Intelligent Generation System Research Articles

Beyond Algorithmic Disclosure for Generative AI

One of the most commonly recommended policy interventions with respect to algorithms in general and artificial intelligence (“AI”) systems in particular is the need for greater transparency, often focusing on the disclosure of the variables employed by the algorithm and the weights given to those variables. This Essay argues that any meaningful transparency regime must provide information on other critical dimensions as well. For example, any transparency regime must also include key information about the data on which the algorithm was trained, including its source, scope, quality, and inner correlations, subject to constraints imposed by copyright, privacy, and cybersecurity law. Disclosures about pre-release testing also play a critical role in understanding an AI system’s robustness and its susceptibility to specification gaming. Finally, the fact that AI, like all complex systems, tends to exhibit emergent phenomena, such as proxy discrimination, interactions among multiple agents, the impact of adverse environments, and the well-known tendency of generative AI to hallucinate, makes ongoing post-release evaluation a critical component of any system of AI transparency.

Exploring the constraints on artificial general intelligence: A game-theoretic model of human vs machine interaction

The potential emergence of artificial general intelligence (AGI) systems has sparked intense debate among researchers, policymakers, and the public due to their potential to surpass human intelligence in all domains. This note argues that for an AI to be considered “general”, it should achieve superhuman performance not only in zero-sum games but also in general-sum games, where winning or losing is not clearly defined. In this note, I propose a game-theoretic framework that captures the strategic interactions between a representative human agent and a potential superhuman machine agent. Four assumptions underpin this framework: Superhuman Machine, Machine Strategy, Rationality, and Strategic Unpredictability. The main result is an impossibility theorem, establishing that these assumptions are inconsistent when taken together, but relaxing any one of them results in a consistent set of assumptions. This note contributes to a better understanding of the theoretical context that can shape the development of superhuman AI.

Principles of ontophylogenetic development of artificial general intelligence systems based on multi-agent neurocognitive architectures

The purpose of the study is to study the possibilities of multigenerational optimization of behavior control systems for agents of general artificial intelligence capable of independently solving a universal range of tasks in a real environment. The main principles of ontophylogenetic synthesis of control systems for agents of general artificial intelligence based on multi-agent neurocognitive architectures have been developed. Methods and algorithms for synthesizing the phenotypes of control systems of intelligent agents according to their genotypes are proposed. A software package for simulating the processes of ontophylogenetic synthesis of multi-agent neurocognitive architectures has been developed and experiments have been carried out to create phenotypes of intelligent agents based on them. A complex genome of an intelligent agent has been developed, the features of a multichromosome genetic algorithm for organizing calculations in the paradigm of multigenerational optimization of multiagent neurocognitive architectures have been established and substantiated. It is shown that multigenerational optimization of the multi-agent neurocognitive architecture of intelligent agents can contribute to the achievement of adaptive resistance to the operating conditions of a general artificial intelligence agent, provide the synthesis of its suboptimal structural and functional scheme, accelerate learning and algorithms for finding solutions to a universal range of problems solved by this agent in its ecological niche.

The energy challenges of artificial superintelligence

We argue here that contemporary semiconductor computing technology poses a significant if not insurmountable barrier to the emergence of any artificial general intelligence system, let alone one anticipated by many to be “superintelligent”. This limit on artificial superintelligence (ASI) emerges from the energy requirements of a system that would be more intelligent but orders of magnitude less efficient in energy use than human brains. An ASI would have to supersede not only a single brain but a large population given the effects of collective behavior on the advancement of societies, further multiplying the energy requirement. A hypothetical ASI would likely consume orders of magnitude more energy than what is available in highly-industrialized nations. We estimate the energy use of ASI with an equation we term the “Erasi equation”, for the Energy Requirement for Artificial SuperIntelligence. Additional efficiency consequences will emerge from the current unfocussed and scattered developmental trajectory of AI research. Taken together, these arguments suggest that the emergence of an ASI is highly unlikely in the foreseeable future based on current computer architectures, primarily due to energy constraints, with biomimicry or other new technologies being possible solutions.

