Complexity, Artificial Life, and Artificial Intelligence.

In 1956 history was made at Dartmouth, at a summer conference organized by John McCarthy which inaugurated (and baptized) the field of Artificial Intelligence (AI). The fathers of the field were all there: McCarthy, Minsky, Samuel, Simon, Selfridge, and they took their inspiration from Alan Turing and John von Neumann, whose combined genius had given the world not only the serial computer, but also a strikingly clear vision of its cognitive powers. Thirty-one years later, in September of 1987, a meeting was held at Los Alamos which may in time overshadow the Dartmouth meeting and its aftermath: the first international Artificial Life (AL) conference, drawing together thinkers from around the world whose work might be said to owe its inspiration to one of John von Neumann's other great ideas: the idea of self-replicating cellular automata. Not that all workers in AL concern themselves directly with either selfreplication or cellular automata. Indeed one of the most striking features of the volume under review, the proceedings of the first Artificial Life conference, is the great diversity of approaches, goals, and grain-sizes of the simulations presented and discussed. This is an extraordinarily rich volume, exciting in tone, kaleidoscopic in its perspectives and contributions. AI is known for its simulations of cognitive competences modeled "from the top down" and perhaps the most basic difference between AI and AL is that most AL simulations are adamantly bottom up in design philosophy. While some of the virtues of bottom-up modeling are now familiar, especially in the manifestos of the connectionists, broader, deeper reasons are offered here. In Vehicles (MIT Press, 1984), Valentino Braitenberg put forward his "law of uphill analysis, downhill synthesis" which made the compelling point that it is simply much harder to figure out the inner machinery of something whose behavior you have data on

The motive of this paper is to provide a general introduction to the area or field of Artificial Intelligence and Legal Reasoning. Legal reasoning is an initiative that makes a new set of demands on artificial intelligence methods. Artificial life has now begun to be a mature inter-discipline. In this contribution, its origin is discovered by investigation, its key questions are elevated to a higher position, its main methodological tools are examined, and finally, its applications are discussed. As part of the flourishing knowledge at the junction between life science and computing, artificial life will continue to thrive and benefit from further scientific and technical procedures on both sides, the biological and the computational. The development of the framework draws remarkably on the philosophy of law, in which the understanding of legal reasoning is an important topic.

The Animals to Animats Conference brings together researchers fromethology, psychology, ecology, artificial intelligence, artificiallife, robotics, engineering, and related fields to furtherunderstanding of the behaviors and underlying mechanisms that allownatural and synthetic agents (animats) to adapt and survive inuncertain environments The Animals to Animats Conference brings together researchers from ethology, psychology, ecology, artificial intelligence, artificial life, robotics, engineering, and related fields to further understanding of the behaviors and underlying mechanisms that allow natural and synthetic agents (animats) to adapt and survive in uncertain environments. The work presented focuses on well-defined models—robotic, computer-simulation, and mathematical—that help to characterize and compare various organizational principles or architectures underlying adaptive behavior in both natural animals and animats. Bradford Books imprint

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.

A comprehensive analysis of the Pan-Canadian Artificial Intelligence Strategy and its implementation measures aimed at the growth of Canada’s scientific and research potential in the field of artificial intelligence forms the foundation of this study. Canada’s selection as the subject of study is attributed to its distinction as a pioneering country to adopt a strategy of this nature, and proving its status through drafting the Artificial Intelligence and Data Act, known as AIDA. The authors have discerned and deliberated on the main organizational factors that have positioned Canada as one of the leading nations in artificial intelligence in accordance with AI country rankings. This article presents the components of the Pan-Canadian Strategy, encompassing principal tasks and areas, including the practical introduction of novel technologies due to second-phase commercialization. It outlines the key focus areas of Canada’s public policy, including research, development and retention of skilled professionals, and the creation of essential infrastructure. The article also consolidates some significant societal outcomes realized during its implementation while identifying current trends. The foundation and activities of national institutions are underscored as pivotal in fostering scientific and research potential, with special emphasis on the initiative to establish a new institute dedicated to the safety of artificial intelligence under the strong influence of AI Safety Summit at Bletchley Park. The authors identify the key participants in the artificial intelligence ecosystem who have the most influence on implementing the Strategy. The conclusions drawn from the article aid in fostering a deeper comprehension of the role played by organizational and administrative processes in propelling advancements in the field of artificial intelligence. The favorable impact on societal development is highlighted, provided risks are mitigated. Given Ukraine’s historical association with high intellectual potential, the findings of this study can be instrumental in honing the national policy pertaining to artificial intelligence in Ukraine.

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.

The article explores the development and implementation of an automated system for constructing professionograms of artificial intelligence (AI) specialists using modern information technologies. Against the backdrop of an ever-evolving digital economy and rising demand for qualified AI professionals, the study highlights the need for accurate and scalable tools to identify and assess relevant competencies. The main objective of the study is to design an innovative digital platform that enables automated generation of professionograms by analyzing unstructured textual data—such as job descriptions, expert publications, and scientific articles—through machine learning algorithms. Special emphasis is placed on the use of TF-IDF (Term Frequency-Inverse Document Frequency) and LDA (Latent Dirichlet Alloca-tion), which allow for effective identification and classification of key terms, skills, and knowledge areas relevant to the AI domain. The methodology involves a comprehensive review of the labor market, an analysis of expert opin-ions, and the processing of large-scale textual data to develop a structured model of professional compe-tencies. The system is designed to function as a decision-support tool in HR analytics, enabling data-driven recruitment, optimization of corporate training programs, and personalized career planning. One of the core advantages of the proposed solution is the reduction of subjectivity in the recruit-ment and evaluation process, as well as its adaptability to various specializations within the AI field. The system architecture includes a web-based interface, an interactive testing module, and visualization tools such as profession graphs and thematic distribution diagrams. This allows stakeholders—from HR man-agers to educators and researchers—to intuitively explore competency models and apply them in real-world scenarios. Furthermore, the article discusses the broader implications of using data-driven approaches in strategic human capital management. It argues that automated professionogram systems can significantly improve workforce planning, reduce turnover rates, and align employee skill sets with evolving business needs. The research also opens pathways for extending the system to other professional domains, incor-porating soft skills and psychological profiling, and developing mobile applications for continuous self-assessment and skill tracking. In conclusion, the study presents a scalable and versatile framework that not only enhances the efficiency of personnel selection and development but also supports long-term organizational growth in the age of artificial intelligence.

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

Enactivism has had an important conceptual influence on Artificial Life. Its notorious mind-life continuity thesis is a fundamental idea that once sustained much of the theoretical framework, but has recently lost support, explicitly in cognitive science, and by omission in Artificial Life. Alternative computational accounts, however, do not seem capable of filling the vacuum it leaves behind, given that according to the Church-Turing thesis, the limit of what is computable is bounded by Turing machines (regardless of the specific instantiation, such as cellular automata, deep, spiking or any other form of neural networks, or even quantum computers, to name some well known cases) and that computable functions formally describe the notion of mechanism, every organismic process that determines a consistent response should be both limited to Turing-machine capabilities and amenable to formalization. This impending issue, namely, the relation among life, cognition and computation as the hypothetical elements of the mind, is particularly pertinent and highly relevant for Artificial Life. Moreover, although it is often overlooked or omitted for practical reasons, it will probably define, or so we argue, the direction of future research in cognitive science, artificial intelligence and related fields.

Chapter 11 - Artificial Life and the Animat Approach to Artificial Intelligence

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.

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.

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.

Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence