Optoelectronic Perovskite Synapses for Neuromorphic Computing

Aim. The work aimed to identify the specific features of cross-border business development in the field of generative artificial intelligence (AI) and develop recommendations for the formation of promising strategies for Russian companies and the Government of the Russian Federation (RF). Objectives. The work seeks to identify the characteristic features of cross-border business and conduct a foresight session on its development prospects in the field of generative AI; to assess the main development trends of international business in the field of generative AI; to determine the specific features of Russian business development in the field of generative AI; to propose conceptual directions for a strategy to support cross-border business in the field of generative AI in Russia. Methods. The research methods include socioeconomic foresight using the “Four Worlds” technique, an expert survey, a literature review, conceptual and statistical analysis, and data extrapolation. Results. The work presents a scientific definition of cross-border business in the field of generative AI and characterizes organizational models for businesses using generative AI, commonly used in the modern context. The reasons for the steady transnationalization of generative AI business are substantiated, and its characteristics are highlighted. Approaches to and challenges in assessing the economic prospects for the development of international generative AI business are examined in detail, and the results of a foresight session on cross-border business development in generative AI are presented. The work revealed the potential of cross-border generative AI business originating in Russia, and identified the barriers to its development and opportunities for overcoming them, which are traced in the intensification of strategic interaction between the state and business. Conclusions. The most probable scenario for the development of cross-border online business in the field of generative AI is a combination of technological integration and competition among major players. For Russian companies and the Russian government, the key challenge in this regard is overcoming external obstacles and creating conditions for integrating own technologies into global value chains.

The relevance of the research topic in the field of artificial intelligence is associated with the growing interest in introducing independent intelligence through self-learning artificial neural networks, the need to expand the field of space research, reducing cosmonaut training and space objects research, as well as significant funds allocated to the subject under study. On the basis of the scientific researches to clarify the applied mathematical tools in the field of artificial intelligence and neural networks application; develop an algorithm for learning neural networks and identify the prospects for their use for artificial intelligence in space activities. In scientific work the following methods of scientific knowledge were used: analysis, synthesis, verification, deduction, modeling, etc. The algorithm for learning neural networks and a scheme for the principles of the development of artificial intelligence were compiled on the basis of the neuron activation functions. The studies of the authors are associated with the development of theoretical and methodological apparatus for the further economic and scientific-technological development of the production and economic system of Russia in the context of the introduction of artificial intelligence in the space industry, including both industrial and engineering parts of work, and theoretical physics.

Recent advancements in the field of artificial intelligence have demonstratedsuccess in a variety of clinical tasks secondary to the development andapplication of big data, supercomputing, sensor networks, brain science, andother technologies. However, no projects can yet be used on a large scale inreal clinical practice because of the lack of standardized processes, lack ofethical and legal supervision, and other issues. We analyzed the existingproblems in the field of artificial intelligence and herein propose possiblesolutions. We call for the establishment of a process framework to ensure thesafety and orderly development of artificial intelligence in the medicalindustry. This will facilitate the design and implementation of artificialintelligence products, promote better management via regulatory authorities, andensure that reliable and safe artificial intelligence products are selected forapplication.

We present a response to the 2018 Request for Information (RFI) from the NITRD, NCO, NSF regarding the "Update to the 2016 National Artificial Intelligence Research and Development Strategic Plan." Through this document, we provide a response to the question of whether and how the National Artificial Intelligence Research and Development Strategic Plan (NAIRDSP) should be updated from the perspective of Fermilab, America's premier national laboratory for High Energy Physics (HEP). We believe the NAIRDSP should be extended in light of the rapid pace of development and innovation in the field of Artificial Intelligence (AI) since 2016, and present our recommendations below. AI has profoundly impacted many areas of human life, promising to dramatically reshape society --- e.g., economy, education, science --- in the coming years. We are still early in this process. It is critical to invest now in this technology to ensure it is safe and deployed ethically. Science and society both have a strong need for accuracy, efficiency, transparency, and accountability in algorithms, making investments in scientific AI particularly valuable. Thus far the US has been a leader in AI technologies, and we believe as a national Laboratory it is crucial to help maintain and extend this leadership. Moreover, investments in AI will be important for maintaining US leadership in the physical sciences.

