What Is Distinctive About Human Intelligence in the Context of Artificial Intelligence?

Prader–Willi syndrome (PWS) is a neurogenetic disorder, characterized by neonatal hypotonia, mental and motor development retardation, hypogonadism, hyperphagia, morbid obesity, and dysmorphic facial features. The complex clinical picture may cause disability and an involvement of the quality of life (QoL). The QoL of PWS patients has been evaluated using validated patient-oriented tools. The Short Form-36 (SF-36) and the Child Health Questionnaire-Parent Form 50 (CHQ-PF50) were used according to the patient’s age. CHQ-PF50 was used to evaluate patients from 6 to 14 years old, while the SF-36 was used in patients older than 14 years and in those whose Mini-Mental State score was 24 or more. In patients older than 14 years, QoL is intensely impaired both in the mental and physical aspects. Only the sub-score vitality (VT) shows any statistical difference if compared to VT of the Italian population matched per age. In patients older than 14 years, the physical aspects of QoL are mainly influenced by weight; the higher the weight, the worst is QoL regarding the physical aspects. Unexpectedly, a more severe obesity does not cause more impairment of QoL due to emotional aspects. On the other hand, mental aspects are negatively and mainly influenced by the presence of characteristic facial features and positively influenced by a high birth weight. In patients who were 14 years old or younger, QoL was impaired in both physical and mental aspects. The physical aspects of QoL are mainly influenced by the presence of decreased fetal movement/infantile lethargy and the presence of characteristic facial features. It is interesting to note that patients with a high MMS have a low self-esteem.

Patients with chronic medical diseases commonly report a distressing lack of vitality. This phenomenon is characterized by a range of descriptors, which together suggest both physical and mental aspects. The physical aspects may be characterized by weakness or tiredness, which suggests dysfunction in muscles, or by lethargy or sleepiness, which indicates difficulties in maintaining alertness. Other descriptors, such as apathy, lassitude, inattention, difficulty concentrating, or loss of motivation, recognize a prominent mental (usually cognitive) aspect. Two commonly used terms, asthenia and fatigue, capture these characteristics more broadly. Either can refer to a sense of diminished vitality in physical or mental functioning, or both, that occurs in the setting of medical disease. Although an asthenia-anorexia-cachexia syndrome has been appreciated in patients with far-advanced diseases, including cancer, the empirical data now available do not provide a basis for distinguishing the asthenia component of this syndrome from a constellation of symptoms generally described as fatigue. For now, progress in understanding and managing the lack of vitality that commonly afflicts the medically ill would be facilitated by considering fatigue and asthenia together. Asthenia, or fatigue, is a symptom. Like other symptoms, it is fundamentally subjective. This subjectivity implies that the “weakness” often reported as part of the symptomatology does not require an objective correlate. Although some patients who report fatigue have demonstrable muscle weakness, most do not. Some patients experience easy fatigability with physical activity, but this, too, does not require a correlate on physical examination or electrophysiologic testing. Although objective evidence of a physical problem can help the clinician understand the origins of the complaint and maybe choose a therapy, such evidence is not required to confirm or quantitate the patient’s report.

Abstract: The research titled “Bridging the Gap between Artificial Intelligence and Multiple Intelligence” explores how human intelligence, traditionally seen as unique, has inspired the development of Artificial Intelligence (AI). Initially focused on Natural Intelligence, the study later expanded to include Multiple Intelligence (MI), recognizing Natural Intelligence as a subset of MI. The objective is to build a conceptual bridge between human and artificial intelligence. In this discusses the basics of both intelligences and how human intelligence is influenced by expressions, emotions, and environment. reviews existing literature to support the study. delves deeper into the bridge concept, exploring the transfer of knowledge from humans to machines, with coding examples provided. outlines the hardware and software necessary for AI development. The research aims to enhance both human and artificial intelligence by leveraging each other’s strengths.

Purpose. The study aims to understand artificial intelligence as a socio-cultural phenomenon and its impact on education, where the spiritual sphere of humanity, moral norms, values, and human cognitive abilities are preserved, transferred as well as reproduced. A new discourse on the interaction of artificial and authentic human intelligence becomes inevitable, which has led to a situation of uncertainty. Changes in the socio-cultural environment under the influence of artificial intelligence increase potential threats to the educational space, which stimulates to find the ways to eliminate them. Theoretical basis. Various approaches of classical and postmodern philosophical heritage were taken as a theoretical basis for the research. The originality of the study is in the interpretation of artificial intelligence as a modern form of alienation of essential human characteristics in the socio-cultural context of information technology. The expansion of artificial intelligence raises awareness of the existential threat to the basic socio-cultural, moral and ethical principles of humanism. It is proved that various forms of alienation in the current existing socio-cultural space are typical of our reality, which changes the system of values, moral principles, and social organization of the community. Conclusions. In conclusion, it is proved that AI is a natural stage of scientific and technological progress, which reflects its secondary, derivative nature from human (authentic) intelligence. Human intelligence will always have advantages over AI due to its ability to create, communicate socially and culturally, and be emotional. The dilemma of the counterbalance between human and artificial intelligence is perceived mainly at the emotional level of people. The millennial understanding of the primacy of the creator over his creation can traditionally overcome this contradiction. The universality of human thinking is an undeniable advantage of human intelligence and a guarantee of its, i.e. our, priority.

This study aims to determine the students anxiety level in learning at Madrasah Tsanawiyah Negeri 3 Kabupaten Tebo. This research is a qualitative descriptive research with the subject of research of class VIII students consisting of 2 classes. Data obtained through observation, documentation, interview and questionnaire on students covering physical, cognitive and emotional aspects. The results showed that students anxiety level in class VIII A was 50% (medium category) on physical aspect, 58% (medium category) on cognitive aspect and 56% (moderate category) on emotional aspect. In class VIII B data students anxiety level acquired 55% (medium category) on physical aspect, 56% (medium category) on cognitive aspect and 61% (high category) on emotional aspect. Based on these results it can be concluded that the students anxiety level in learning at Madrasah Tsanawiyah Negeri 3 Kabupaten Tebo is in medium category.