The symbol grounding problem in the system of general artificial intelligence based on multi-agent neurocognitive architecture

The purpose of this study is to develop the basic principles and algorithms for solving the fundamental scientific problem of substantiating natural language symbols in the process of its development by a general artificial intelligence system when interacting with a social communicative environment. The signs of a minimally functional system of general artificial intelligence based on a rational agent of artificial life under the control of a multi-agent neurocognitive architecture are formulated. It is shown that such a software agent is capable of solving a spectrum of information processing tasks that is universal within the state space of the “intelligent agent - environment” system. Universalism is achieved through the implementation of the problem solving paradigm based on the synthesis of the behavior of an intelligent agent aimed at moving along the graph of problem situations, defined in the state space, marked by the values of the objective function.A simulation model of a multi-agent neurocognitive architecture of a minimal system of general artificial intelligence immersed in a communicative environment has been developed that implements the dynamic formation of functional systems based on the cooperation of neuron agents in its composition based on the principles of ontoneuromorphogenesis - situationally determined development of a multi-agent knowledge base.The main conclusion of the study is that the problem of substantiating language symbols can be solved with the help of a constructed and tested universal algorithm for acquiring language symbols that uses such functional systems to represent semantics, taking into account the communicative context, meaning and specific linguistic forms of these statements.

The Moral Status of AGI-enabled Robots

For a long time, researchers of Artificial Intelligence (AI) and futurists have hypothesized that the developed Artificial General Intelligence (AGI) systems can execute intellectual and behavioral tasks similar to human beings. However, there are two possible concerns regarding the emergence of AGI systems and their moral status, namely: 1) is it possible to grant moral status to the AGI-enabled robots similar to humans? 2) if it is (im)possible, then under what conditions do such robots (fail to) achieve moral status similar to humans? To examine the possibilities, the present study puts forward a functionality argument, which claims that if a human being and an AGI-enabled robot have similar functionality, but different creative processes, they may have similar moral status. Furthermore, the functionality argument asserts that an entity’s (a human being or an AGI-enabled robot) creation/production from carbon or silicon or its brain’s utilization of neurotransmitters or semiconductors does not carry any significance. Rather, if both entities have similar functionality, they may have similar moral status, which implies that the AGI-enabled robot may achieve human-like moral status if it performs human-like functions.

Cognitive decisions based on a rule-based fuzzy system

We develop an agent-based artificial general intelligent system that can be implemented in compact and power-efficient electronic hardware. The hardware under development is called the Street Engine, which is a hardware-based cognitive architecture for implementing agent-based artificial intelligence. In this paper, we introduce an agent-based system to replicate simple cognitive behaviours. In the processes of this system, numerical data are converted into fuzzy symbolic representations of the surrounding environment, and reasoning rules are included in a modified Fuzzy Inference System to support the cognitive decision-making. We use a case study example, the homing behaviour of the honey bee, to demonstrate constructing production rules and implementing the cognitive and reasoning capabilities of agents. The low level cognitive behaviour is converted into a rule-based fuzzy system, and hardware-based experiments have been conducted to verify the effectiveness of the proposed technique.

The Why, What, and How of Artificial General Intelligence Chip Development

The AI chips increasingly focus on implementing neural computing at low power and cost. The intelligent sensing, automation, and edge computing applications have been the market drivers for AI chips. Increasingly, the generalisation, performance, robustness, and scalability of the AI chip solutions are compared with human-like intelligence abilities. Such a requirement to transit from application-specific to general intelligence AI chip must consider several factors. This article provides an overview of this cross-disciplinary field of study, elaborating on the generalisation of intelligence as understood in building artificial general intelligence (AGI) systems. This work presents a listing of emerging AI chip technologies, classification of edge AI implementations, and the funnel design flow for AGI chip development. Finally, the design consideration required for building an AGI chip is listed along with the methods for testing and validating it.

ПЕРСПЕКТИВЫ ПРИМЕНЕНИЯ ТЕХНОЛОГИЙ BUSINESS INTELLIGENCE (BI) В САНКТ-ПЕТЕРБУРГЕ

The present article concentrates on revealing prospects of Business Intelligence systems use for efficient management. Terms studied include information technologies, management, Business Intelligence, vendor, dashboard, business analytics. Digital transformation influences all the spheres and management is no exception. Data and related processes are considered to be one of the most significant aspects of doing business. Public administration is also exploring prospects of information technologies in general and Business Intelligence systems in particular. Saint-Petersburg is one of the pioneers regarding this and can serve as a fine example for the rest Northwest Region and Russia on the whole.

