Analysis of Artificial Intelligence Training Indicators According to the Results of Russian Universities Monitoring

Artificial intelligence (hereinafter referred to as AI) is currently an area of strategic importance and a key technology ensuring a new digital economy development in Russia. Qualified AI specialist's training plays an important role in achieving ambitious AI-related goals as stated in government documents. The article presents survey results of more than 200 Russian universities, which enabled to create indicators characterizing both current and planned training volumes of AI specialists.According to the research results, Russian universities have responded quickly to the AI market development. Since 2019, they have been enrolling students at AI learning programs by intensifying training volumes annually. More than half of all AI learning programs are implemented within the «09.00.00 Informatics and Computer Science» and «01.00.00 Mathematics and Mechanics» majors/ specialties. AI specialist training in Russian universities is largely carried out at the expense of budgetary funds. The number of students enrolled at the AI learning programs is much higher for the bachelor programs.The specialists’ graduation in AI-related education programs was evaluated until the year 2025. The authors have also analyzed the best foreign practice in AI specialists training and proposed some measures to increase training volumes of AI specialists at Russian universities, for example, re-orienting higher education programs in the IT field at AI-related technologies. It is important that AI learning programs take into account recruitment needs projection in terms of training volumes and skills profiles

The Six Emotional Dimension (6DE) Model: A Multidimensional Approach to Analyzing Human Emotions and Unlocking the Potential of Emotionally Intelligent Artificial Intelligence (AI) via Large Language Models (LLM)

The rapid advancements in artificial intelligence (AI) research, particularly in training large language models (LLMs) such as OpenAI\u27s ChatGPT 3.5 and 4, hold significant potential for future applications in education, healthcare, and assisted living. Emotionally intelligent AI systems can provide personalized and adaptive educational experiences, enhancing engagement and educational outcomes. In healthcare, they can offer empathetic mental health support, augmenting existing resources. In assisted living, AI companions can provide emotional support, cognitive stimulation, and monitoring services, promoting independence and safety. However, ethical considerations and privacy safeguards are crucial to ensure responsible deployment. Integrating emotionally intelligent AI in these domains has the potential to improve human experiences and well-being greatly, but continued research and responsible development are needed to leverage its benefits while addressing challenges and ensuring ethical implementation

Synthesizing Sentience: Integrating Large Language Models and Autonomous Agents for Emulating Human Cognitive Complexity

The paper aims to present a novel methodology for emulating the intricacies of human cognitive complexity by ingeniously integrating large language models with autonomous agents. Grounded in the theoretical framework of the modular mind theory-originally espoused by Fodor and later refined by scholars such as Joanna Bryson—the study seeks to venture into the untapped potential of large language models and autonomous agents in mirroring human cognition. Recent advancements in artificial intelligence, exemplified by the inception of autonomous agents like Age in GPT, auto GPT, and baby AGI, underscore the transformative capacities of these technologies in diverse applications. Moreover, empirical studies have substantiated that persona-driven autonomous agents manifest enhanced efficacy and nuanced performance, mimicking the intricate dynamics of human interactions. The paper postulates a theoretical framework incorporating persona-driven modules that emulate psychological functions integral to general cognitive processes. This framework advocates for the deployment of a plurality of autonomous agents, each informed by specific large language models, to act as surrogates for different cognitive functionalities. Neurological evidence is invoked to bolster the theoretical architecture, delineating how autonomous agents can serve as efficacious proxies for modular cognitive centers within the human brain. Given this foundation, a theory of mind predicated upon modular constructs offers a fertile landscape for further empirical investigations and technological innovations

Individuality and the collective in AI agents: Explorations of shared consciousness and digital homunculi in the metaverse for cultural heritage