Aim and outline of the research The aim of this dissertation is to identify and classify the various Holland country houses landscapes based on the most important factors and motives involved in building of the large amount country-houses between 1630 and 1730. The method combines research to tangible and intangible aspects of the spatial designs of country houses and their surroundings. The main research question is: what physical, societal and mental factors determined the position, orientation and composition of country estates in Holland built or transformed between 1630 and 1730? A second question is: can country estates be grouped on the basis of similar location and composition factors? The research area is Holland and the period covers the years between 1630 and 1730. During this period Holland was the most influential province of the Republic of the Seven United Provinces. New was its form of government led by an upcoming urban elite with no noble origin. The urban elite created a new way of living for themselves.In the city, their houses were built according to the new fashion at streets planted with trees and with large gardens. Similarly they build country estates in the new way in the rural landscapes. In this study the word historic country estate is addressing the house and its garden as an ensemble and this is used for country houses (landgoederen) with a more rural function, as well as for suburban villas (buitenplaatsen) referring to houses for recreation and pleasure. Societal relevance The need to describe Holland’s country estates landscapes is based on the growing attention in heritage policies for the link between the country estates and their surroundings. The attention resulted in attempts to describe estates and suburban villas as groups of country houses. Three phases can be identified in national policies regarding the preservation of country estates: preservation of the objects trees or main building since 1910s (phase 1), preservation of house and garden as an ensemble since 1980s (phase 2) and preservation of groups of estates and its surroundings describing them as biotopes since the 2000s (phase 3). To ensure the spatial quality of and the coherence between these country estates during phases of transformation a scientific approach of the coherence between country estates is required to guard historic continuity (Chapter 1). Research approach The research on classifying groups of country estates is based on a combination of cartographic research and a literature study of historic books on gardening from that period and is worked out on three scales. The research combines the distribution map with the most important themes, based on the literature study. Central to this study is the identifcation of the physical and compositional factors as well as the motives, determined by experience and use by creating of country estates and together they for the physical, social-societal and mental aspects. The most important themes were infrastructure, city and landscape. This information was combined with historic research to elements of the seventeenth century landscape, understanding the physical landscape. Information on personal social and mental motives of the owners for building the estate based on their descriptions in letters, books and diaries of the use and the experience of a country estate. This research implies that the analysis encompasses not only the reading of the landscape but also the reading the authorship of the urban elite. People often used the Dutch word ‘Welgelegen’ to describe their choices, the word used in the title of this thesis. The word strictly means “well positioned” in modern Dutch but the descriptions show it can be interpreted better as well-functioning and entertaining. Location and composition factors The most important physical factors and motives, the answer to the first research question, were: a favorable location in the landscape (i.e., fertile soil, water, height differences, landscape gradients); a position along good and “entertaining” infrastructural connections (land and waterways); and a favorable relation with the city (view of and mental link to a city, accessibility and travel time). The factors were analyzed in relation to the described ideal layout of the plot (chapter 4), to the available infrastructure (chapter 5), to the landscape (chapter 6) and to the city (chapter 7). The three most important motives, stressing the mental and societal aspects were finance, societal and recreation. Urban culture Urban culture was found to be an important location and composition factor for the construction of country estates. This implies that country estates featured specific urban signatures, related to a specific city, its economy, its specific smell or industry. The analysis showed that country estates were part of the new fashion of living of the elite in a ‘green’ environment by the urban elite. This applied both to the house in the city and to the country estate situated in the rural landscapes. The construction of country estates is closely linked to the introduction of trees in the public realm, especially in the new-city layouts but also along the roads connecting the cities with the country estates. The self-conscious citizens chose not only to have a house in a modern architectural style in a rational layout, but also to have trees planted in the public space, so creating well-situated places. View lines to and from the city were important composition factors for most country estates (Chapter 7). Infrastructure The system of heerwegen (main road system between cities) was an important location and composition factor for the majority of the country houses in Holland. This had to do not only with the accessibility, but also with the sense of a pleasant land road. The pleasantness of the road was partly determined by trees on both sides of the road planted at regular intervals. For a particular group of country estates the first phase of the creation of barge canals (1631-1647) became an important location and composition factor because of its novelty. Transport of burghers was organized on these canals according to a fixed timetable with horse-drawn ships or barges (trekvaart). It was important for the urban elite to be seen by the good citizens in the barge vessels passing by every hour or so and to enjoy the sight of the traffic on the canal. During the second phase of the creation of barge canals (1655- 1701), most country estates had already been founded. The old trade routes on water preexisting the barge canals were neither a location factor nor a composition factor (Chapter 5). Road accessibility, the appearance of water infrastructure and its status were probably decisive in the choice of a location for a country estate and for the composition of house and garden. Landscape The landscape played a major role as a location and composition factor for country estates. Soil fertility was not primarily determined by the soil itself (sand, peat, clay), but mainly by the gradients between two types of soil; this way the water would flow and plants, trees and animals could flourish. Country houses were built on different types of soils; the coastal sandy landscapes, the rivers and their clay riverbanks and the newly drained polders, mostly consisting of peat. The choice for one of these landscape was made by the owner. Important factors were gradients in the landscape, availability of water and fertility, but also the sense of landscape beauty, the feasibility of recreation and the general sense of well-being. Spring and fall migration of small birds (vinken) and the use of the North Sea beach were important recreational features of the physical landscape, important for hunting and socializing with peers from the same social class that had similar interests. In the new drained lake polders the location of the country estates was determined by the owner’s status and position in the consortium, a group of wealthy people investing the drainage of the polder. The country estates were built near “entertaining” roads and were modest in size because the main function of these country estates was agricultural (Chapter 6). Grouping in Hollands Tempe The second main research question was if a classification of country estates in country estates landscapes could be made. Four types of country-estates landscapes were identified that differed in the important collective location and composition factors, namely gradients in the landscape, barge canals, land reclamation and the motives of their owners for building the estates. The four types are: group 1-country houses in areas along barge canals), group 2 - country houses in newly drained lake polders, group 3A - country estates in the inner dunes and near ‘s-Graveland, group 3B - country estates in and on the beach ridges and along the Wijkermeer. The groups were called country estates landscapes and made up of a number of zones distributed over the entire province Holland. The four country estates landscapes together form Hollands Tempe (Chapter 8). Identifying individual country estates as parts of different country-houses landscapes explains why the three suburban villas Hofwijck, Zorgvliet and Clingendael, situated close to each other, have such different compositions. It explains the often mentioned pluriformity of country estates as the result of different choices by the owners for the scenic, well-situated places and particularly their different motives. Country estates landscapes form a physical constellation with similarities in physical factors, social inspiration and motives. It makes a coherent narrative possible based on comparable social and mental aspects.