Future Trends for Human‐AI Collaboration: A Comprehensive Taxonomy of AI/AGI Using Multiple Intelligences and Learning Styles

This article discusses some trends and concepts in developing a new generation of future Artificial General Intelligence (AGI) systems which relate to complex facets and different types of human intelligence, especially social, emotional, attentional, and ethical intelligence. We describe various aspects of multiple human intelligences and learning styles, which may affect a variety of AI problem domains. Using the concept of “multiple intelligences” rather than a single type of intelligence, we categorize and provide working definitions of various AGIs depending on their cognitive skills or capacities. Future AI systems will be able not only to communicate with human users and each other but also to efficiently exchange knowledge and wisdom with abilities of cooperation, collaboration, and even cocreating something new and valuable and have metalearning capacities. Multiagent systems such as these can be used to solve problems that would be difficult to solve by any individual intelligent agent.

The Synthesis and Decoding of Meaning

Abstract Thinking machines must be able to use language effectively in communication with humans. It requires from them the ability to generate meaning and transfer this meaning to a communicating partner. Machines must also be able to decode meaning communicated via language. This work is about meaning in the context of building an artificial general intelligent system. It starts with an analysis of the Turing test and some of the main approaches to explain meaning. It then considers the generation of meaning in the human mind and argues that meaning has a dual nature. The quantum component reflects the relationships between objects and the orthogonal quale component the value of these relationships to the self. Both components are necessary, simultaneously, for meaning to exist. This parallel existence permits the formulation of ‘meaning coordinates’ as ordered pairs of quantum and quale strengths. Meaning coordinates represent the contents of meaningful mental states. Spurred by a currently salient meaningful mental state in the speaker, language is used to induce a meaningful mental state in the hearer. Therefore, thinking machines must be able to produce and respond to meaningful mental states in ways similar to their functioning in humans. It is explained how quanta and qualia arise, how they generate meaningful mental states, how these states propagate to produce thought, how they are communicated and interpreted, and how they can be simulated to create thinking machines.

Guest Editorial: Artificial Intelligence in the E&P Industry: What Have We Learned So Far and Where to Next?

Artificial intelligence (AI) has captivated the imagination of science-fiction movie audiences for many years and has been used in the upstream oil and gas industry for more than a decade (Mohaghegh 2005, 2011). But few industries evolve more quickly than those from Silicon Valley, and it accordingly follows that the technology has grown and changed considerably since this discussion began. The oil and gas industry, therefore, is at a point where it would be prudent to take stock of what has been achieved with AI in the sector, to provide a sober assessment of what has delivered value and what has not among the myriad implementations made so far, and to figure out how best to leverage this technology in the future in light of these learnings. When one looks at the long arc of AI in the oil and gas industry, a few important truths emerge. First among these is the fact that not all AI is the same. There is a spectrum of technological sophistication. Hollywood and the media have always been fascinated by the idea of artificial superintelligence and general intelligence systems capable of mimicking the actions and behaviors of real people. Those kinds of systems would have the ability to learn, perceive, understand, and function in human-like ways (Joshi 2019). As alluring as these types of AI are, however, they bear little resemblance to what actually has been delivered to the upstream industry. Instead, we mostly have seen much less ambitious “narrow AI” applications that very capably handle a specific task, such as quickly digesting thousands of pages of historical reports (Kimbleton and Matson 2018), detecting potential failures in progressive cavity pumps (Jacobs 2018), predicting oil and gas exports (Windarto et al. 2017), offering improvements for reservoir models (Mohaghegh 2011), or estimating oil-recovery factors (Mahmoud et al. 2019). But let’s face it: As impressive and commendable as these applications have been, they fall far short of the ambitious vision of highly autonomous systems that are capable of thinking about things outside of the narrow range of tasks explicitly handed to them. What is more, many of these narrow AI applications have tended to be modified versions of fairly generic solutions that were originally designed for other industries and that were then usefully extended to the oil and gas industry with a modest amount of tailoring. In other words, relatively little AI has been occurring in a way that had the oil and gas sector in mind from the outset. The second important truth is that human judgment still matters. What some technology vendors have referred to as “augmented intelligence” (Kimbleton and Matson 2018), whereby AI supplements human judgment rather than sup-plants it, is not merely an alternative way of approaching AI; rather, it is coming into focus that this is probably the most sensible way forward for this technology.

