Artificial Intelligence Problems Research Articles

SGO: An innovative oversampling approach for imbalanced datasets using SVM and genetic algorithms

Imbalanced datasets present a challenging problem in machine learning and artificial intelligence. Since most models typically assume balanced data distributions, imbalanced positive and negative examples can lead to significant bias in prediction or classification tasks. Current over-sampling methods frequently encounter issues like overfitting and boundary bias. A novel imbalanced data augmentation technique called SVM-GA over-sampling (SGO) is proposed in this paper, which integrates Support Vector Machines (SVM) with Genetic Algorithms (GA). Our approach leverages SVM to identify the decision boundary and uses GA to generate new minority samples along this boundary, effectively addressing both over-fitting and boundary biases. It has been experimentally validated that SGO outperforms the traditional methods on most datasets, providing a novel and effective approach to address imbalanced data problems, with potential application prospects and generalization value.

Automatically designing counterfactual regret minimization algorithms for solving imperfect-information games

Strategic decision-making in imperfect-information games is an important problem in artificial intelligence. Counterfactual regret minimization (CFR), a family of iterative algorithms, has been the workhorse for solving these types of games since its inception. In recent years, a series of novel CFR variants have been proposed, significantly improving the convergence rate of vanilla CFR. However, most of these new variants are hand-designed by researchers through trial and error, often based on different motivations, which generally requires a tremendous amount of effort and insight. This work proposes AutoCFR, a systematic framework that meta-learns novel CFR algorithms through evolution, easing the burden of manual algorithm design. We first design a search language that is rich enough to represent various CFR variants. We then exploit a scalable regularized evolution algorithm with a set of acceleration techniques to efficiently search over the combinatorial space of algorithms defined by this language. The learned novel CFR algorithm can generalize to new imperfect-information games not seen during training and performs on par with or better than existing state-of-the-art CFR variants. In addition to superior empirical performance, we also theoretically show that the learned algorithm converges to an approximate Nash equilibrium. Extensive experiments across diverse imperfect-information games highlight the scalability, extensibility, and generalizability of AutoCFR, establishing it as a general-purpose framework for solving imperfect-information games.

The wizard of artificial intelligence: Are physicians prepared?

BACKGROUND: Despite the increasing use of artificial intelligence (AI) in medicine, research into the knowledge and attitudes of medical experts toward AI is limited. This study aimed to assess physicians’ attitudes and perceptions of AI applications in healthcare. MATERIALS AND METHODS: A cross-sectional study was conducted at the College of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia between November 1 and December 20, 2023. Data was collected using a web-based validated self-administered questionnaire; information sought included demographics, familiarity with AI technology, attitudes towards AI applications in medicine, and perceived risks associated with its implementation. Data analysis performed using SPSS. Categorical variables were presented as frequencies and percentages, whereas mean and standard deviations were calculated for continuous variables. Four domains were used as study variables: familiarity, attitude, AI application, and possible risks. The domains were compared to the scored individual question using Pearson’s correlation coefficient. RESULTS: A total of 128 academic staff participated (response rate 21.3%). About 41% reported being aware of AI as an emerging field and expressed desire to learn about AI. Concerns about the effect of AI on physicians’ employability were not prominent. Instead, most (65.6%) agreed that new positions would be created and that the job market for those who embraced AI would increase. Thirty-two percent reported actively seeking out new technologies in their practice areas, and 24.2% would be willing to adapt AI to practice if its usefulness was published in scientific journals. Most participants (87.5%) agreed that dedicated courses would help them implement AI in their specialty. The most commonly reported problem of AI was its inability to provide opinions in unexpected scenarios. A quarter of participants believed that both AI creators and doctors should be liable for AI-caused medical errors. Physician’s age, gender, or years of experience were not related to their familiarity with or attitudes toward AI. CONCLUSION: Doctors are open to learning about AI use in medicine. Including AI learning objectives or short courses in medical curricula would help physicians develop the necessary skills for an AI-augmented healthcare system.