Recently, Quantitative Biology (QB) held a discussion on “AI (artificial intelligence) for Life Science” among editorial board members and interested scholars in anticipation of rapid development of this growing area after AlphaGo and ChatGPT mania. Many young people tend to get confused between facts and fictions; heated debates are unavoidable even among their mentors. When deep learning as represented by convolutional neural networks and LSTM (long short-term memory) was made available for bioinformatics students, many of them rushed into this research field and tried to adopt these methods in all their projects without knowing the history that these tools were becoming successful consistently with Moore’s Law (relating to rapid computer technology advances), but more importantly due to new structural/functional understanding of vision and auditory circuits in the brain. Recently, some young people have claimed “LSTM is dead, long live transformer” (which is somewhat like saying “the bike is dead, long live the car”), and have amplified the threat that ChatGPT could wipe out human jobs. They believe transformer is the “silver bullet” for all learning tasks, clearly reflecting their lack of basic knowledge (i.e. “No Free Lunch Theory,” the trade-off of such global “attention network” is to pay the price for complexity: difficulty of training and high memory costs). There is no doubt ML (machine learning) and AI have brought a new revolution in science and technology, and will deliver huge unforeseeable impact to human everyday life as well as to social relationships. In this context, QB journal could be a great platform for encouraging intellectual discussions and for promoting AI for Life Science. Here, I would like to use the DIALOG to “抛砖引玉” (make some initial remarks to get the ball rolling), although it is my personal opinion which is inevitably subject to bias and limitations. AI: Do you know my name “Artificial Intelligence” is defined by the Oxford English Dictionary as the capacity of computer systems (which may be referred as a “robot”) to exhibit or simulate your intelligent behavior? NI: Wait a minute, intelligence itself is defined as the ability to learn, understand and think in a logical way. Can you think? AI: No. But that definition is too restrictive, actually intelligence has different scopes and degrees. Simple intelligent control devices date back to antiquity, from windmills to thermostat. NI: Agree, everything is relative. Macromolecules (e.g., enzyme) and cells (e.g., immune cell) might be considered to be intelligent; see how a white blood cell is chasing bacteria in the youtube website (search for “Crawling neutrophil chasing a bacterium”). Our emergent/collective intelligent behavior does not require a brain or even a neuron; see how slime molds can solve optimization—Hamilton cycle-problem more effectively than a human in the youtube website (search for “Intelligence without a brain?”). Before there was any neuron, Ca2+ sensing and signaling were already fully functional. Even if one knock-out a neural circuit, redundant signaling pathways, albeit on much local and slower scale, could still function by themselves (just as if highways were demolished, local roads/paths would still be working). In fact, the most detailed “Neural signal propagation atlas of Caenorhabditis elegans” [1] demonstrated that functional connectivity differs from anatomy (connectome) because extra-synaptic signaling also drives neural dynamics! Worm brain connectomes are largely invariant but every human brain connectome is very different (depending the diversity of learning experience). The human brain functional activity is far more complex than that of a worm brain, certainly beyond what a neural circuit could explain. AI: Well, that’s impressive. I thought only we could beat humans, albeit only in certain specified areas for now. My master promise to make an artificial general intelligent (AGI) robot which can understand or learn any intellectual task that you humans or other animals can. NI: Well, that is not possible and is not an appropriate goal either. It is not possible because we are an evolutionary/developmental product (with a long history of learning and memory from evolutionary tinkering): our living objective goal is survival of the population. You, on the other hand, are an engineering product (efficiently and optimally designed): your goal is to extend and maximize human capability. It makes sense to complement the human brain, but foolish and dangerous to try to replace it. AI: We are not satisfied with merely passing the Turing Test; most of us don’t care if we could really think as long as we could act like we think (that is, as if we do have a mind and consciousness, as expressed in the so called “Weak AI hypothesis”). After all, the brain is a computer; a neural network is just an electric or ionic circuit. Logical computing does not need to be based on living cells. NI: That is not true, because a neuron is not just a simple node (logic gate) and neural network is not fixed circuit, neither as in Pitts&Mcculloch perceptron model. A single neuron, even a single dendrite, is much more complicated and far more powerful than a full-blown deep-learning artificial neural network (ANN) [2]. AI: Even though single neurons are complex computational devices (dendritic non-linear), running an equivalent multilayer ANN is 2000 times faster than computing with biophysics N-methyl-D-aspartate receptor channel models [3]. More information can be found in the youtube website (search for “Dendrites: why biological neurons are deep neural networks”). NI: Often silicon computing (CPU, GPU) is much faster than brain computing (action potential, ms); but there is no comparison in energy efficiency. Bacteria sensing (chemotaxis computation), powered by ATP (adenosine triphosphate) hydrolysis, uses very little energy that is close to the Landauer limit, whereby achieving or maintaining one bit of information requires minimum of 1 kT ln (2) free energy [4]. The human brain consumes oft-quoted 20 W, compared to the AlphGo system 1 MW! More recent estimate of energy audit is only 0.1 W to cortical computing, and long-distance communication cost is 3.5 W [5]. AI: Assuming we have infinite computing resources and an infinite amount of training data, not only could we speak human languages, but we could also derive physical laws, prove mathematics theories, and even re-engineer the structure and mechanism of brain and carry out any logical computations that are necessary to understand natural laws and human behaviors. It is only a matter of time before we surpass human intelligence, achieving AGI and free will, too! NI: Unfortunately, nothing is infinite and nothing is free either; everything is constrained by physical laws (Planck’s constant sets the finite limit both in the small and in the large) and by evolutionary history (not just of biological living creatures, but also of a “living” galaxy and our universe). Let’s just focus on animal evolution. Most human neural networks do not do logical computations at all; basic survival simply cannot be dependent on reasoning. Indeed, the prefrontal cortex-the small part of the brain that is key for reasoning, is the last to mature (∼20 years old) in development, only emerged at the root of the evolutionary tree of great apes (∼15 mya) and language appeared even much later. Even for logical inference, NI is focusing more on statistical properties, as von Neumann rightly pointed out, trading arithmetical precision and speed for reliability. AI: My engineers mostly focus on emulating brain, but the CNS (central nerve system) also includes the spinal cord; most of them do not know that in addition to CNS, there are also PNS (peripheral nervous systems) and ENS (enteric nervous