Удосконалена методика оцінювання інформаційних документів в інтересах забезпечення інформаційної діяльності

The article proposes the solution of the actual scientific task of improving the scientific and methodological apparatus of evaluating information documents in conducting intelligence and information activities in the military intelligence system. Intelligence is a continuous process that includes a set of measures for organizing and conducting intelligence, information and analytical work in the general military intelligence system to provide intelligence to its customers. Collect in a timely manner, promptly process, analyze and timely report is the main and decisive task of intelligence and information activities in the interests of decision-making. The peculiarity of modern intelligence-information activity is the solution of a wide range of intellectual tasks, the results of which are the development of information documents. Assessment of the quality of their implementation is an integral part of the evaluation of information and analytical activity as a whole.

The missing G

Artificial general intelligence (AGI) is not a new notion, but it has certainly been gaining traction in recent years, and academic as well as industry resources are redirected to research in AGI. The main reason for this is that current AI techniques are limited as they are designed to operate in specific problem-domains, following meticulous preparation. These systems cannot operate in an unknown environment or under conditions of uncertainty, reuse knowledge gained in another problem domain, or autonomously learn and understand the problem-domain. We shall call AI systems capable of such feats artificial general intelligent (AGI) systems. The three tasks of this paper are to provide a working definition of the term AGI, examine the “missing G”, i.e., the set of abilities that current AI systems lack and whose implementation will result in a basic AGI system, and consider different approaches, including a hybrid one, to a comprehensive solution for an AGI.

AGI Protocol for the Ethical Treatment of Artificial General Intelligence Systems

Abstract The AGI Protocol is a laboratory process for the assessment and ethical treatment of Artificial General Intelligence systems that could be conscious and have subjective emotional experiences “theoretically”. It is meant for looking at systems that could have emotional subjective experiences much like a human, even if only from a theoretical standpoint. That is not to say that other ethical concerns do not also need to be addressed but this protocol is designed to focus on how we treat such systems in the lab. Other ethical concerns are out of scope. The protocol is designed to provide the basis for working with Artificial General Intelligence systems especially those that are modeled after the human mind in terms of systems that have the possibility of having emotional subjective experience from a theoretical standpoint. The intent is to create a reusable model and have it in the public domain so others can contribute and make additional suggestions for working with these types of systems.

Research on the Development of Integration of Neuroscience and Artificial Intelligence

Since the birth of the subject of artificial intelligence, the long-term goal of this subject is to realize the intelligent system of human level. However, after nearly 60 years of development, no general intelligent system has been able to approach the level of human beings: it has the ability to cooperate with a variety of different cognitive abilities; it has a strong adaptive ability to complex environments; it has the ability of self-learning to new things and new environments. With the development of Brain and Neuroscience and Cognitive Science, it is possible to observe some activities of Brain Neural Networks under various cognitive tasks at different scales and obtain relevant data. Therefore, inspired by brain working mechanism, the development of Brain-like Intelligence has become a hotspot in the field of Artificial Intelligence and Computational Science in recent years. Machine Intelligence Brain-like Intelligence System (MIBS) inspired by cognitive behavior mechanism and realized by hardware and software cooperation is similar to human beings in information processing mechanism, cognitive behavior and intelligence level. The goal is to enable machines to realize various human cognitive abilities and their coordination mechanism, and ultimately reach or surpass the level of human intelligence. In this paper, we will study from the perspective of brain science, cognitive science and artificial intelligence. This paper reviews the history, current situation and research focus of brain-like intelligence research, and looks forward to its development direction, potential application fields and potential far-reaching impact.

Human-like Emotional Responses in a Simplified Independent Core Observer Model System

Most artificial general intelligence (AGI) system developers have been fo-cused upon intelligence (the ability to achieve goals, perform tasks or solve problems) rather than motivation (*why* the system does what it does). As a result, most AGIs have an unhuman-like, and arguably dangerous, top-down hierarchical goal structure as the sole driver of their choices and actions. On the other hand, the independent core observer model (ICOM) was specifically designed to have a human-like “emotional” motivational system. We report here on the most recent versions of and experiments upon our latest ICOM-based systems. We have moved from a partial implementation of the abstruse and overly complex Wilcox model of emotions to a more complete implemen-tation of the simpler Plutchik model. We have seen responses that, at first glance, were surprising and seemingly illogical – but which mirror human re-sponses and which make total sense when considered more fully in the context of surviving in the real world. For example, in “isolation studies”, we find that any input, even pain, is preferred over having no input at all. We believe that the fact that the system generates such unexpected but “humanlike” behavior to be a very good sign that we are successfully capturing the essence of the on-ly known operational motivational system.