Влияние нарушений качества рентгенографических исследований органов грудной клетки на работу врачей-рентгенологов и диагностического искусственного интеллекта.

When performing radiographic studies, quality violations and errors periodically occur, making it difficult for an image to be interpreted and described by both a radiologist and software based on artificial intelligence technology. Incorrect filling of meta-information in DICOM format headers may be a separate problem for diagnostic artificial intelligence. Purpose: determination of quality violations of the chest x-ray examinations, which most strongly impede the work of radiologists and diagnostic software based on artificial intelligence in the conditions of the Moscow city health care system. Material and methods. To study the impact of the quality of x-ray examinations on the work of a radiologist, an online survey was conducted among radiologists, employees of the Moscow reference center for radiation diagnostics. To study the impact of the quality of x-ray examinations on the work of AI-based software, an analysis of the results of processing diagnostic studies according to the modalities chest x-ray and lung fluorography for the 4th quarter of 2023 by artificial intelligence services was carried out as part of an “Experiment on the use of innovative computer vision technologies for medical image analysis and subsequent applicability in the healthcare system of Moscow”. Results. The survey involved 172 radiologists. The most common quality violations by doctors are incompleteness of chest coverage and asymmetric position of the patient, as well as violations of image clarity / contrast. At the same time, the incompleteness of the chest coverage causes the greatest difficulties in interpreting the study for doctors with up to 5 years of experience. For more experienced doctors, the greatest difficulties are caused by violations of the clarity and contrast of images. The main problems hindering the description of studies by AI-services are non-filling or incorrect filling of meta-information about the study stored in DICOM format, as well as violations of the patient’s laying and positioning. Conclusion. The problems we have identified indicate the need for more careful adherence to the methodology of conducting diagnostic studies, especially with regard to filling out meta-information about the study. AI-based software developers need to evaluate the performance of their solutions based on the developed testing and monitoring methodology.

“What could go wrong?”: An evaluation of ethical foresight analysis as a tool to identify problems of AI in libraries

Artificial intelligence (AI) has entered libraries in various ways and raised concern about its potential ethical consequences therein. A number of approaches have been developed to encourage ethical AI and audit the ethics of specific AI applications, but very few approaches have been applied or tested, especially in a library setting, and so it remains unclear which, if any approaches are suitable or useful for encouraging ethical AI in libraries. We applied Ethical Foresight Analysis as an approach to identify possible ethical risks of an AI project for (semi-)automated subject indexing in a large research library. Specifically, to identify risks we conducted a two-round ethical Delphi study wherein experts on AI development, library practices, and AI ethics sought consensus on potential risks and their relative importance. The experts' post-test reflections on the procedure were then collected to inform an evaluation of the approach's feasibility. A variety of ethical risks of the specific project and of general AI indexing were indeed identified, most notably discrimination and under-representation stemming from attributes of the bibliographic training data provided by the library (e.g. varied historical contexts and gaps left by unindexed items). However, we identified some drawbacks of the approach tested: (1) it is time-consuming, which is likely prohibitive for many libraries, and (2) the identified risks were mainly well-known issues of AI and its training data rather than the subtle, application-specific, and human-centred issues that ethical foresight analysis might be employed to identify. Thus, although libraries should continue to model ethical AI through careful planning and auditing, alternative development and auditing approaches may be more practical to undertake and more effective at identifying novel or application-specific issues.