The confluence of extended reality (XR) technologies, including augmented and virtual reality, with large language models (LLM) marks a significant advancement in the field of digital humanities, opening uncharted avenues for the representation of cultural heritage within the burgeoning metaverse. This paper undertakes an examination of the potentialities and intricacies of such a convergence, focusing particularly on the creation of digital homunculi or changelings. These virtual beings, remarkable for their sentience and individuality, are also part of a collective consciousness, a notion explored through a thematic comparison in science fiction with the Borg and the Changelings in the Star Trek universe. Such a comparison offers a metaphorical framework for discussing complex phenomena such as shared consciousness and individuality, illuminating their bearing on perceptions of self and awareness. Further, the paper considers the ethical implications of these concepts, including potential loss of individuality and the challenges inherent to accurate representation of historical figures and cultures. The latter necessitates collaboration with cultural experts, underscoring the intersectionality of technological innovation and cultural sensitivity. Ultimately, this chapter contributes to a deeper understanding of the technical aspects of integrating large language models with immersive technologies and situates these developments within a nuanced cultural and ethical discourse. By offering a comprehensive overview and proposing clear recommendations, the paper lays the groundwork for future research and development in the application of these technologies within the unique context of cultural heritage representation in the metaverse

A Universal Knowledge Model and Cognitive Architecture for Prototyping AGI

The article identified 42 cognitive architectures for creating general artificial intelligence (AGI) and proposed a set of interrelated functional blocks that an agent approaching AGI in its capabilities should possess. Since the required set of blocks is not found in any of the existing architectures, the article proposes a new cognitive architecture for intelligent systems approaching AGI in their capabilities. As one of the key solutions within the framework of the architecture, a universal method of knowledge representation is proposed, which allows combining various non-formalized, partially and fully formalized methods of knowledge representation in a single knowledge base, such as texts in natural languages, images, audio and video recordings, graphs, algorithms, databases, neural networks, knowledge graphs, ontologies, frames, essence-property-relation models, production systems, predicate calculus models, conceptual models, and others. To combine and structure various fragments of knowledge, archigraph models are used, constructed as a development of annotated metagraphs. As components, the cognitive architecture being developed includes machine consciousness, machine subconsciousness, blocks of interaction with the external environment, a goal management block, an emotional control system, a block of social interaction, a block of reflection, an ethics block and a worldview block, a learning block, a monitoring block, blocks of statement and solving problems, self-organization and meta learning block

Real-time optimization of working memory in autonomous reasoning for high-level control of cognitive robots deployed in dynamic environments

High-level, real-time mission control of autonomous and semi-autonomous robots, deployed in remote and dynamic environments, remains a research challenge. Robots operating in these environments require some cognitive ability, provided by a simple, but robust, cognitive architecture. The most important process in a cognitive architecture is the working memory, with core functions being memory representation, memory recall, action selection and action execution, performed by the central executive. The cognitive reasoning process uses a memory representation, based on state flows, governed by state transitions with simple, quantified propositional transition formulae. In this thesis, real-time working memory quantification and optimization is performed using a novel adaptive entropy-based fitness quantification (AEFQ) algorithm and particle swarm optimization (PSO), respectively. A cognitive architecture, using an improved set-based PSO is developed for real-time, high-level control of single-task robots and a novel coalitional games-theoretic PSO (CG-PSO) algorithm extends the cognitive architecture for real-time, high-level control in multi-task robots. The performance of the cognitive architecture is evaluated by simulation, where a UAV executesfour use cases: Firstly, for real-time high-level, single-task control: 1) relocating the UAV to a charging station and 2) collecting and delivering medical equipment. Performance is measured by inspecting the success and completeness of the mission and the accuracy of autonomous flight control. Secondly, for real-time high-level control of multi-task autonomous vehicle control: 3) delivering medical equipment to an incident and 4) provide aerial security surveillance support. The performance of the architecture is measured in terms of completeness and cognitive processing time and cue processing time. The results show that coalitions correctly represent optimal memory and action selection in real-time, while overall processing time is within a feasible time limit, arbitrarily set to 2 seconds in this study