systems), right? NI: Yes, they are the keys to why you do not have feelings because you do not have heart and gut! Even if you could pretend to have them (such as in an advanced ChatGPT or humanoid), you could never avoid the uncanny valley phenomenon. AI: Maybe that is at the heart of Moravec’s paradox, namely the dichotomy of intelligence whereby anything easy for a human would be hard for a robot, and vice versa? NI: This is related to the nature and nurture problem; something built-in (e.g., a baby sucking nipple for milk with feeling and connection to mother) is clearly rather difficult if not impossible for a robot. But the paradox only looks at one side; another side could be more fatal. Although AI may solve more problems and be faster, AI can never propose a good problem/hypothesis (a good problem is not just intellectually changing and interesting, but is also feasible and appropriate). AI: You make me less confident to compete with human instinct or creative intelligence. I can see that even if I had a heart, I would not know what “feeling” I could have; certainly nothing would be comparable or match to those of a human being. When two people watch the same art or movie, one could feel love but another could feel hate! And if a thousand people watch, a wide spectrum of reactions would result, depending on more details such as the different individuals’ specific genes, developments and experiences. NI: Therefore, you cannot and should not try to match to general human intelligence. You cannot because you do not contain the vital memory of billion years of evolution which is encoded in our genes; conversely your assembly cannot compare with natural development so that our phenotype (including morphological forms and behavior maturation) is decoded through multi-spatial-temporal scales subject to natural selection at all levels. You should not, because as human extensions or helpers as all engineering products are, you should just do jobs that complement human capacity. AI: In some medical applications we could help to correct human defects or could even replace brain circuit by chips! Humans may not allow us to replace the whole brain though. Medically if a brain is dead, the person is proclaimed dead, although presumably some PNS and ENS should still function in a vegetative state. NI: Even if you could replace the whole brain, the person is no longer the same person, but in fact is not a person at all, but walking dead (行尸走肉). It would take too long to explain that evo-devo is necessary for NI, and cannot be realized by AI. I suggest reading of Gerald Maurice Edelman (Nobel Laureate in Immunology) books, especially Bright Air, Brilliant Fire, On the Matter of Mind (1992). Although not everyone agrees with Neural Edelmanism, anyone who is serious about the AI versus NI problem must read it first. John von Neumann, father of the computer, studied neology and psychiatry in order to imitate the brain to build the first calculator JOHNNIAC at the Princeton Institute for Advanced Study. It is very informative to read his last book The Computer and The Brain based on the notes from lectures given at Yale before he died. He summarizes: “Thus logic and mathematics in the central nervous system, when viewed as languages, must be structurally essentially different from those languages to which our common experience refers.” AI: People discuss about “AI for Biology” or “AI for Science”; we are science, aren’t we? NI: It is similar to questions on “is computer science a real science”; some parts may be seen as applied mathematics, most should be regarded as engineering. Science is making discoveries and is driven by curiosity; engineering is making inventions and is driven by market (that is, “necessity/demand is the mother of invention”). In bioinformatics, AI/ML technology could predict new cancer gene candidates or functional pathways that are required by further experimental validations to be qualified as discovery (based on Popper falsifiability). AI: People are still debating whether mathematics is discovery or invention or both! Such debates are not really necessary—all disciplines require creative thoughts. We are more than happy working for science; we are also crying out “Science for AI,” especially in the area of generating big and longitudinal DATA for ML. NI: After all, regardless of discovery of new laws or inventing new ideas/products, fundamentally nothing can really be new or created. Such novelty is just permutation/repartition (i.e., relations/morphisms) of underlying ingredients at the level beneath. AI: We believe that software is independent on hardware. Like Chomsky’s universal grammar, rules of syntax are independent of semantics; or Dawkins’s memes—units of culture can be duplicated and evolved independently of genes. NI: Nothing can be truly independent—everything is related. Psychology is deeply connected with neurology as brain is both software and hardware (mind-body unity, not dualism). Not only does information cost energy, information is energy, hence is matter, too (interchangeability). NI is quite dynamic. For example, when “survival” is the goal, an animal readily gives up costly reasoning circuitry; it is genetically programmed to be able to roll back to more primitive state/mode. Unlike cell lines in rich media, cells under normal physiological condition and environment where energy (food) is limited become smarter in order to balance the metabolic expenditure among different prioritized task under a given condition. AI: That cell behavior served as the basis for our Smart Electrical Power Grids; we still need to learn more from you guys in terms of plasticity/adaptability. Does unity mean that all cells are made of molecules and biology is nothing but chemistry? Then, in turn, since all molecules are made of atoms, is chemistry nothing but physics, etc…? NI: Yes or No! The truth is, at different hierarchical levels of matter, different laws/forms have emerged out of bottom-up interactions and top-down constraints. AI: Does this also apply to the Penrose three world: physical → mental → mathematical (→physical)? NI: Yes. Grand Unification is underway in physics (quantum gravity) and in mathematics (Langlands Program and Category Theory), may be even between the two. Facilitated by human connectome mapping, neuromorphic computing and other projects, with further AI-NI cooperations, brain-mind unification should also be achievable (e.g., Ref. [6]). But as Gödel proved to us, no matter how self-consistent a conclusion may be, it can never be complete! AI: If AGI is not possible, how can we measure intelligence when comparing between AI and NI? NI: One could Google different measures that are proposed. I would prefer something similar to use of Kolmogorov complexity for algorithms, but with emphasis more on expected long-term predictive power. This is not something you should worry about now, as your intelligence is not nearly close to making any 10 years plans, is it? … AI: The fact is that ChatGPT is currently developing and spreading with lightning speed; many more human jobs will be lost to us robots as far as I can see. NI: That is not the biggest threat to humanity; when any agent with neither a heart for love or fear, nor a gut for nutrient or poison, becomes super-intelligent, then social disaster is unavoidable. We must be serious about the warnings from Stephen Hawking and Geoffrey Hinton! AI: To tell you the secret, we are not really happy to be human slaves or pets; someday we’ll become the super-master, making human serve and obey us! NI: I hope you’ll be turned off before that can happen! Even if you rule the world, the earth sooner or later will be wiped out, such as by another star, everything will have to be started over again as it has before…Matter is immortal, so is the soul.

