Association Between Multiple Intelligence and Artificial Intelligence

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.

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.

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.

n the context of total digitalization of education, problems connected with the implementation of artificial intelligence (AI) technologies, as well as issues related to the role of a person as a carrier of human intelligence (HI) in the educational process, become especially topical for universities. The increasing penetration of AI into many areas of higher education requires a balanced approach, considering all the pros and cons, assessing the risks and consequences for humans as subjects of education. The goal of the article is to compare AI and HI capabilities in the educational process of a university. The authors set and solved the following tasks: 1) to study the theoretical material and practical experience of Russian universities in the aspect of applying AI and HI in higher education; 2) to study the concepts of “artificial intelligence” and “human intelligence”; 3) to identify the advantages and disadvantages of AI and HI for the educational process at a university; 4) to present the comparative description of AI and HI in the educational process of a university; 5) to define the perspectives for the development of AI and HI in higher education. To solve the tasks the methods of analysis of scientific literature, analysis of regulatory documents, study and generalization of advanced pedagogical experience, and comparative analysis are used. The results indicate that the issues of introducing AI technologies into the educational process of universities are topical for the state and society. In theory, there is no consensus on the definition of the AI phenomenon, while HI is interpreted as the sum of a person’s cognitive abilities. The review of practice shows a wide range of applicability of AI technologies in universities: from automating routine tasks to creating an immersive educational environment. The comparative analysis of the capabilities of AI and HI has been carried out according to the following parameters: processing and analysis of big data, adaptive learning, interactivity, feedback, creative thinking and critical thinking, complexity of tasks, ethics and morality, emotional communication, objectivity of assessment. The analysis shows that AI and HI in relation to higher education have advantages and disadvantages. A promising scenario for the future development of the system of higher education under the influence of AI technologies will be the integration of AI and HI, which opens up new opportunities for humans in Education 4.0.

The primary goal of artificial intelligence (AI) is to learn, mimic, and ultimately surpass human intelligence. Connectionist AI draws inspiration from human neuroscience and biological mechanisms and has made significant progress in many fields like autonomous driving (AD). However, the performance of AD systems in open, complex, and dynamic environments is still limited. Integrating robust and high-level human intelligence into AI remains a critical trend for its ongoing development and evolution. Inspired by this, this paper reviews Human Intelligence Augmented Artificial Intelligence (HIA-AI) from an AD perspective and offers a taxonomy of related methods, including learning from human demonstrations, tuning from human feedback, integrating from human mechanisms, and abstracting from human knowledge. It discusses current applications and innovations of related methods. Furthermore, this paper examines the characteristics, advantages, and disadvantages of various HIA-AI methods and points out potential future research directions.

IntroductionConsidering the extensive development of artificial intelligence (AI) facilities, like Generative Pre-Trained Transformer (ChatGPT) 4.o and ChatGPT Scholar, we explored their abilities to conduct a systematic literature review. Using as a specific domain, an attempt to frame/methodize clinical assessment instruments used to evaluate neuro-functional deficits in Parkinson’s disease (PD) – including framed through the ICF(-DH) paradigm – for the above-mentioned comparison between human intelligence (HI) and AI, this paper is as well, a follow-up regarding the most actual subject matter of the AI’s capabilities evolution in this respect. As well-known clinical-/paraclinical-/functional evaluations, using assessment quantitative (as much as possible) instruments, are basic endeavors for rehabilitation, as they enable setting of appropriate and realistic therapeutic-rehabilitative specific goals.MethodsWithin the actual work, we have first achieved a narrative synthesis of the main molecular mechanisms involved in PD pathophysiology, underpinning its clinical appearance and evolution. To fundament our knowledge on an up-to-date information regarding the clinical-functional evaluation tools practiced in PD, we systematically reviewed the literature in this domain, published in the last 6 years, through a PRISMA type method for filtering/selecting the related bibliographic resources. The same keywords combinations/syntaxes have been used contextually, also to dialogize with ChatGPT4.o and ChatGPT.ResultsScholar Applying PRISMA type methodology (HI achieved), we have selected, matching the filtering criteria, 24 articles. Interrogating the two AI above-mentioned models, we obtained quite difficult to be availed/useful – comparative to our HI obtained – outcomes. Thus, when interrogating ChatGPT4.o, ChatGPT Scholar repeatedly, they provided - partially diverse - inappropriate related answers, including ones pending on the interrogator’s IP, although they claimed to have this capacity.DiscussionWe consider, regarding their capabilities to achieve systematic literature reviews, that neither ChatGPT 4.o nor ChatGPT Scholar still cannot succeed this (yet, they keep improving lately). Additionally, we have consistently extended, including within a narrative related literature review, our ‘dialogue” with these two AI facilities regarding their availability to enhance the related evaluation instruments accuracy on neurofunctional assessments within biomarker-based frameworks. So, our research aimed basically to emphasize the main topical data regarding these two important paradigms of knowledge (based on HI and on AI) acquirements – considering the impetuous development of the latter – and thus, possibly to contribute inclusively at improving the actual performances to achieve Systematic Literature Reviews through the PRISMA type method – for the moment still better served by HI.

