Artificial Intelligence Recognition Research Articles

Experimental Study on Using Synthetic Images as a Portion of Training Dataset for Object Recognition in Construction Site

The application of Artificial Intelligence (AI) across various industries necessitates the acquisition of relevant environmental data and the implementation of AI recognition learning based on this data. However, the data available in real-world environments are limited and difficult to obtain. Construction sites represent dynamic and hazardous environments with a significant workforce, making data acquisition challenging and labor-intensive. To address these issues, this experimental study explored the potential of generating synthetic data to overcome the challenges of obtaining data from hazardous construction sites. Additionally, this research investigated the feasibility of hybrid dataset in securing construction-site data by creating synthetic data for scaffolding, which has a high incidence of falls but low object recognition rates due to its linear object characteristics. We generated a dataset by superimposing scaffolding objects, from which the backgrounds were removed, onto various construction site background images. Using this dataset, we produced a hybrid dataset to assess the feasibility of synthetic data for construction sites and to evaluate improvements in object recognition performance. By finding the optimal composition ratio with real data and conducting model training, the highest accuracy was achieved at an 8:2 ratio, with a construction object recognition accuracy of 0.886. Therefore, this study aims to reduce the risk and labor associated with direct data collection at construction sites through a hybrid dataset, achieving data generation at a low cost and high efficiency. By generating synthetic data to find the optimal ratio and constructing a hybrid dataset, this research demonstrates the potential to address the problems of data scarcity and data quality on construction sites. The improvement in recognition accuracy of the construction safety management system is anticipated, suggesting that the creation of synthetic data for constructing a hybrid dataset can reduce construction safety-accident issues.

AI-Driven Cybersecurity: Balancing Advancements and Safeguards

As Artificial Intelligence (AI) continues its rapid evolution, its profound influence on cybersecurity becomes increasingly evident. This study delves into the pivotal role of AI in fortifying cybersecurity measures, emphasizing its capacity for enhanced threat detection, automated response mechanisms, and the development of resilient security frameworks. However, alongside its promise, recognition of AI's susceptibility to exploitation in sophisticated cyber-attacks exists, underscoring the imperative for continual advancements in AI-driven security solutions. This research offers a nuanced perspective on AI's impact on cybersecurity, advocating for the proactive integration of AI strategies, sustained research efforts, and formulating ethical guidelines. Adopting supervised machine learning (ML) algorithms like decision trees, support vector machines, and neural networks aims to harness AI's potential to bolster cybersecurity while concurrently addressing associated risks, paving the way for a secure digital landscape. Regarding accuracy, the neural network outperforms other models by 98%.

Application Types of Computer Artificial Intelligence Recognition Technology

Application Types of Computer Artificial Intelligence Recognition Technology

Assessing the Knowledge and Perception of Artificial Intelligence for Teaching and Research among Lecturers in the Faculties of Arts in Nigeria

This study assesses the knowledge of artificial intelligence (AI) for teaching and research among lecturers in the faculties of arts in Nigeria. Despite the growing recognition of AI's potential to enhance educational practices, there are significant gaps in educators' AI literacy. The research adopts a quantitative approach, surveying lecturers across Nigerian universities to gauge their awareness, engagement, and perceptions of AI integration. Results reveal a moderate level of AI awareness among respondents, with a notable interest in further training tailored to arts disciplines. Challenges such as technical barriers and limited resources hinder seamless AI integration, highlighting the need for targeted interventions and support mechanisms. Recommendations include enhanced training programs, infrastructure improvement, and ethical guidelines to facilitate responsible AI utilization in arts education. Addressing these challenges and fostering AI literacy among educators can create an enabling environment for leveraging AI to enhance teaching and research outcomes in Nigerian arts faculties.

Application value of the automated machine learning model based on modified CT index combined with serological indices in the early prediction of lung cancer.