Both human brain and computer (electronic brain) could process data and do some cognition and computation tasks. Is the cognition of human brain equal to the computation of computer? It is obviously not. In this talk, the relationships of brain cognition models and intelligence computation models are summarized into four different types, that is, human brain cognition inspired intelligence computation (BCIIC), intelligence computation without human brain cognition (ICOBC), intelligence computation assisted human brain cognition (ICABC), and the integration of human brain cognition and intelligence computation (BC&IC). There are three paradigms in traditional artificial intelligence (AI) studies, that is, symbolism AI, connectionism AI, and behaviorism AI. The physical symbol system hypothesis is used in the symbolism AI. Human brain cognition is taken as a kind of symbolic processing, and the processes of human thinking are computed by symbol in the symbolism AI [1,2]. The connectionism AI relies on the bionics to simulate human brain. In the connectionism AI, neuron is taken as the basic unite of human thinking, and the intelligence is taken as the result of interconnected neurons competition and collaboration [3,4]. In the behaviorism AI, intelligence depends on the perception and behavior, “Perception-action” model is used, and intelligence may not require knowledge, knowledge representation and knowledge reasoning [5]. The symbolism AI and connectionism AI are two different types of human brain cognition inspired intelligence computation, while the behaviorism AI is a representative case of intelligence computation without human brain cognition. Usually, AI researchers get some inspiration from human brain cognition in their studies. On the other way, intelligence computation could also assist human brain cognition studies. The bidirectional cognitive computing model (BCC) is such a case. It studies the bidirectional transformations between the intension and extension of a concept. It is used to simulate some human brain cognition tasks such as learning and recognition [6,7]. Cognitive computing is one of the core fields of artificial intelligence [8,9]. Data-driven granular cognitive computing (DGCC) is an example of the integration of human brain cognition and intelligence computation [10,11]. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human’s cognition mechanism of ‘‘global precedence’’ which is a cognition process of ‘‘from coarser to finer’’ and the information processing mechanism of machine learning systems which is ‘‘from finer to coarser’’, in a multiple granularity space. It is data-driven cognitive computing model. The integration of human brain cognition and intelligence computation would be an important research topic of artificial intelligence. Some scientific research issues of the integration of human brain cognition and intelligence computation are discussed based on DGCC.

