataset consisting of 4,028 images containing 5,014 lesions and a test dataset consisting of 3,166 images containing 3,656 lesions were collected and annotated. We selected a setting that maximized the area under the curve (AUC) and minimized the difference in sensitivity and specificity by adjusting the internal parameters of the AI system, achieving an AUC, sensitivity, and specificity of 0.95, 90.0%, and 88.5%, respectively. Furthermore, based on 30 images extracted from the test data, the diagnostic accuracy of 20 clinicians and the AI system was compared, and the AI system was found to be significantly superior to the clinicians (McNemar test, p < 0.001). Then, we conducted a trial to introduce the system for use in clinical practice. Physicians reviewed the images and determined whether they were BI-RADS3 or lower, or BI-RADS4a or higher. Next, the classification was performed again for the same images concerning the AI diagnosis. At this time, the initial judgment was allowed to be overturned. We checked whether there was any difference in the diagnostic accuracy, sensitivity, and specificity before and after reviewing to the AI diagnosis. Reviews by 24 physicians were evaluated: 4 Japanese Breast Cancer Society breast specialists, 5 non-specialists and physicians with experience treating more than 40 cases of breast cancer, and 15 non-specialists and physicians with no experience treating more than 40 cases of breast cancer. The average rate of accuracy before confirming the AI diagnosis increased to 73.1% after confirming the AI diagnosis (p=0.00548), compared to 69.3% on average before the AI diagnosis. Compared to practice experience, the accuracy increased from an average of 77.1% to 79.6% for the 9 physicians who were breast specialists or who had treated 40 or more cases of breast cancer. For the 15 physicians with less than 40 breast cancer cases, the average rate of accuracy increased from 64.7% to 69.2%. Furthermore, sensitivity increased significantly to an average of 99.7% after reviewing of the AI diagnosis from an average of 88.8% prior to reviewing the AI-diagnosis.(p< 0.01). Specificity increased from an average of 62.4% to 63.8% (p=0.433) after reviewing AI diagnosis. We showed that our AI system, when applied to clinical practice and used by physicians, contributes to the improvement of diagnostic accuracy. Our results indicated that our AI diagnostic system was sufficiently accurate to be used in the clinical practice. Citation Format: Erina Odani, Tetsu Hayashida, masayuki kikuchi, Aiko Nagayama, tomoko seki, maiko takahashi, Akiko Matsumoto, Takeshi Murata, Rurina Watanuki, Takamichi Yokoe, Ayako Nakashoji, Hinako Maeda, Tatsuya Onishi, Sota Asaga, Takashi Hojo, Hiromitsu Jinno, Keiichi Sotome, Akira Matsui, Akihiko Suto, Shigeru Imoto, Yuko Kitagawa. Establishment of the breast ultrasound support system using deep-learning system [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P1-05-06.

Artificial intelligence emerged from human thinking that has both logical and intuitive or subjective sides. The logical side has been developed and utilized, resulting advanced von Neumann type computers and expert systems, both constituting the hard computing domain. However, it is found that hard computing can’t give the solution of very complicated problems by itself. In order to cope with this difficulty, the intuitive and subjective thinking of human mind was explored, resulting the soft computing domain (also called computational intelligence). It includes neural networks, fuzzy logic and probabilistic reasoning, the last gathering evolutionary computation (including genetic algorithms with related efforts in genetic programming and classifier systems, evolution strategies and evolutionary programming), immune networks, chaos computing and parts of learning theory. In different kind of applications, all pure artificial intelligence methods mentioned above proved to be rather complementary than competitive, so that combined methods appeared in order to gather the advantages and to cope with the disadvantages of each pure method. The scope of this chapter is to study and finaly compare some representative classes of pure and combined computational intelligence methods applied in engine knock detection.

In this study, we leverage the fusion of edge computing, artificial intelligence (AI) methods, and facilities provided by B5G to build a heterogeneous set of AI techniques for COVID-19 outbreak prediction. Advancement in the areas of AI, edge computing, the Internet of Things (IoT), and fast communication networks provided by beyond 5G (B5G) networks has opened doors for new possibilities by fusing these technologies and techniques. In a pandemic outbreak, such as COVID-19, the need for rapid analysis, decision making, and prediction of future trends becomes paramount. On a global map, the distributed processing and analysis of data at the source is now possible and much more efficient. With the features provided by B5G, such as low latency, larger area coverage, higher data rate, and realtime communication, building new intelligent and efficient frameworks is becoming easier. In this study, our aim is to achieve higher accuracy in prediction by fusing multiple AI methods and leveraging the B5G communication architecture. We propose a distributed architecture for training AI methods on edge devices, with the results of edge-trained models then propagated to a central cloud AI method, which then combines all the received edge-trained models into a global and final prediction model. The experimental results of five countries (United States, India, Italy, Bangladesh, and Saudi Arabia) show that the proposed distributed AI on edges can predict COVID-19 outbreak better than that of each individual AI method in terms of correlation coefficient scores.

Artificial General Intelligence is the idea that someday an hypothetical agent will arise from artificial intelligence (AI) progresses, and will surpass by far the brightest and most gifted human minds. This idea has been around since the early development of AI. Since then, scenarios on how such AI may behave towards humans have been the subject of many fictional and research works. This paper analyzes the current state of artificial intelligence progresses, and how the current AI race with the ever faster release of impressive new AI methods (that can deceive humans, outperform them at tasks we thought impossible to tackle by AI a mere decade ago, and that disrupt the job market) have raised concerns that Artificial General Intelligence (AGI) might be coming faster that we thought. In particular, we focus on 3 specific families of modern AIs to develop the idea that deep neural networks, which are the current backbone of nearly all artificial intelligence methods, are poor candidates for any AGI to arise due to their many limitations, and therefore that any threat coming from the recent AI race does not lie in AGI but in the limitations, uses, and lack of regulations of our current models and algorithms. This article appears in the AI & Society track.