Data or mathematics? Solutions to semantic problems in artificial intelligence

Data or mathematics? Solutions to semantic problems in artificial intelligence

Research status and prospect of the application of artificial intelligence in the acupuncture and moxibustion field based on bibliometric

To explore the research hotspot, development trend and existing problem of artificial intelligence (AI) application in the field of acupuncture and moxibustion by using bibliometric method. Relevant articles of AI application in the field of acupuncture and moxibustion published in CNKI, Wanfang, VIP, PubMed and Web of Science from the database establishment to September 17, 2023 were searched. Excel 2019, CiteSpace 6.2.R4 and VOSviewer 1.6.19 were used to draw visual map of the number of publication, authors, research institutions and keywords, and further analyzed the research hotspot and trend. A total of 443 Chinese articles and 68 English articles were included. The number of annual publication of Chinese articles showed an overall increasing trend, while the number of publication of English articles was less, with a growing trend from 2020. Keywords analysis showed that "Machine learning", "neural network", "deep learning", "data mining", "robot" and other AI technologies were developing around acupuncture and moxibustion diagnosis and treatment of disease, efficacy prediction, teaching and intelligent equipment development. However, the research on the application of AI in the field of acupuncture and moxibustion is in the preliminary stage of development, and in the future, it is necessary to strengthen the communication and cooperation among the teams, to further explore the AI system in line with the characteristics of acupuncture and moxibustion diagnosis and treatment, and to promote the development of the digitalisation, intellectualisation and industrialisation of acupuncture and moxibustion.

Measuring Heuristic Accuracy on the Performance of Search Algorithms in Solving 8-Puzzle Problems

The goal of this paper is to examine the effect of heuristic accuracy on the performance of search algorithms in solving 8-puzzle problems. The 8-puzzle is a popular benchmark search problem in Artificial Intelligence, and numerous search strategies have been developed to solve it. This research evaluates the performance of search algorithms using different heuristics to determine the optimal level of heuristic accuracy for solving 8-puzzle problems. Several search algorithms, including uniform cost search and A* search, were implemented and tested using different heuristics. Manhattan and Euclidean distances were chosen as heuristics for A* algorithm. Heuristics play a crucial role in guiding these algorithms towards the solution, and their accuracy can greatly impact the performance of the search. Results revealed that reduction attained in both time and space complexity by A* search with Manhattan heuristic is over 99.1% for the average case, while the reduction in the effective branching factor is over 21.1%. These results from the experiments provide insights into the trade-off between heuristic accuracy and search efficiency, and contribute to a better understanding of the behavior of search algorithms in complex problems

Measuring Disentanglement: A Review of Metrics.

Learning to disentangle and represent factors of variation in data is an important problem in artificial intelligence. While many advances have been made to learn these representations, it is still unclear how to quantify disentanglement. While several metrics exist, little is known on their implicit assumptions, what they truly measure, and their limits. In consequence, it is difficult to interpret results when comparing different representations. In this work, we survey supervised disentanglement metrics and thoroughly analyze them. We propose a new taxonomy in which all metrics fall into one of the three families: intervention-based, predictor-based, and information-based. We conduct extensive experiments in which we isolate properties of disentangled representations, allowing stratified comparison along several axes. From our experiment results and analysis, we provide insights on relations between disentangled representation properties. Finally, we share guidelines on how to measure disentanglement.

Ethics of AI in medicine

Artificial intelligence (AI) is increasingly finding its way into medicine, and it is not yet clear how it will change the practice of medicine and the way doctors see themselves. This article explores the ethical limits of AI by (1) discussing the reductionistic elements inherent in AI, (2) working out the problematic implications of algorithmisation and (3) highlighting the lack of human control as an ethical problem of AI. The conclusion is that although AI is a useful tool to support medical judgement, it is absolutely dependent on human decision-making authority in order to actually prove beneficial for medicine.