The presented article discusses the social impact of ‎innovations in the field of ICT and artificial ‎intelligence on society. The article examines the ‎possibilities of artificial intelligence's impact on the ‎social character of society, analyzes artificial ‎intelligence from its positive and negative aspects, and ‎notes the possibilities of pragmatic use of artificial ‎intelligence against the background of new changes, as ‎well as its real and probable consequences. At the same ‎time, it discusses global and regional challenges related ‎to artificial intelligence and analyzes the main goals of ‎national strategies related to artificial intelligence. ‎Preventive security measures and ethical aspects during ‎the use of artificial intelligence against the background ‎of changes in the field of artificial intelligence in ‎Azerbaijan are also among the main research topics.

The article is devoted to the determination of the legal principles of the use of artificial intelligence in the implementation of administrative proceedings in Ukraine and the formation of the author's vision regarding their classification. It was considered in which international normative documents in one way or another the issue of using artificial intelligence in the administration of justice was regulated. In particular, it was established that the absence of a direct prohibition in the norms of the Convention on the Protection of Human Rights and Fundamental Freedoms (European Convention on Human Rights) regarding the use of artificial intelligence in the administration of justice does not exclude the possibility of using these advanced technologies during the administration of justice. Particular attention is paid to the provisions of the Resolution on intellectual property rights for the development of artificial intelligence technologies, in the provisions of which there is a clear principle of distinguishing the features of the protection of creations created by humans with the help of artificial intelligence, and creations created by artificial intelligence itself. Attention is focused on certain norms of the European Ethical Charter regarding the use of artificial intelligence in judicial systems and their environment, and the main principles established in it, which must be followed in the field of artificial intelligence and justice, are listed. It has been found out which documents of the current national legal framework regulate to one degree or another the issues of using artificial intelligence in the process of administrative proceedings. It was established that the exclusion of the possibility of transfer of powers from the consideration and resolution of certain legal disputes by judges, as a constitutional principle of the administration of justice, does not directly indicate the prohibition of the use of artificial intelligence in the administration of justice by them, but on the contrary, it should effectively help in this process and contribute to the optimization of the work of the courts . Some norms of the legislation on copyright are analyzed in order to solve the question of whether the results of the functioning of artificial intelligence fall under the protection of copyright. It is emphasized that the use of information technologies in the form of artificial intelligence is subject to the national legislation on copyright. When studying the national legislation on administrative proceedings, it was concluded that the absence of a direct rule on the prohibition of the use of artificial intelligence in the implementation of administrative proceedings indicates the possibility of using these latest information technologies in this process. The main provisions of the Concept of the development of artificial intelligence in Ukraine, operating in Ukraine, were considered, as a result of which a normative definition of the term "artificial intelligence" was provided and the main tasks of the development of artificial intelligence in the field of justice were reflected. Key words: legal principles, justice, administrative proceedings, artificial intelligence, information technologies.

This paper draws objective conclusions from field research, establishes a talent cultivation and technological innovation system in the field of artificial intelligence, analyzes the phenomena and problems that arise in school enterprise cooperation, explores their underlying reasons, and explores how to establish a talent cultivation mechanism for school enterprise cooperation in the field of artificial intelligence, establishes a basic model for artificial intelligence talent cultivation, establishes standards, human resources support, improves guarantee systems, reforms in teaching and learning, and integrates disciplines.

In this era of rapid development of global science and technology, artificial intelligence and industrial technology are converging at an unprecedented speed, driving social progress and industrial upgrading. In order to deeply discuss the latest development in these two and relevant fields, share research results, and promote academic exchanges and cooperation, we are honored to have organized the 2024 4th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA 2024). This proceedings is the crystallization of this event, which brings together the wisdom and insights of experts, scholars, engineers and technicians from all over the world. AIITA 2024 was grandly held in Guangzhou, China from April 12th to 14th, 2024 via hybrid form, attracting over 200 participants from all over the world. The conference aimed to provide a platform for experts and scholars, engineers and technicians, researchers and developers in the fields of artificial intelligence and industrial engineering to share scientific research results and cutting-edge technologies, understand academic development trends, broaden research ideas, and strengthen academic research and discussion. During the conference, the participants had an in-depth discussion on a number of topics related to the application of artificial intelligence and industrial technology, and jointly explored the new direction of future development. In the keynote speech part, latest research results and cutting-edge technologies in related fields including Graph Neural Networks: Models and Applications in Pattern Recognition (by Prof. Bo Jiang, Anhui University, China), Distributed GPU-accelerated Evolutionary Computation (by Dr. Ran Cheng, Southern University of Science and Technology, China), Quantum Model Learning (by Prof. Daowen Qiu, Sun Yat-sen University, China), Face Image Editing from the Perspective of Conditional Generative (by Prof. Si Wu, South China University of Technology, China), Cooperative Global Robust Output Regulation for Nonlinear Multi-Agent Systems with Switching Networks (by Prof. Wei Liu, South China University of Technology, China) were discussed. At the same time, we have set up a number of presentations and panel discussion sessions, in which participants gained more opportunities to have in-depth discussions and exchanges on specific issues. This conference calls for papers on a wide range of topics, covering all aspects of artificial intelligence and industrial technology applications. We received hundreds of submissions from all over the world, and after strict evaluation and screening, we finally included some excellent papers in this proceedings. These papers not only have high academic value and practical significance, but also represent the latest research dynamics and trends in the fields of artificial intelligence and industrial technology applications. Finally, we would like to thank all the experts, scholars, engineers and staff who participated in the AIITA 2024 for their support and contribution. We believe that with the joint efforts of all of us, the research and application in the fields of artificial intelligence and industrial technology applications will achieve more brilliant achievements. The Committee of AIITA 2024 List of Committee Member is available in this Pdf.