Accurately predicting the extent of lung tumor infiltration is crucial for improving patient survival and cure rates. This study aims to evaluate the application value of an improved CT index combined with serum biomarkers, obtained through an artificial intelligence recognition system analyzing CT features of pulmonary nodules, in early prediction of lung cancer infiltration using machine learning models. A retrospective analysis was conducted on clinical data of 803 patients hospitalized for lung cancer treatment from January 2020 to December 2023 at two hospitals: Hospital 1 (Affiliated Changshu Hospital of Soochow University) and Hospital 2 (Nantong Eighth People's Hospital). Data from Hospital 1 were used for internal training, while data from Hospital 2 were used for external validation. Five algorithms, including traditional logistic regression (LR) and machine learning techniques (generalized linear models [GLM], random forest [RF], gradient boosting machine [GBM], deep neural network [DL], and naive Bayes [NB]), were employed to construct models predicting early lung cancer infiltration and were analyzed. The models were comprehensively evaluated through receiver operating characteristic curve (AUC) analysis based on LR, calibration curves, decision curve analysis (DCA), as well as global and individual interpretative analyses using variable feature importance and SHapley additive explanations (SHAP) plots. A total of 560 patients were used for model development in the training dataset, while a dataset comprising 243 patients was used for external validation. The GBM model exhibited the best performance among the five algorithms, with AUCs of 0.931 and 0.99 in the validation and test sets, respectively, and accuracies of 0.857 and 0.955 in the validation and test groups, respectively, outperforming other models. Additionally, the study found that nodule diameter and average CT value were the most significant features for predicting lung cancer infiltration using machine learning models. The GBM model established in this study can effectively predict the risk of infiltration in early-stage lung cancer patients, thereby improving the accuracy of lung cancer screening and facilitating timely intervention for infiltrative lung cancer patients by clinicians, leading to early diagnosis and treatment of lung cancer, and ultimately reducing lung cancer-related mortality.

Research on adaptive shooting algorithm for unmanned aerial vehicles under typical operating conditions of overhead transmission lines

We consider typical environmental conditions, such as different shooting backgrounds, lighting, and shooting distances, and study adaptive shooting algorithms for unmanned aerial vehicle (UAV) on-site environments under typical working conditions to achieve high-quality acquisition of UAV inspection images and reduce the technical difficulty of artificial intelligence recognition of inspection images. Based on the collected high-quality images and the characteristics of unmanned aerial vehicle inspection images, a deep learning technology framework is adopted to study the intelligent identification method of hidden dangers in transmission line equipment and channel environment, achieving the intelligent recognition of pin level fine particle defects.

AI in risk management: An analytical comparison between the U.S. and Nigerian banking sectors

This paper presents a comprehensive review of the application and impact of Artificial Intelligence (AI) in risk management within the banking sectors of the United States and Nigeria, emphasizing a comparative analysis. The objective is to assess how AI technologies are adopted and implemented in risk management practices across these diverse banking environments, identifying the benefits achieved and the challenges faced. The review synthesizes existing literature, including case studies, industry reports, and academic research, to outline the current state of AI in risk management. It delves into various risk types such as credit, market, operational, and compliance risks, exploring the specific AI tools and techniques employed to address these risks in each country. Key findings suggest that U.S. banks have a more mature implementation of AI in risk management, characterized by the adoption of advanced analytics, machine learning models, and natural language processing for enhanced decision-making, fraud detection, and compliance monitoring. In contrast, the Nigerian banking sector is at a nascent stage of AI adoption, with efforts hampered by challenges like inadequate technological infrastructure, regulatory hurdles, and a lack of skilled professionals in AI. Despite these differences, the paper identifies a strong interest and potential for growth in AI applications within the Nigerian banking sector, spurred by an increasing recognition of AI's value in enhancing competitiveness and meeting regulatory demands. Conclusively, the review underscores the critical role of supportive regulatory policies, investment in technological infrastructure, and capacity building in human capital as pivotal elements for fostering the effective integration of AI in risk management. The comparative analysis reveals both the disparities and potential areas of collaboration between the U.S. and Nigerian banking sectors, advocating for a global dialogue on best practices and strategies for AI adoption in risk management.

Research on Ferrographic Image Fault Diagnosis Based on Channel Overlapping Technique and Information Fusion Mechanism

Abstract Utilizing computer technology to realize the application of ferrographic intelligent fault diagnosis technology is a foundational investigation to oversee the operations of mechanical equipment. To continuously improve the accuracy of artificial intelligence recognition, the complexity and computation of the model will be increased. The proposal of the transformer model (the core technology of chatgpt) has fundamentally changed the intelligence level of artificial intelligence, but it has also greatly increased the demand for computer computing power. What's more, it is difficult to equip industrial quality inspection sites with high computing power computers. The channel overlapping technique developed in this paper is a technology to segment the three channels of image information and reserve overlapping areas for an information communication mechanism. With this mechanism, the model location channel overlapping convolutional neural network can obtain high recognition accuracy by using only one-half of the original training computing power. When channel overlapping combines with no position information, information fusion is formed. The model channel overlapping technique fusion convolutional neural network established by the information fusion mechanism will get a higher prediction accuracy through joint training with the original image. However, the computation consumption is nearly one-third of the pure traditional convolutional neural network algorithm.