An Exploratory Study on the Application and Impact of Artificial Intelligence Technology in China’s Film and Television Industry: Centered on the Innovation and Development Strategy of AI in Film Production

Purpose - This paper discusses the historical traceability of artificial intelligence technology and its development lineage in the film and television industry. It analyzes the close connection between AI technology and the film and television industry. The text summarizes the challenges and impacts of AI technology on the film and television industry, along with suggestions for future development. Design/Methodology/Approach - In order to explore the impact of artificial intelligence on the film and television culture industry, this paper analyzes the impact of artificial intelligence on the film and television culture industry from the perspective of macro-industry and the perspective of application technology, starting from the overall technological impetus of artificial intelligence to the film and television culture industry and the application of artificial intelligence in film and television image processing. The case was analyzed in detail. Based on a summary of previous research, this study identifies key issues and proposes solutions. Findings - Through the research of this paper, the problems of artificial intelligence in film and television creation are summarized, and the potential problems are easily generated technical dependence, legal risks in creation, and reduced professional value of practitioners, etc. Countermeasure suggestions are put forward to break the technical dependence, provide a legal basis for the creation of artificial intelligence, and adjust the use of the positioning to realize the synergy between man and machine. Research Implications - This paper aims to enhance the application of AI in the film and television culture industry, thereby increasing the industry vitality of the film and television sector.

A Note on the Application of Identity Matrix

Objectives: The Identity Matrix is an important notion in linear algebra with various applications across computational and mathematical domains. The objective of the paper is to introduce a novel method for transposing matrices by utilizing the properties of Identity Matrix as agent. Method: In the proposed method, the input matrix performs Sum of Product (SOP) operations with the Identity Matrix gives the transpose. This involves describing specific mathematical operations that exploit the properties of the Identity Matrix to achieve matrix transpose. Findings: The results show that the Identity Matrix can be used to transpose binary, non- binary and complex matrices. The paper explores results obtained through empirical studies. The result shows that the algorithm runs in polynomial time. Novelty: The case study explores the application of the Identity Matrix for transposing the binary, non- binary and complex matrices with the support of examples and implementation. The uniqueness of the method is its competence to transpose matrix by operating the Sum of Product (SOP) structure. The method of matrix transpose by briefing Identity Matrix as the agent can be applied to develop a way of research to address artificial intelligence problems. Keywords: Agent­ Based Simulation, Binary Matrix, Complex Matrix, Identity Matrix, Matrix Transpose, Sum of Product

Heuristics in philosophy

This article argues that heuristics play a key role in philosophy, in generating both our verdicts on proposed counterexamples to philosophical theories and philosophical paradoxes. Heuristics are efficient ways of answering questions, quick and easy to use, but imperfectly reliable. They have been studied by psychologists and cognitive scientists such as Gigerenzer and Kahneman, but their relevance to philosophical methodology has not been properly recognized. Several heuristics are discussed at length. The persistence heuristic can be summarized in the slogan ‘Small changes don’t matter’. Without it, updating would present an intractable problem for both natural and artificial intelligence. But our reliance on the persistence heuristic also makes us vulnerable to paradoxes of vagueness. Disquotational heuristics of various kinds are considered. They play central roles in our ascriptions of truth, falsity, and belief, but they also generate semantic paradoxes such as the Liar and Frege puzzles about coreference. The use of an additive heuristic for combining reasons is also discussed. Our reliance on fallible heuristics in philosophy does not make philosophical knowledge impossible, just as our reliance on fallible heuristics in perception does not make perceptual knowledge impossible. Nevertheless, it should motivate us to take a more critical attitude to our data. By identifying and analyzing the heuristics on which we rely, we may be able to work out where they make us most vulnerable to error.

Artificial sociality in the light of old and new theoretical and methodological approaches

The article critically analyzes the problems of artificial intelligence (AI) and artificial sociality (AS) from several theoretical and methodological positions. First, it is shown that the concept of IS actualizes extra-subjective approaches to understanding social ontology and is comparable with a number of concepts developed in the history of sociology, in particular, with P. Sorokin’s “conductors” (“mediums”). In this respect, AI is similar to other cultural (“artificial”) phenomena. Second, in the perspective of the Slavophile concept of “integral knowledge”, the limited epistemological horizons of AI as “rational cognition” are scrutinized. Third, sociological interpretations of AI that do not provide clarity in understanding its agentic status in social interactions (“participant” or “mediator”) are subjected to revision. Fourth, it is argued that a sociologically correct formulation of the IS question should focus not on natural or artificial agents of sociality, but on forms of sociality as ways of implementing social order. Today’s social reality, so the author of this paper, has not led so far to destruction of old and emergence of fundamentally new forms of social interactions, which, in the situation of insufficient theoretical comprehension, renders IS issue a journalistically superficiality.

