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An evolutionary model based on bit-strings with intelligence is set up in this paper. In this model, gene is divided into two parts which relative to health and intelligence. The accumulated intelligence influences the survival process by the effect of food and space restrictions. We modify the Verhulst factor to study this effect. Both asexual and sexual model are discussed in this paper. The results show that after many time steps, stability is reached and the population self-organizes, just like the standard Penna model. The intelligence made the equilibrium to be reached larger both in asexual model and sexual model. Compared with asexual model the population size fluctuates more strongly in the sexual model.

A cellular automata model of learning in a close ecosystem on the basis of Penna model with intelligence is set up. We use the Penna model with intelligence to describe the individual. The individuals are set in a lattice of L × L and the rules of learning is expressed by cellular automata. Then, we present the results of our simulations and discuss the evolution of intelligence and knowledge respectively. From the results we find that the intelligence for the living individuals is more than that for the dead individuals. It can be concluded the effect of natural selection on the evolution of intelligence.

The aim of this paper is to explore the relationship between intelligence and economic and financial crimes. For this purpose, we use a cross-sectional sample of 182 countries for the time span of 2012–2017. Our research provides empirical evidence on the existence of a significant impact of intelligence upon economic and financial crimes. When we analyze the entire sample, we find that intelligent people are more prone to comply with the law and thus increase the efficiency of implementing government policies to reduce economic and financial crimes. However, when we conduct our analysis among the two subgroups of high- and low-income countries, different results are obtained. For high-income countries, we obtain evidence of a positive coefficient for the impact of intelligence on economic and financial crimes, meaning that increased intellectual capacities of people from these countries, including high professional knowledge and skills, are used to break the traditional technology in order to get illegal benefits. Our results conducted for the low-income countries' subsample do not support intelligence as being a determining factor for economic and financial crimes; in these countries, other determinants are more important for engaging in such activities. Our study may have important implications for the policymakers who must acknowledge that various policies in the field of economic and financial crimes need to be differentially adopted depending on the level of development of each country, which offers different ways of involvement in such crimes, related to the level of people's intelligence.

Intelligence Science for Creating a Brain.

The Cognitive Neural Mechanism of Human Inductive Reasoning: A Brain Informatics Perspective.

Endogenous Indicators in Mitochondria.

Tocomin Tocotrienols Complex.

Knowledge management as traditionally espoused has two main strands: dealing with the aggregation of knowledge, and the transfer of knowledge. However, this official discourse and its key concepts grew out of the experience of large, mature, highly structured and dispersed enterprises. A look at the environment in which small enterprises work suggests a different set of key concepts, considering the notions of experience and structural capital as key knowledge manipulation tools. These tools are particularly relevant to environments of high uncertainty, volatility and risk — and so also have a significant contribution to make to the direction of knowledge management for larger enterprises, where adaptiveness and ignorance management tools are becoming increasingly important.

The purpose of this paper is to present my personal journey in the last 30-plus years of research and development efforts on a cognitive robotic system called Intelligent Soft Arm Control (ISAC). In doing so, I will highlight how our team approached solving certain robot intelligence, communication, and control problems.

An intelligent soft measurement and information processing method for predicting parameters of process control system was proposed. Process neural network (PNN) is a new configuration of artificial neural network put forward in recent years. Some algorithms of PNN were discussed, and these algorithms were based on function orthogonal basis expansion, yet the convergence rate was comparatively low. An improved algorithm for raising training speed based on function orthogonal basis expansion in PNN for soft measurement was researched. After increasing the normalizing rule on the original algorithm, and introducing function momentum adjustment item and learning rate automatically adjustment method for network weight function, the training time of learning algorithm for PNN was reduced, and a good result was represented by simulation in wastewater treatment system.

The brain has evolved to enable organisms to survive in a complicated and dynamic world. Its operation is based upon a priori models of the environment which are adapted, during learning, in response to new and changing stimuli. The same qualities that make biological learning mechanisms ideal for organisms make their underlying mathematical algorithms ideal for certain technological applications, especially those concerned with understanding the physical processes giving rise to complicated data sets. In this paper, we offer a mathematical model for the underlying mechanisms of biological learning, and we show how this mathematical approach to learning can yield a solution to the problem of imaging time-varying objects from X-ray computed tomographic (CT) data. This problem relates to several practical aspects of CT imaging including the correction of motion artifacts caused by patient movement or breathing.

Intelligence can be understood as a form of rationality, in the sense that an intelligent system does its best when its knowledge and resources are insufficient with respect to the problems to be solved. The traditional models of rationality typically assume some form of sufficiency of knowledge and resources, so cannot solve many theoretical and practical problems in Artificial Intelligence (AI). New models based on the Assumption of Insufficient Knowledge and Resources (AIKR) cannot be obtained by minor revisions or extensions of the traditional models, and have to be established fully according to the restrictions and freedoms provided by AIKR. The practice of NARS, an AI project, shows that such new models are feasible and promising in providing a new theoretical foundation for the study of rationality, intelligence, consciousness, and mind.

This paper explores some of the potential connections between natural and artificial intelligence and natural and artificial consciousness. In humans we use batteries of tests to indirectly measure intelligence. This approach breaks down when we try to apply it to radically different animals and to the many varieties of artificial intelligence. To address this issue people are starting to develop algorithms that can measure intelligence in any type of system. Progress is also being made in the scientific study of consciousness: we can neutralize the philosophical problems, we have data about the neural correlates and we have some idea about how we can develop mathematical theories that can map between physical and conscious states. While intelligence is a purely functional property of a system, there are good reasons for thinking that consciousness is linked to particular spatiotemporal patterns in specific physical materials. This paper outlines some of the weak inferences that can be made about the relationships between intelligence and consciousness in natural and artificial systems. To make real scientific progress we need to develop practical universal measures of intelligence and mathematical theories of consciousness that can reliably map between physical and conscious states.

The science of consciousness has made great strides in recent decades. However, the proliferation of competing theories makes it difficult to reach consensus about artificial consciousness. While for purely scientific purposes we might wish to adopt a ‘wait and see’ attitude, we may soon face practical and ethical questions about whether, for example, agents artificial systems are capable of suffering. Moreover, many of the methods used for assessing consciousness in humans and even non-human animals are not straightforwardly applicable to artificial systems. With these challenges in mind, I propose that we look for ecumenical heuristics for artificial consciousness to enable us to make tentative assessments of the likelihood of consciousness arising in different artificial systems. I argue that such heuristics should have three main features: they should be (i) intuitively plausible, (ii) theoretically-neutral, and (iii) scientifically tractable. I claim that the concept of general intelligence — understood as a capacity for robust, flexible, and integrated cognition and behavior — satisfies these criteria and may thus provide the basis for such a heuristic, allowing us to make initial cautious estimations of which artificial systems are most likely to be conscious.

A systematic understanding of the relationship between intelligence and consciousness can only be achieved when we can accurately measure intelligence and consciousness. In other work, I have suggested how the measurement of consciousness can be improved by reframing the science of consciousness as a search for mathematical theories that map between physical and conscious states. This paper discusses the measurement of intelligence in natural and artificial systems. While reasonable methods exist for measuring intelligence in humans, these can only be partly generalized to non-human animals and they cannot be applied to artificial systems. Some universal measures of intelligence have been developed, but their dependence on goals and rewards creates serious problems. This paper sets out a new universal algorithm for measuring intelligence that is based on a system’s ability to make accurate predictions. This algorithm can measure intelligence in humans, non-human animals and artificial systems. Preliminary experiments have demonstrated that it can measure the changing intelligence of an agent in a maze environment. This new measure of intelligence could lead to a much better understanding of the relationship between intelligence and consciousness in natural and artificial systems, and it has many practical applications, particularly in AI safety.

The history of U.S. intelligence and military assessments of the security implications of climate change go back many decades to the 1980s. Since that time, hundreds of analyses of climate change, a massively growing body of literature on the impacts of human-caused climate change, and reports from every U.S. defense, intelligence, and security agency have acknowledged the links between climate and security, with a focus on two key areas: the vulnerability of U.S. military bases and assets to the threats posed by climate change; and the risks that the consequences and impacts of climate change will cause political instability that may lead to increased U.S. military interventions.

Humans are highly intelligent, and their brains are associated with rich states of consciousness. We typically assume that animals have different levels of consciousness, and this might be correlated with their intelligence. Very little is known about the relationships between intelligence and consciousness in artificial systems.

Most of our current definitions of intelligence describe human intelligence. They have severe limitations when they are applied to non-human animals and artificial systems. To address this issue, this chapter sets out a new interpretation of intelligence that is based on a system’s ability to make accurate predictions. Human intelligence is measured using tests whose results are converted into values of IQ and g-score. This approach does not work well with non-human animals and AIs, so people have been developing universal algorithms that can measure intelligence in any type of system. In this chapter a new universal algorithm for measuring intelligence is described, which is based on a system’s ability to make accurate predictions.

Many people agree that consciousness is the stream of colorful moving noisy sensations that starts when we wake up and ceases when we fall into deep sleep. Several mathematical algorithms have been developed to describe the relationship between consciousness and the physical world. If these algorithms can be shown to work on human subjects, then they could be used to measure consciousness in non-human animals and artificial systems.

At present we can use our own imagination, intelligence and consciousness to picture possible relationships between intelligence and consciousness in non-human systems. In the future, we could use mathematical algorithms to measure intelligence, measure consciousness and identify correlations between intelligence and consciousness. This would lead to a more rigorous scientific understanding of the relationships between intelligence and consciousness in natural and artificial systems.

We replicate the results of a previous study about the effect of intelligence and financial resources on the repayment of High- and Low-Consequence Debts (HCD and LCD, respectively), and extend the scope of the individual differences that are examined to include personality characteristics, and particularly the big-five personality dimensions. Our results from the first study are replicated showing that intelligence is more strongly (negatively) related to HCD repayment difficulty than to LCD repayment difficulty, whereas financial resources tend to be more strongly (negatively) related to LCD repayment difficulty than to HCD repayment difficulty. We also find that personality has a stronger effect on HCD than LCD repayment difficulties. These results are explained by the positive relationship between involvement and quality of financial decision-making in general, and debt-taking decisions in particular. The relationships between the big five and HCD and LCD payment difficulties are also explained by the relationship between involvement and decision quality. Of special interest in this set of findings were the more positive [negative] effect of conscientiousness [neuroticism] on the repayment of HCD [LCD]. These results are consistent with the idea that the self-discipline and deliberation associated with conscientiousness on the one hand, and impulsivity and emotionality associated with neuroticism on the other hand, affect people’s debt-taking decision making.

This study discusses the communication between autonomous robots and humans through the development of a robot that has an emotion model. The model refers to the internal secretion system of humans and it has four kinds of the hormone parameters to use to adjust various internal conditions such as motor output, cooling fan output and sensor gain. As the result of the experiments, the hormone parameters enabled the robot to adjust its conditions like homeostasis in humans and generate the primitive emotional behaviors. In this paper, human's mental images and language are given consideration as a method for emotional expression. The hypothesis model for the acquisition of the internal expressions of robots and the experimental results using a real autonomous robot are described.

This chapter highlights the impact and manageability of rapid, constantly/ continuously and unique changes taking place in humanity that are affecting the existence individuals, as well as all categories of human organizations. It has been observed that the traditional Newtonian mindset, and its associated reductionist hypothesis and design paradigm that have served humanity ‘well’ are manifesting their limits/constraints, vulnerability and disparities. The crux of the issue is escalating complexity density, incoherency, greater mismatch among current thinking, principles, values, structure, dynamic, and hierarchical dominance, limited predictability, and the overall changing ‘reality’.

Vividly, order and linearity are not the only inherent attributes of humanity. Consequently, the significance, appropriateness and exploration of certain properties of complexity theory are introduced, partially to better identify, analyze, comprehend and manage the accelerating gaps of inconsistency — in particular, to nurture a new mindset. Arising from the new mindset, human organizations/systems are confirmed as intrinsic composite complex adaptive systems (composite CAS, nonlinear adaptive dynamical systems) comprising human beings/agents that are CAS. In this respect, leadership, governance, management, and strategic approaches adopted by all human organizations must be redefined.

Concurrently, a special focus on intelligence (and its associated consciousness), the first inherent strengths of all human agents, and its role as the key latent impetus/driver, is vital. This recognition indicates that a change in era is inevitable. Humanity is entering the new intelligence era — the core of the knowledge-intensive and complexity-centric period. Overall, an integrated intelligence/consciousness-centric, complexity-centric and network-centric approach is essential. It adopts a complexity-intelligence-centric path that focuses on the optimization of all intense intrinsic intelligence/consciousness sources (human thinking systems), better exploitation of the co-existence of order and complexity, and integration of networks in human organizations — (certain spaces of complexity must be better utilized, coherency of network of networks must be achieved, and preparation for punctuation point must be elevated).

The new holistic (multi-dimensional) strategy of the intelligent organization theory (IO theory) is the complexity-intelligence strategy, and the new mission focuses on the new intelligence advantage.

In this chapter, an introductory analysis of human intelligence and consciousness is executed to establish a conceptual foundation for the intelligent organization theory. Fundamentally, the new intelligence mindset and thinking, and intelligence paradigm focus on high intelligence/ consciousness-centricity. It concentrates on human intrinsic intelligence/ consciousness sources (its intense intelligence and consciousness — awareness and mindfulness), and stipulates that organizing around intelligence (a strategic component of the complexity-intelligence strategy) is the new strategic direction to be adopted by all human organizations in the present knowledge-intensive, fast changing, and not always predictable environment (limited predictability). In addition, the characteristics and variation in capabilities of intelligence and consciousness are further scrutinized using an intelligence spectrum (compared to other biological intelligence sources on this planet — encompassing proto-intelligence, basic life-intelligence, basic human intelligence and advanced human intelligence). In the intelligent organization theory, consciousness (awareness, mindfulness) only exists in the living/biological world, and mindfulness is confined to humanity.

Subsequently, intelligence/consciousness management and its associated dynamics that are critical activities of intelligent human organization (iCAS) are more deeply examined with respect to complexity-centricity (encompassing attributes such as stability-centricity, autopoiesis, symbiosis, self-centric, network-centric, org-centric, independency, interdependency, intelligence-intelligence linkage, engagement, self-organization/ self-transcending constructions, local space, complex networks, constructionist hypothesis and emergence). Concurrently, the urgency and impact for nurturing the new intelligence mindset and intelligence paradigm is also discussed. In a situation with escalating complexity density, this paradigmatic shift in mindset and thinking in leadership, governance and management of human organizations is highly significant for higher functionality and coherency — to all human interacting agents (both leaders and followers), as well as the organizations themselves.

Another component of the complexity-intelligence strategy examined is the nurturing of an intelligent biotic and complex adaptive macro-structure that will serve all human organizations better (towards higher coherency, synergy and structural capacity). The analysis clearly indicates the necessity of systemic transformation or structural reform that is more coherent with intelligence/consciousness, and information processing and knowledge acquisition capability. In this case, a greater operational/ practical utility and higher structural capacity can be achieved with the presence of the intelligent biotic macro-structure and agent-agent/ system micro-structure (principle of locality) that concurrently supports the intelligent complex adaptive dynamic (iCAD) better — a finer synchrony between structure and dynamic — higher intelligence advantage.

This chapter focuses on establishing the conceptual foundation of the macroscopic perspective (structure and processes) of human thinking systems—the conceptualization of the general information theory (a component of the complexity-intelligence strategy). In this analysis, the human thinking system is perceived to be comprised of two components namely, the energy–matter subsystem (the natural component), and the physical symbol subsystem (an artificial component unique to humanity). The general procedure in which the human mind handles and exploits one or more physical symbol systems (symbols manipulation) is analyzed. The conceptual development encompassing the creation of the character set, capturing and transformation of data and information, acquisition of knowledge and emergent of wisdom (the four external entities), and the significance of a written language, as well as the additional associated functions are investigated.

The unique ability of creating a character set is confined to humanity indicating that human thinking systems are the most intense intelligence sources on this planet. The intrinsic and interactive properties of the character set and the language depict the characteristics and sophistication/ complexity of the physical symbol system. Besides interacting among themselves (data, information, knowledge and wisdom), these external entities also interact with the natural entities, the information-coded energy quanta, according to certain rules and principles. Subsequently, the energy quanta interact with the information-coded matter structure (the brain’s complex neural network). This unique ability (the presence of a written language) allows knowledge to be stored externally, and abstract concepts (including theory) and complex strategizing to emerge for the first time in the human world. In this respect, in the intense human intelligence source (individual or collective), information and knowledge exists in physical, energy and matter forms, and they are inter-convertible (internalization and externalization). The interactions among the six entities and the conversion from one form to another vividly review the presence of the uniqueness of the human thinking system, as well as the orgmind — greatly redefining the interactions and dynamic in the humanity and its organizations.

Subsequently, the boundaries and objectives of human thinking systems as information processing systems, and the necessity of artificial information systems are conceived as four postulates. In the intelligent organization theory, a deeper comprehension of the human thinking systems (complex adaptive systems) is vital, as they are the vital assets closely associated with the nurturing of highly intelligent human organizations (iCAS), including the coherency and contributions of a multi-layer intelligent structure (intelligent biotic macro-structure and agent-agent/system micro-structure). In this respect, the cognitive perspective must be better comprehended and utilized.

The continuously increasing complexity of equipment and processes of hot stamping characterizes production technologies. Hot stamping is a hot sheet metal forming process, by which car body components with a high crash performance at low weight can be produced. The production method is challenging due to: 1) the combination of a forming operation with a heat treatment process, 2) temperature-sensitive properties of products; 3) relatively low productivity compared with traditional stamping. That implies the observation of a large number of parameters and their correlations and corrects any process variations in a very short time in order to ensure a consistent part quality and thus avoid waste. Therefore, the intelligent process and production method was developed. This paper is based on analysis of process-related parameters such as forming temperature, transfer time, press-holding time in terms of their influence on key quality parameters of the final component, and refines intelligent model of equipment and production system. Using the example of forming a Bpillar, the feasibility of this intelligent model of equipment and production system are presented.