Estimating On-Road Transportation Carbon Emissions from Open Data of Road Network and Origin-Destination Flow Data

Accounting for over 20% of the total carbon emissions, the precise estimation of on-road transportation carbon emissions is crucial for carbon emission monitoring and efficient mitigation policy formulation. However, existing estimation methods typically depend on hard-to-collect individual statistics of vehicle miles traveled to calculate emissions, thereby suffering from high data collection difficulty. To relieve this issue by utilizing the strong pattern recognition of artificial intelligence, we incorporate two sources of open data representative of the transportation demand and capacity factors, the origin-destination (OD) flow data and the road network data, to build a hierarchical heterogeneous graph learning method for on-road carbon emission estimation (HENCE). Specifically, a hierarchical graph consisting of the road network level, community level, and region level is constructed to model the multi-scale road network-based connectivity and travel connection between spatial areas. Heterogeneous graphs consisting of OD links and spatial links are further built at both the community level and region level to capture the intrinsic interactions between travel demand and road network accessibility. Extensive experiments on two large-scale real-world datasets demonstrate HENCE's effectiveness and superiority with R-squared exceeding 0.75 and outperforming baselines by 9.60% on average, validating its success in pioneering the use of artificial intelligence to empower carbon emission management and sustainability development. The implementation codes are available at this link: https://github.com/tsinghua-fib-lab/HENCE.

Preliminary Study on the Identification of Aerobic Vaginitis by Artificial Intelligence Analysis System

To develop an artificial intelligence vaginal secretion analysis system based on deep learning and to evaluate the accuracy of automated microscopy in the clinical diagnosis of aerobic vaginitis (AV). In this study, the vaginal secretion samples of 3769 patients receiving treatment at the Department of Obstetrics and Gynecology, West China Second Hospital, Sichuan University between January 2020 and December 2021 were selected. Using the results of manual microscopy as the control, we developed the linear kernel SVM algorithm, an artificial intelligence (AI) automated analysis software, with Python Scikit-learn script. The AI automated analysis software could identify leucocytes with toxic appearance and parabasal epitheliocytes (PBC). The bacterial grading parameters were reset using standard strains of lactobacillus and AV common isolates. The receiver operating characteristic (ROC) curve analysis was used to determine the cut-off value of AV evaluation results for different scoring items were obtained by using the results of manual microscopy as the control. Then, the parameters of automatic AV identification were determined and the automatic AV analysis scoring method was initially established. A total of 3769 vaginal secretion samples were collected. The AI automated analysis system incorporated five parameters and each parameter incorporated three severity scoring levels. We selected 1.5 μm as the cut-off value for the diameter between Lactobacillus and common AV bacterial isolates. The automated identification parameter of Lactobacillus was the ratio of bacteria ≥1.5 μm to those <1.5 μm. The cut-off scores were 2.5 and 0.5, In the parameter of white blood cells (WBC), the cut-off value of the absolute number of WBC was 103 μL－1 and the cut-off value of WBC-to-epithelial cell ratio was 10. The automated identification parameter of toxic WBC was the ratio of toxic WBC toWBC and the cut-off values were 1% and 15%. The parameter of background flora was bacteria<1.5 μm and the cut-off values were 5×103 μL－1 and 3×104 μL－1. The parameter of the parabasal epitheliocytes was the ratio of PBC to epithelial cells and the cut-off values were 1% and 10%. The agreement rate between the results of automated microscopy and those of manual microscopy was 92.5%. Out of 200 samples, automated microscopy and manual microscopy produced consistent scores for 185 samples, while the results for 15 samples were inconsistent. We developed an AI recognition software for AV and established an automated vaginal secretion microscopy scoring system for AV. There was good overall concordance between automated microscopy and manual microscopy. The AI identification software for AV can complete clinical lab examination with rather high objectivity, sensitivity, and efficiency, markedly reducing the workload of manual microscopy.

Intraoperative AI-assisted early prediction of parathyroid and ischemia alert in endoscopic thyroid surgery.