Optimization of robot manipulator configuration calibration by using Zhang neural network for repetitive motion

High precision and low complexity control algorithm plays an important role in the developing of the end-effector instrumentation of different robot manipulators. In order to reduce the kinetic energy and the high-speed drift phenomenon of the repetitive motion tracking task, the robot manipulator needs to calibrate its configuration. In this paper, we formulate the configuration calibration of the robot manipulator for the repetitive motion task as a future quadratic programming optimization problem constrained with equality constraints, which is also regarded as a fundamental problem in artificial intelligence and modern control engineering. Zhang neural network, which is a canonical method, can be adopted to deal with the continuous form of the future optimization problem, named as temporally dependent quadratic programming problem with equality constraints. In order to overcome the issue of temporally dependent inverse computing, a novel Zhang neural network model and its uncertain disturbance tolerant model, which are termed as filtered reciprocal-kind Zhang neural network model and uncertain disturbance tolerant filtered reciprocal-kind Zhang neural network model, respectively, are proposed by integrating the energy-type cost function and Zhang neural network design formula for solving the temporally dependent quadratic programming problem with equality constraints in this paper. Based on the Euler discrete formula and the models, the discrete filtered reciprocal-kind Zhang neural network and the discrete uncertain disturbance tolerant filtered reciprocal-kind Zhang neural network algorithms are proposed for solving the future quadratic programming problem with equality constraints and the robot manipulator configuration calibration problem of repetitive motion. The convergence properties of the reciprocal-kind Zhang neural network model and its corresponding uncertain disturbance tolerant model are obtained by Lyapunov stability theory of nonlinear system and its corresponding perturbed system, while the convergence property of the filtered reciprocal-kind Zhang neural network model is analyzed by the limit thinking. For the repetitive motion task, three experiments for solving the configuration calibration problem of PUMA560, Kinova Jaco2, and Franka Emika Panda robot manipulators are performed to illustrate the effectiveness, robustness and superiority of our proposed discrete filtered reciprocal-kind Zhang neural network algorithms.

Civil law regime of objects generated by artificial intelligence

The article is devoted to the civil law aspects of the problems of artificial intelligence and its place in the structure of civil law relations, as well as questions regarding the legal regime of objects generated by artificial intelligence. The approaches expressed in the domestic legal doctrine regarding the understanding of artificial intelligence as an object and as a subject of civil law relations, as well as a point of view regarding the possibility of simultaneously considering artificial intelligence as an object and as a subject of such legal relations, are analyzed. On the basis of approaches to the definition of the concept of «artificial intelligence», which are beginning to be formed at the level of legal acts of the United Nations and the European Union, as well as enshrined in the legislation of Ukraine, it is concluded that artificial intelligence must be considered as an object of civil law relations. In this regard, a critical position has been expressed regarding attempts made at the theoretical level to interpret artificial intelligence or robots endowed with artificial intelligence as subjects equivalent to natural persons, as quasi-legal entities. The main provisions of the Law of Ukraine «On Copyright and Related Rights» on the sui generis right on a non-original object generated by a computer program, which can be considered corresponding concept of «object generated by artificial intelligence», have been analyzed. The features of a non-original object generated by a computer program are singled out as a special type of an object of sui generis right. A position has been expressed regarding the expediency of clarifying the provisions of the Law of Ukraine «On Copyright and Related Rights» in the part of defining the subject of a sui generis right to a non-original object generated by a computer program. It was concluded that objects generated by artificial intelligence can be classified as objects of intellectual property law, within the framework of which their legal protection should be ensured by a special kind of right – sui generis right. The expediency of enshrining the main provisions regarding the sui generis right to an object generated by artificial intelligence in Book 4 of the Civil Code of Ukraine is emphasized.