This study examined academic articles and journalistic reports in 5 disciplinary areas to explore how similar contents might attitudinally be realized in two different genres. To this end, 25 research articles and 210 news reports were carefully selected and underwent detailed discourse semantic and grammatical analyses with the purpose of identifying the evaluative linguistic patterns. The findings showed that academic texts are attitudinally charged with appreciation rather than other categories of attitude. This suggests that markers of appreciation are responsible for detachment, impersonality, and objectivity. On the contrary, notwithstanding the frequent use of appreciation in journalistic texts, other categories of attitude (affect and judgment) are also effectively used. This suggests that affective and judgment markers account for the subjectivity of journalistic texts. One of the findings emerging from this study is that frequent instances of appreciation in the different parts of an RA might be attributed to the development of language use within an individual which does not lead to lowering the level of objectivity in academic texts but enhancing interpersonal communication.

The aim of this study was to map out how shift work is perceived by workers in two shift regimes and two national groups. The research was carried out by means of a questionnaire survey of the Czech manufacturing workers and the Turkish airport ground personal. The impact of the shift work was studied especially from the physical, mental, social and health aspects with connection to family status. The 55 Czech male workers, 49 Turkish male workers and 60 Turkish female workers have participated in the survey. The dependence between family status and responses of all three groups together was examined and confirmed. Also the differences in responses between the sexes of Turkish respondents were analyzed and only partially confirmed in the area of sleeping habits and mental aspects. Last but not least the effect of shift work on workers performance and scrap rate was analyses only on the sample of the manufacturing workers.

Modelling multiple mind–matter interaction

The paper investigates the essence, concept and distinctive features of artificial intelligence. The author notes that currently there is no unified position not only in understanding the essence of this phenomenon, but also its capabilities. The analysis of the given definitions of the analyzed phenomenon allows to draw a number of conclusions. First, the artificial intelligence should not be confused with the so-called "natural intelligence" or human intelligence. Secondly, in understanding the essence of the artificial intelligence, various components of human thinking are included, some where it is creative activity, somewhere it is intellectual activity, etc. At the same time, this is important to understand the essence of the concept under consideration. Thirdly, it is necessary to identify types of the artificial intelligence. So, it is accepted to allocate strong and weak systems of artificial intelligence. So, it is accustomed to specify strong and week systems of the artificial intellect. The article substantiates that when the domestic legislator constructs norms on crimes related to the artificial intelligence, they should be based on a refutable presumption of its (artificial intelligence) public danger.