The preservation of parathyroid glands is crucial in endoscopic thyroid surgery to prevent hypocalcemia and related complications. However, current methods for identifying and protecting these glands have limitations. We propose a novel technique that has the potential to improve the safety and efficacy of endoscopic thyroid surgery. Our study aims to develop a deep learning model called PTAIR 2.0 (Parathyroid gland Artificial Intelligence Recognition) to enhance parathyroid gland recognition during endoscopic thyroidectomy. We compare its performance against traditional surgeon-based identification methods. Parathyroid tissues were annotated in 32 428 images extracted from 838 endoscopic thyroidectomy videos, forming the internal training cohort. An external validation cohort comprised 54 full-length videos. Six candidate algorithms were evaluated to select the optimal one. We assessed the model's performance in terms of initial recognition time, identification duration, and recognition rate and compared it with the performance of surgeons. Utilizing the YOLOX algorithm, we developed PTAIR 2.0, which demonstrated superior performance with an AP50 score of 92.1%. The YOLOX algorithm achieved a frame rate of 25.14 Hz, meeting real-time requirements. In the internal training cohort, PTAIR 2.0 achieved AP50 values of 94.1%, 98.9%, and 92.1% for parathyroid gland early prediction, identification, and ischemia alert, respectively. Additionally, in the external validation cohort, PTAIR outperformed both junior and senior surgeons in identifying and tracking parathyroid glands (p < 0.001). The AI-driven PTAIR 2.0 model significantly outperforms both senior and junior surgeons in parathyroid gland identification and ischemia alert during endoscopic thyroid surgery, offering potential for enhanced surgical precision and patient outcomes.

Cross-Cultural Fuzzy Information Recognition and Translation Processing in English Interpretation: A Perspective on Artificial Intelligence Recognition Technology

Cross-Cultural Fuzzy Information Recognition and Translation Processing in English Interpretation: A Perspective on Artificial Intelligence Recognition Technology

An automatic parathyroid recognition and segmentation model based on deep learning of near-infrared autofluorescence imaging.

Near-infrared autofluorescence imaging (NIFI) can be used to identify parathyroid gland (PG) during surgery. The purpose of the study is to establish a new model, help surgeons better identify, and protect PGs. Five hundred and twenty three NIFI images were selected. The PGs were recorded by NIFI and marked with artificial intelligence (AI) model. The recognition rate for PGs was calculated. Analyze the differences between surgeons of different years of experience and AI recognition, and evaluate the diagnostic and therapeutic efficacy of AI model. Our model achieved 83.5% precision and 57.8% recall in the internal validation set. The visual recognition rate of AI model was 85.2% and 82.4% on internal and external sets. The PG recognition rate of AI model is higher than that of junior surgeons (p < 0.05). This AI model will help surgeons identify PGs, and develop their learning ability and self-confidence.

Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis.

As Artificial Intelligence (AI) technologies such as Generative AI (GenAI) have become more common in academic settings, it is necessary to examine how these tools interact with issues of authorship, academic integrity, and research methodologies. The current landscape lacks cohesive policies and guidelines for regulating AI's role in academic research which has prompted discussions among publishers, authors, and institutions. This study employs inductive thematic analysis to explore publisher policies regarding AI-assisted authorship and academic work. Our methods involved a two-fold analysis using both AI-assisted and traditional unassisted techniques to examine the available policies from leading academic publishers and other publishing or academic entities. The framework was designed to offer multiple perspectives, harnessing the strengths of AI for pattern recognition while leveraging human expertise for nuanced interpretation. The results of these two analyses are combined to form the final themes. Our findings indicate six overall themes, three of which were independently identified in both the AI-assisted and unassisted, manual analysis using common software tools. A broad consensus appears among publishers that human authorship remains paramount and that the use of GenAI tools is permissible but must be disclosed. However, GenAI tools are increasingly acknowledged for their supportive roles, including text generation and data analysis. The study also discusses the inherent limitations and biases of AI-assisted analysis, necessitating rigorous scrutiny by authors, reviewers, and editors. There is a growing recognition of AI's role as a valuable auxiliary tool in academic research, but one that comes with caveats pertaining to integrity, accountability, and interpretive limitations. This study used a novel analysis supported by GenAI tools to identify themes emerging in the policy landscape, underscoring the need for an informed, flexible approach to policy formulation that can adapt to the rapidly evolving landscape of AI technologies.

Artificial Intelligence Assisted Recognition of Anatomical Landmarks and Laparoscopic Instruments in Transabdominal Preperitoneal Inguinal Hernia Repair.