A Principled Framework for General Adaptive Social Robotics

A principled framework for general adaptive intelligent systems is described and applied to the domain of social robotics. Under the principled framework, the author develops computational methods to address an important aspect of a social robot, which is the ability to rapidly adapt to changes in the environment such as the introduction of novel objects and installations that serve novel purposes. Methods are also developed to address another important aspect of a social robot, which is the ability to understand the needs of humans that it interacts with by having a deep model of their needs, which enables the robot to assist humans in various tasks in a socially realistic manner. The author describes the methods of causal learning and script learning through computational visual observation that allow a robot to acquire the scripts and plans that enable it to understand the intentions of humans as well as solve problems to provide assistance to humans. The robot thus adapts rapidly to changing environmental factors as new observation provides new knowledge to guide its behavior. The assistance provided to humans is formulated as a script interaction problem and the optimal points at which assistance is provided are computed using a motivational strength model derived from psychological research and formulated computationally for robotic purposes. Also, a method is proposed to handle competition of needs which arises frequently in the course of robot-human interactions to generate socially realistic and appropriate behavior on the part of the robot. This paper uses primarily a home environment to demonstrate the methodology involved, but a robot that incorporates the methodology described could rapidly adapt to any environments such as the office and factory.

Synthetic cognitive development

The human cognitive system is a remarkable exemplar of a general intelligent system whose competence is not confined to a specific problem domain. Evidently, general cognitive competences are a product of a prolonged and complex process of cognitive development. Therefore, the process of cognitive development is a primary key to understanding the emergence of intelligent behavior. This paper develops the theoretical foundations for a model that generalizes the process of cognitive development. The model aims to provide a realistic scheme for the synthesis of scalable cognitive systems with an open-ended range of capabilities. Major concepts and theories of human cognitive development are introduced and briefly explored, focusing on the enactive approach to cognition and the concept of sense-making. The initial scheme of human cognitive development is then generalized by introducing the philosophy of individuation and the abstract mechanism of transduction. The theory of individuation provides the ground for the necessary paradigmatic shift from cognitive systems as given products to cognitive development as a formative process of self-organization. Next, the conceptual model is specified as a scalable scheme of networks of agents. The mechanisms of individuation are formulated in context-independent information theoretical terms. Finally, the paper discusses two concrete aspects of the generative model – mechanisms of transduction and value modulating systems. These are topics of further research towards an implementable architecture.

Regional Development Classification Model using Decision Tree Approach

Regional development classification is one way to look at differences in levels of development outcomes. Some frequently used methods are the shift share, Gain index, the Iindex Williamson and Klassen typology. The development of science in the field of data mining, offers a new way for regional development data classification. This study discusses how the decision tree is used to classify the level of development based on indicators of regional gross domestic product (GDP). GDP Data Central Java and Banten used in this study. Before the data is entered into the decision tree forming algorithm, both the provincial GDP data are classified using Klassen typology. Three decision tree algorithms, namely J48, NBTRee and REPTree tested in this study using cross-validation evaluation, then selected one of the best performing algorithms. The results show that the J48 has a better accuracy rate which is equal to 85.18% compared to the algorithm NBTRee and REPTree. Testing the model is done to the six districts / municipalities in the province of Banten, and shows that there are two districts / cities are still at the development of the status quadrant relatively underdeveloped regions, namely Kota Tangerang and Kabupaten Tangerang. As for the Central Java Province, Kendal, Magelang, Pemalang, Rembang, Semarang and Wonosobo are an area with a quadrant of development also on the status of the region is relatively underdeveloped. Classification model that has been developed is able to classify the level of development fast and easy to enter data directly into the decision tree is formed. This study can be used as an alternative decision support for policy makers in order to determine the future direction of development. General Terms Machine intelligence, data mining and decision support systems.