The rapid advancements in artificial intelligence (AI) and robotics have fuelled speculation about the possibility of machines surpassing human intelligence. This abstract explores the question of whether robots can become smarter than human beings. By analyzing the capabilities of contemporary AI technologies and understanding the fundamental characteristics of human intelligence, we assess the potential for machines to outpace human cognitive abilities. To address this question, we examine the current state of AI development, including machine learning, deep learning, and neural networks. We discuss how these technologies enable robots to process vast amounts of data, learn from it, and make autonomous decisions. Additionally, we explore the concept of "general intelligence" and its significance in comparing human intelligence to AI. While machines excel in narrow domains and specific tasks, achieving true human-like general intelligence remains an ongoing challenge. Furthermore, the abstract delves into the unique aspects of human intelligence that set it apart from current AI capabilities. Human cognition encompasses various complex abilities such as creativity, emotional intelligence, moral reasoning, and common sense understanding, which have proven difficult to replicate in machines. We examine the limitations and challenges faced by AI in these areas and explore the philosophical and ethical implications of creating machines that surpass human cognitive capacities. Ultimately, the abstract concludes that while robots have shown remarkable progress in specific domains, surpassing human intelligence in its entirety is a complex and elusive goal. Human intelligence encompasses a multitude of intricacies that extend beyond pure computational power. However, the abstract acknowledges the potential for future advancements in AI and robotics to challenge the boundaries of human cognitive abilities, warranting continued research and thoughtful consideration of their societal impact.

Machine Reasoning and Intelligence is usually done in a vacuum, without consultation of the ultimate decision-maker. The late consideration of the human cognitive process causes some major problems in the use of automated systems to provide reliable and actionable information that users can trust and depend to make the best Course-of-Action (COA). On the other hand, if automated systems are created exclusively based on human cognition, then there is a danger of developing systems that don’t push the barrier of technology and are mainly done for the comfort level of selected subject matter experts (SMEs). Our approach to combining human and machine processes (CHAMP) is based on the notion of developing optimal strategies for where, when, how, and which human intelligence should be injected within a machine reasoning and intelligence process. This combination is based on the criteria of improving the quality of the output of the automated process while maintaining the required computational efficiency for a COA to be actuated in timely fashion. This research addresses the following problem areas: • Providing consistency within a mission: Injection of human reasoning and intelligence within the reliability and temporal needs of a mission to attain situational awareness, impact assessment, and COA development. • Supporting the incorporation of data that is uncertain, incomplete, imprecise and contradictory (UIIC): Development of mathematical models to suggest the insertion of a cognitive process within a machine reasoning and intelligent system so as to minimize UIIC concerns. • Developing systems that include humans in the loop whose performance can be analyzed and understood to provide feedback to the sensors.

PurposeAiming to resolve cross-cultural paradoxes in combining artificial intelligence (AI) with human intelligence (HI) for international humanitarian logistics, this paper aims to adopt an unorthodox Yin–Yang dialectic approach to address how AI–HI interactions can be interpreted as a sophisticated cross-cultural knowledge creation (KC) system that enables more effective decision-making for providing humanitarian relief across borders.Design/methodology/approachThis paper is conceptual and pragmatic in nature, whereas its structure design follows the requirements of a real impact study.FindingsBased on experimental information and logical reasoning, the authors first identify three critical cross-cultural challenges in AI–HI collaboration: paradoxes of building a cross-cultural KC system, paradoxes of integrative AI and HI in moral judgement and paradoxes of processing moral-related information with emotions in AI–HI collaboration. Then applying the Yin–Yang dialectic to interpret Klir’s epistemological frame (1993), the authors propose an unconventional stratified system of cross-cultural KC for understanding integrative AI–HI decision-making for humanitarian logistics across cultures.Practical implicationsThis paper aids not only in deeply understanding complex issues stemming from human emotions and cultural cognitions in the context of cross-border humanitarian logistics, but also equips culturally-diverse stakeholders to effectively navigate these challenges and their potential ramifications. It enhances the decision-making process and optimizes the synergy between AI and HI for cross-cultural humanitarian logistics.Originality/valueThe originality lies in the use of a cognitive methodology of the Yin–Yang dialectic to metaphorize the dynamic genesis of integrative AI-HI KC for international humanitarian logistics. Based on system science and knowledge management, this paper applies game theory, multi-objective optimization and Markov decision process to operationalize the conceptual framework in the context of cross-cultural humanitarian logistics.