In modern society, artificial intelligence (AI) is developing more rapidly. And the Field Programmable Gate Array (FPGA) has always been the focus of research as a driving platform. This paper studies in detail the theoretical basis, applications, defects, and future development directions of FPGAs. It is concluded that FPGA has three characteristics: gate array, programmable, and scene, and the detailed positioning of FPGA, the structure, principle, tools, process, and description language of FPGA design. And the unique advantages of FPGA in the field of artificial intelligence: flexible and configurable, special optimizations for convolutional neural networks, and deterministic low latency. Several typical applications of FPGA in the field of artificial intelligence, deficiencies and solutions, and two future development directions. This article will make a great contribution to the development of FPGA in the field of artificial intelligence in the future.

Training personnel for the development of artificial intelligence technologies is one of the strategic tasks facing Russian universities. This article aims to analyze the employment of graduates from Russian universities in educational programs related to artificial intelligence. The study tests the hypothesis about the influence of university status on the Alliance ranking in the field of AI and the presence of specialized educational programs in AI at the university on the effectiveness of graduate employment in these programs. The stud y is based on the monitoring of universities implementing educational programs in the field of artificial intelligence (n = 191). As part of the analysis of the employment of graduates with competencies in the field of artificial intelligence, the specifics of their distribution among employment channels were determined. The authors identified key universities in terms of the effectiveness of training AI personnel, the amount of wages, and the most popular positions in employment. Based on the comparison of the employment rates of university graduates, as well as alternative sources of AI personnel training (self-education and professional retraining) with the indicators of personnel needs in the field of artificial intelligence, a conclusion was made about the provision of this need by 43.9 %. The obtained data allow us to conclude that universities have successfully implemented the Russian Government’s order to increase the volume of training of AI specialists. The value of the article lies in the presentation of unique factual materials that, for the first time, describe the employment of university graduates in the field of AI. In particular, the article details the losses of human resources in the AI field on the way from the admission of applicants to universities to the direct employment of graduates. The target audience of the article is researchers, experts, analysts, employees, and managers of universities, as well as government representatives involved in the process of developing artificial intelligence in Russia.

Artificial synapses that mimic the functions of biological neurons are fundamental elements of brain-like computing. The development of artificial synaptic devices is essential for future applications in computer vision information processing capabilities, as well as in the fields of artificial intelligence and the internet of things. However, the sensitivity and detection range of optoelectronic synapses, which can also serve as self-powered photodetectors, pose urgent challenges to be addressed, particularly in achieving broadband and infrared detection using individual two-dimensional semiconductor materials. In this report, a black phosphorus (BP)-based transistor is constructed based on the potential difference between BP nanoflake and Au electrodes with varying thicknesses. The BP-based transistor demonstrates the capability for self-powered photodetection across a wide range from 405 to 1064 nm. Furthermore, with the assistance of an external voltage of 1 V, the photodetection bandwidth of the BP-based transistor extends to 2200 nm. The specific detectivity and responsivity of the BP-based transistor are high to 2.47 × 1011 and 1.94 × 1011 Jones, 34 and 20 A/W under 1550 and 2200 nm infrared light, respectively. Moreover, the BP-based transistor can emulate the “learning-forgetting” behaviors of optoelectronic synapses under light with a wide range from 405 to 2200 nm, providing an effective approach for brain-like recognition processing systems. This research contributes to the advancement of optoelectronic synaptic devices and holds promise for future developments in neuromorphic computing.