Problems of artificial intelligence in the practical field of electronic warfare

Problems of artificial intelligence in the practical field of electronic warfare

In Search of Dispersed Memories: Generative Diffusion Models Are Associative Memory Networks.

Uncovering the mechanisms behind long-term memory is one of the most fascinating open problems in neuroscience and artificial intelligence. Artificial associative memory networks have been used to formalize important aspects of biological memory. Generative diffusion models are a type of generative machine learning techniques that have shown great performance in many tasks. Similar to associative memory systems, these networks define a dynamical system that converges to a set of target states. In this work, we show that generative diffusion models can be interpreted as energy-based models and that, when trained on discrete patterns, their energy function is (asymptotically) identical to that of modern Hopfield networks. This equivalence allows us to interpret the supervised training of diffusion models as a synaptic learning process that encodes the associative dynamics of a modern Hopfield network in the weight structure of a deep neural network. Leveraging this connection, we formulate a generalized framework for understanding the formation of long-term memory, where creative generation and memory recall can be seen as parts of a unified continuum.

인공지능(AI)과 한국 부동산의 부패방지에 관한 연구

This study attempted to present discussions and directions regarding artificial intelligence(AI) and the prevention of corruption in Korean real estate. This Paper dealt with moral hazard, corruption prevention, and trust using real estate, the 4th industrial revolution(4IR), AI, and intelligent information technology as keywords. Prior research is focusing on economic utility and technical benefits related to AI and real estate. In contrast, this study pointed out the risk of moral hazard and corruption arising from the combination of AI and real estate, and conducted a paper to prevent moral hazard and corruption. In this respect, there is a difference between this study and existing previous studies. This is where the originality of this study appears. The results of this study on anti-corruption of AI and Korean real estate are as follows. As a result of the study, the problems of AI and Korean real estate, and anti-corruption were presented in three major categories: 1. AI and the problem of changing times, 2. Korean real estate and corruption problem, and 3. AI and real estate problem. In relation to this, this study largely derived three measures as implications of the research. The contents include, first, responses to the issues of AI and changing times, second, responses to Korean real estate and corruption issues, and third, measures to respond to AI and real estate issues. This study hopes to restore market trust through Artificial Intelligence(AI) and the prevention of corruption in Korean real estate, and to build a future where the benefits and utility provided by real estate, AI, and intelligent information technology are desirable.

DYNAMIC PROGRAMMING FOR SOLVE THE INVENTORY MANAGEMENT PROBLEM IN LOGISTICS

Currently, there is a problem of methods insufficient efficiency for finding solutions to the inventory management problem. The research object is the process of solving inventory management problems. The research subject is methods for finding a solution to the inventory management problem based on dynamic programming. The research goal is to increase the efficiency of finding a solution to the inventory management problem through dynamic programming. A method based on deterministic dynamic programming, a method based on stochastic dynamic programming, a method based on Q-learning, and a method based on SARSA were applicated for the inventory management problem. There are advantages of the methods. of Methods modification of deterministic and stochastic dynamic programming, Q-learning, and SARSA due to dynamic parameters makes it possible to increase the learning speed while maintaining the root-mean-square error of the method. The numerical study made it possible to evaluate the methods (for modifying the deterministic and stochastic dynamic programming methods, the number of iterations is close to the number of stages; for both methods of deterministic and stochastic dynamic programming, the root mean square error was 0.02; for modifying the Q-learning and SARSA methods, the number of iterations was 300, for both methods of Q-learning and SARSA, the root mean square error was 0.05). These methods make it possible to expand the scope of dynamic programming, which is confirmed by their adaptation to the inventory management problem and helps to increase the intelligent computer systems efficiency for general and special purposes. The application of these methods for a wide class of artificial intelligence problems are the prospects for further research.