Laparoscopic TAPP (Trans-Abdominal PrePeritoneal) is a minimally invasive surgical procedure used to repair inguinal hernias. Arguably, one important aspect to TAPP hernia repair is the identification of anatomical landmarks and the correct use of various laparoscopic instruments. There are very few studies regarding the use of artificial intelligence in laparoscopic inguinal hernia repair and more specifically in TAPP. The aim of this study is to evaluate the feasibility and usefulness of AI in the recognition of anatomical landmarks and tools in laparoscopic TAPP videos. Imaging data have been exported from 20 Laparoscopic TAPP videos that have been performed by the authors and another 5 high quality TAPP videos from the internet (free access) performed by other surgeons. In total 1095 selected images have been exported for annotation. To accomplish the AI result of computer vision, the YOLOv8 model of deep learning was used. In total 2716 segmented areas of interest have been exported. The AI model was able to detect the various classes with a maximum F1 score of .82 when the confidence threshold was set to .406. The mAP50 was .873 for all classes. The Precision was above 50% when the confidence was over 10%. The Recall rate was above 50% when confidence was less than 70%. These results suggest that the model is effective at balancing precision and recall, capturing both true positives and minimizing false negatives. Artificial Intelligence recognition of anatomical landmarks and laparoscopic instruments in TAPP is feasible with acceptable success rates.

Assessment of Awareness, Perception, and Opinions Towards Artificial Intelligence among Health Care Professionals and Medical Students at Tertiary Care Teaching Hospital: A Cross-sectional Study

Background: Artificial intelligence (AI) in healthcare, including machine learning and deep learning, enhances diagnosis and treatment across specialties like cardiology, dermatology, and ophthalmology. Its applications extend to transcription, patient data organization, and remote healthcare, offering support to medical professionals and transforming medical education for students and trainees. Methods: A prospective cross-sectional study was conducted with consecutive sampling, and 90 medical students were included in the study. Data was collected using Questionnaire data forms and analysed. Results: The findings revealed that 73.9% of students were aware of AI, but a significant 80.0% reported a lack of formal education on the subject. Positive perceptions included the recognition of AI's efficacy in reducing errors (71.1%) and facilitating patient education (56.7%). However, concerns were raised regarding potential impacts on the healthcare professional-patient relationship. The majority (56.7%) advocated for the integration of AI knowledge and skills into the academic curriculum. The mean positive perception score of 29.8 showed associations with age, and year of study. Conclusion: This study underscores the need to address gaps in AI awareness and advocates for the integration of AI education into pharmacy curricula. The findings highlight nuanced perspectives among students and emphasize the potential benefits of tailored educational strategies to harness positive attitudes toward AI integration in healthcare.

Identifying IDH-mutant and 1p/19q noncodeleted astrocytomas from nonenhancing gliomas: Manual recognition followed by artificial intelligence recognition.

The T2-FLAIR mismatch sign (T2FM) has nearly 100% specificity for predicting IDH-mutant and 1p/19q noncodeleted astrocytomas (astrocytomas). However, only 18.2%-56.0% of astrocytomas demonstrate a positive T2FM. Methods must be considered for distinguishing astrocytomas from negative T2FM gliomas. In this study, positive T2FM gliomas were manually distinguished from nonenhancing gliomas, and then a support vector machine (SVM) classification model was used to distinguish astrocytomas from negative T2FM gliomas. Nonenhancing gliomas (regardless of pathological type or grade) diagnosed between January 2022 and October 2022 (N = 300) and November 2022 and March 2023 (N = 196) will comprise the training and validation sets, respectively. Our method for distinguishing astrocytomas from nonenhancing gliomas was examined and validated using the training set and validation set. The specificity of T2FM for predicting astrocytomas was 100% in both the training and validation sets, while the sensitivity was 42.75% and 67.22%, respectively. Using a classification model of SVM based on radiomics features, among negative T2FM gliomas, the accuracy was above 85% when the prediction score was greater than 0.70 in identifying astrocytomas and above 95% when the prediction score was less than 0.30 in identifying nonastrocytomas. Manual screening of positive T2FM gliomas, followed by the SVM classification model to differentiate astrocytomas from negative T2FM gliomas, may be a more effective method for identifying astrocytomas in nonenhancing gliomas.