The rapid development in the field of artificial intelligence is bringing advances in many research fields including the renewable electricity‐driven CO 2 and CO reduction, which features as a promising approach to mitigate carbon emission and electrically synthesize value‐added chemicals. This review focuses on the recent progresses on artificial intelligence and machine learning in the field of CO (2) reduction based on the three aspects: the catalyst screening and design, reaction mechanism investigation, and the knowledge graph construction. For the catalyst development, this work discusses the acceleration of catalyst optimization and the generative artificial intelligence frameworks based on machine learning model predictions, which represent an emerging paradigm for designing efficient catalysts. For the investigation of reaction mechanisms, the combination of machine learning and in situ spectroscopies can present detailed information on the structure evolution and the electric field effects, enabling to bridge experiments and theories. For constructing the knowledge graph in the CO (2) electroreduction, the natural language processing technologies are demonstrating strong capabilities. Finally, the current challenges and potential perspectives on the interdisciplinary fields of CO (2) RR and artificial intelligence are proposed to provide inspiration for the continuous developments of this field.

Biochemical origin of the schizophrenia and of the behavioral pathologies of schizoid type

The current stage of integration of artificial intelligence (AI) technologies into Educa- tion is characterized by a gradual transition to the triad “teacher – student – artificial intelligence”. AI is gradually beginning to take on many functions previously associated with the teacher, and this brings changes to the traditional learning process, transferring it to a new, more complex level in terms of solving cognitive problems. In turn, it creates a need for teachers and lecturers to solve new didactic objectives, which requires a revision of some of the teacher’s functions and requirements for his competence in the field of AI. The purpose of the study is to develop the structure and content of a teacher’s competence in the field of AI and to determine which of the structural components of this type of competence higher education teachers are able to implement at the present stage. Based on the analysis of academic literature, the following structural components of a teacher’s competence in the field of AI were proposed: 1) motivational; 2) normative and legal; 3) information security; 4) ethical; 5) prompt engineering; 6) teaching and assessment; 7) management of the edu- cational process; 8) professional development. As part of the empirical component of the study, an online survey was conducted to determine the structural components of competence in the field of AI of higher education teachers, which they are able to implement. The respondents were 219 teach- ers of specialized disciplines from 17 universities of the Russian Federation. The results of the study showed that among the substantive components of competence in the field of AI, teachers are more proficient in such aspects as teaching and assessment (x̄ = 3,35–3,71, Мо = 4), information security (x̄ = 3,56–3,88, Мо = 4), management of the educational process (x̄ = 3,41–3,84, Мо = 4). The most difficulties for teachers at the present stage are caused by the normative and legal component (x̄ = 3,35–3,47, Мо = 3) and prompt engineering (x̄ = 2,97–3,21, Мо = 3). The structure and content of the teacher’s competence in the field of AI proposed in the paper are of a recommendatory and framework nature. Based on them, depending on the specifics of the subject area and the availability of AI technical solutions, it is possible to develop the content of the competence in the field of using AI by teachers of specific academic disciplines or specialties.

This study is devoted to the consideration of the peculiarities of the policy of the EAEU states in the field of artificial intelligence (AI) in the context of global technological confrontation.Aim. To identify the key directions of the strategy of the EAEU member states in the field of AI, as well as to identify priority policy directions in this area at the union level.Tasks. To analyze the results of the development of the AI industry in Armenia, Belarus, Kazakhstan, Kyrgyzstan and Russia, to consider the main economic indicators, achievements in the creation of computing infrastructure, the development of their own research and development in the field of AI, to identify the main provisions of the key official documents of the EAEU member states in the field of AI. To identify the main directions of AI policy at the Union level.Methods. Research of official documents and on the basis of information and analytical technologies, comparative analysis, content analysis, inductive method using approaches of critical geopolitics.Results. The study showed that the policy of the EAEU countries in the field of AI is characterized by a focus on external technologies and investments, the creation of hotbeds of technological growth, largely due to the efforts of large commercial organizations around which the AI ecosystem is being built, including infrastructure, education and R&D. In conditions of relatively small domestic investments, the implementation of research and development results is largely associated with participation in international outsourcing projects. Against the background of increasingly escalating global technological competition, it is of paramount importance for the EAEU to strengthen its own technological sovereignty and to develop as soon as possible a unified union policy in the field of the most advanced technologies, including in the field of AI, as a conceptual basis for the development of bilateral and multilateral cooperation formats both within the EAEU itself and with other countries.Conclusions. International cooperation in high-tech spheres is becoming particularly relevant not only in the context of the accelerating transition to a new technological order, but also against the background of escalating trends of decoupling, concentration of technologies, tightening of sanctions policy. For the EAEU, the development of a unified AI policy is of priority importance, which will contribute to the intensification of scientific and technical cooperation, strengthening integration potential and technological sovereignty.