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study

In this chapter, we discuss, in depth, visual information processing and a new artificial intelligent (AI) system that is based upon cognitive mechanisms. The relationship between a general model of intelligent systems and cognitive mechanisms is described, and in particular we explore visual information processing with selective attention. We also discuss a methodology for studying the new AI system and propose some important basic research issues that have emerged in the intersecting fields of cognitive science and information science. To this end, a new scheme for associative memory and a new architecture for an AI system with attractors of chaos are addressed.

Artificial intelligence (AI) and Human Computer Interaction (HCI) share common roots and early work on conversational agents has laid the foundation for both fields. However, in subsequent decades the initial tight connection between the fields has become less pronounced. The recent rise of deep learning has revolutionized AI and has led to a raft of practical methods and tools that significantly impact areas outside of core-AI. In particular, modern AI techniques now power new ways for machines and humans to interact. Thus it is timely to investigate how modern AI can propel HCI research in new ways and how HCI research can help direct AI developments. This workshop offers a forum for researchers to discuss new opportunities that lie in bringing modern AI methods into HCI research, identifying important problems to investigate, showcasing computational and scientific methods that can be applied, and sharing datasets and tools that are already available or proposing those that should be further developed. The topics we are interested in including deep learning methods for understanding and modeling human behaviors and enabling new interaction modalities, hybrid intelligence that combine human and machine intelligence to solve difficult tasks, and tools and methods for interaction data curation and large-scale data-driven design. At the core of these topics, we want to start the conversation on how data-driven and data-centric approaches of modern AI can impact HCI.

Background: Enumeration of CTCs in blood samples with the CELLSEARCH® system is a prognostic biomarker in metastatic breast, prostate and colorectal cancer (full intended use documents.cellsearchctc.com). Currently, the final CTC identification is performed by human reviewers through visual assessment, which is a time-consuming procedure that could be affected by subjective interpretations. Modern Artificial Intelligence (AI) methods can provide a solution by automating the process of CTC identification, thus eliminating subjectivity and producing faster and more reproducible results. We have recently presented an automated algorithm (for research use only) based on deep learning models for cell segmentation and classification which allows the automated identification of CTCs in CELLSEARCH® fluorescent images (Ansaloni et al., EACR 2022). The purpose of this study was to assess inter-rater variability among human reviewers and to compare the AI performance against them. Methods: Blood samples derived from metastatic cancer patients were processed with CELLTRACKS® AUTOPREP®. The AI algorithm was trained on fluorescent images of 7255 CTCs and 32876 non-CTC events (training set) acquired by CELLTRACKS ANALYZER II® (CTAII) from 90 breast, 122 prostate and 54 colorectal cancer samples. 10 human reviewers, qualified for CTC image analysis, performed blind labeling of a separate dataset independently (test set: 55 samples taken from 27 breast and 28 prostate cancer). We studied inter-rater agreement and variability using Fleiss Kappa and Coefficient of Variation (CV). The labels provided by 5 experienced reviewers (average experience ≅ 15 years) were used to generate the ground truth (GT) by majority voting. The CTC detection performance of the AI was ranked against the other 5 reviewers (average experience ≅ 5 years). Results: In the test dataset, 2990 out of 9554 events were identified as CTCs by the GT review on CTAII images. In the task of classifying each event as CTC or non-CTC, the inter-rater agreement among the 10 human reviewers measured by Fleiss Kappa was 0.85. These classification results provided the number of CTCs per patient counted by each reviewer. The inter-rater variability on the CTC enumeration among the 10 reviewers was evaluated for each sample (median CV = 17.9% and interquartile range = 17.6% , excluding 27 samples with mode = 0 CTC). Measured against the GT classification, the AI performance (accuracy = 94.7%; F1 = 91.6%) was at the top of the human reviewers’ ranking (accuracy = 92.4 - 93.6 - 93.8 - 94.0 - 95.8%; F1 = 86.8 - 89.2 - 89.2 - 89.5 - 93.1%). Conclusion: The AI algorithm reached top-ranking performance in CTC identification, surpassing 4 out of 5 expert reviewers. These results show the applicability of AI to cell classification to remove human subjectivity from the review process and to maximize standardization among different research centers. Citation Format: Luca Biasiolli, Pietro Ansaloni, Nicolò Gentili, Ramona Miserendino, Chiara Rossi, Giulio Signorini, Gianni Medoro. Deep learning for circulating tumor cell (CTC) identification with the CELLSEARCH system: achieving top human-level performance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3373.

Background:The training of plastic surgery residents is constantly evolving. This study explored whether different artificial intelligence (AI) systems highlight the same key aspects of residency training as experts in the field.Methods:Six modern AI systems (ChatGPT 3.5, ChatGPT 4o, Claude 3.5, Gemini 1.5 Pro, Llama 3.1 70B, and OpenAI o1) were tested on various aspects of residency training. Their responses were evaluated for accuracy, quality, and comprehensiveness both by the AI systems and by field experts.Results:All AI systems delivered accurate and comprehensive responses. OpenAI o1 and ChatGPT 4o consistently ranked highest, nearly achieving excellent scores in all categories. Although some systems addressed certain aspects more superficially, all identified most of the key elements and provided a solid overview of important topics in residency training.Conclusions:OpenAI o1 and ChatGPT 4o stood out for their accuracy and depth, emphasizing the potential of AI in medical education. AI may offer new opportunities for personalized and efficient surgical training.