The Pancasila in the Age of Digital Society 5.0: Indonesia Legal System Perspective

his study explores the transformative impact of the digital era, specifically Society 5.0, on the Indonesian legal system, focusing on the incorporation of Pancasila values as a universal ethical foundation. Adopting a normative legal research method with a conceptual approach, the study analyzes how the Indonesian legal system, predominantly influenced by the civil law system, adapts to the challenges posed by technological advancements and the emerging digital society. The research reveals that Indonesia's unique legal system, grounded in Pancasila, exhibits characteristics of religiosity, humanism, and social justice, enabling it to adapt to societal changes while maintaining core national values. The digital transformation, embodied in Society 5.0, necessitates a legal framework that balances economic development with social problem-solving through innovative and sustainable technology integration. The study highlights the increasing significance of cybersecurity awareness and the potential recognition of artificial intelligence as a new legal subject. Emphasizing the role of Pancasila's ethical principles, the study concludes that a consistent application of these values is crucial for developing a responsive legal system capable of addressing the complexities of a digital society. This alignment ensures that legal evolution aligns with societal aspirations, maintaining harmony and order within the framework of a rapidly evolving digital landscape. The findings underscore the importance of legal reform in legislative, human resources, institutional, and cultural aspects, advocating for a harmonized approach between technological advancements and existing or forthcoming regulations, guided by Pancasila's values.Highlights:&#x0D; &#x0D; &#x0D; Pancasila as Ethical Guide: Emphasizes Pancasila's pivotal role in shaping Indonesia's legal responses to digital challenges.&#x0D; Legal System's Digital Adaptation: Highlights the Indonesian legal system's evolution in the face of advancing digital technologies.&#x0D; Harmonized Legal Reforms: Stresses the need for sustainable legal reforms aligned with societal values in the digital era.&#x0D; &#x0D; Keywords: Society 5.0, Pancasila, Indonesian Legal System, Digital Transformation, Cybersecurity&#x0D;

Challenges and obstacles to recognizing Artificial Intelligence as a new economic actor

The article offers an in-depth analysis of the role of Artificial Intelligence (AI) in terms of the development of a new Economic Formation. Amid the transformation of classical economic-political systems, a new acting image of the modern formation of digitalism - its new economic actor - Artificial Intellect - emerges. The term “economic agent” is defined, and a comparative historical analysis of applicants for the role of the second economic agent in the history of mankind is made. The author, for the first time relying on the comparative and deductive method of research, provides arguments for the possible emergence of a new economic actor on Earth. And again, for the first time, four main groups of Problems and Obstacles from the Human Community on the path to the recognition of AI as a new economic actor are classified and described. Along with classifying and describing the issues, the author also identifies possible ways to overcome the obstacles and recognize AI as a new economic actor. A wide range of sciences, from economics to philosophy, are involved in solving these problems. Special attention is paid to the elements of psychological and emotional perception of Artificial Intelligence by society.

NEW OBJECTS OF LAW CREATED BY DIGITAL TECHNOLOGIES AND LEGAL CONSEQUENCES OF LEGAL RELATIONS WITH THEM

In this article, the authors highlight the issues of the legal status of artificial intelligence. The authors note that the development of new technologies, to one degree or another, necessitates the legal regulation of emerging new social relations, including those related to digital technologies and artificial intelligence. At the same time, the authors distinguish two aspects related to the recognition of artificial intelligence as a subject of law, as well as a subject of legal relations. The first aspect, according to the authors, is related to the fact that if artificial intelligence is recognized as a subject of law and a subject of legal relations, then in this case artificial intelligence is recognized as having legal capacity, legal capacity and tortiousness, i.e. artificial intelligence will have the legal personality that humans have. The second aspect is related to the fact that if artificial intelligence is recognized as a subject of law and a subject of legal relations, would it be ethically and morally correct to simultaneously declare artificial intelligence both a new object of law and a new object of civil law in particular? According to the authors, one cannot be a subject of law and a subject of legal relations and at the same time be an object of law and an object of legal relations. At the same time, the authors appeal to the current legislation, which recognizes individuals and legal entities as subjects of law and subjects of legal relations, but at the same time does not recognize them as objects of law, including objects of civil law. In addition, the authors briefly cover the issues of digital law in the article. In particular, the authors agree with the thesis that digital law can be considered both in an objective and subjective sense. At the same time, the authors believe that today there is a need to develop a draft Digital Code of the Republic of Kazakhstan, despite the fact that the literature suggests that the development of a draft Digital Code is premature as of 2022-2024.