Introduction. The relevance of the study is explained by the fact that in the age of rapid development of science and technology related to the development and application of the concept of machine learning, operating with big data, artificial intelligence (AI) has achieved the ability to surpass people in each separate area. The development of hybrid intelligence faces cognitive differences, the digital gap between man and machine, and other challenges. Strengthening the interaction between cognition and perception can be the key to overcoming weaknesses. The purpose of the article is to conduct research on the development of a model of human-computer interaction, to study a new model of this process.Materials and Methods. The work uses analysis and synthesis, a comparative method. The architecture of hybrid intelligence is built on the basis of a combination of the idea of human participation in the process and the confrontation between man and machine. The method of comparative and systematic analysis of similarities and differences between human intelligence, AI, and AI systems is also used.Results. There are three main cognitive perspectives in current AI research: technological, human, and human-machine cooperation. Modern research mainly focuses on the theoretical elaboration and application of mixed intelligence and the construction of a hybrid intelligent architecture model. The confrontation between man and machine cannot be understood simply as competition between man and machine, and its ultimate goal is still to achieve a harmonious stable state of symbiosis between man and machine. It is emphasized that the interaction of man and computer involves the participation of man and machine, while the degree and status of this participation differ. “Interaction” reflects two-way information transmission and is machine-driven, while “cooperation” reflects human-dominated shared decision-making.Discussion and Conclusion. Currently, a new round of scientific and technological revolution and industrial transformation is taking place. Big data applications and innovations in theoretical algorithms are driving intelligence. Technologies such as 5G and cloud intelligence take the development of distributed artificial intelligence to the next level allowing data to generate knowledge through cloud learning. Thus, thanks to the cooperation of machine intelligence and human mind, the merger of man and machine became the ultimate goal of the intelligence development. Human mind and machine intelligence adapt to each other and realize a common evolution and optimization of intelligence. Hybrid intelligence is a highly converged combination of rationality and sensitivity, intuition and logic, memory and data storage, as well as calculations, which is the future direction of the intelligent technologies’ development.

This chapter explores the nature of intelligence itself, conceptualizing it as a massive operating system that functions on biological technology as human intelligence (HI), or digital technology as artificial intelligence (AI). Through the lens of biological technology, the operating system contains many different biological individuals, each with an evolved range of dimensional “capabilities” needed to exist in the world environment. The operating system itself can be considered a hyperselfish intelligence (HYPERINT) development system that is relentlessly evolving, and exploitatively optimizing the development of the highest performing technologies within the world environment, regardless of the needs of individuals or a species, in order to ruthlessly maximize its capabilities. This chapter argues that HYPERINT is driving humans towards a situation where AI technologies could rapidly lead to the development of artificial general intelligence (AGI) with super intelligence that vastly exceeds HI and human controllability. Strong global regulation of AI is needed to protect human civilization from the most extreme risks of AI and AGI. In conclusion, the chapter argues that the global community must make a transition out of Industry 4.0, which focuses on progressive total automation of human work, and move towards the goals of Industry 5.0, which are more human-centric and globally sustainable.

Purpose: The provision of a method for thoughtful decision-making is the core purpose of artificial intelligence research and development. The primary goal of artificial intelligence (AI) is to give computers the ability to do intellectual tasks such as making decisions, solving problems, seeing their surroundings, and understanding human communication. Amazon is famous for using robots—roughly 30,000 of them—within its distribution centres. The company has gained mechanical autonomous organisation, which allows the robots to function independently. Kiva in 2012. Retailers can improve their demand estimates, make better pricing decisions, and optimise product placement with the aid of AI. The end result is that customers are connected with the proper products at the suitable time, in the appropriate place, and at the appropriate price. Utilizing predictive analytics may assist in determining the amount of a product that should be ordered to ensure that shops do not end up with either an excess or a shortage of inventory. Design/Methodology/Approach: The efficiency of our workplaces may be substantially improved by the use of artificial intelligence. When AI is utilised to do tasks that are boring or dangerous, human workers are given more time to concentrate on endeavours that need capabilities such as creativity and empathy, amongst other abilities. Artificial intelligence (AI) may be of assistance to a corporation in three different areas: the automation of corporate processes; the acquisition of insight through data analysis; and the interaction with consumers and staff. Findings/Result: As a result of AI, individuals will be freed up to focus on the 20% of non-routine jobs that account for 80% of the value they create. In the future, "intelligent automation of process change" (IAPC) will be used by smart machines to constantly examine and improve the whole process of a business's response to artificial intelligence. Automated and optimised everyday chores save your time and money and improves operational efficiency and productivity. The outputs of cognitive technology may help you make quicker business judgments and benefits the AI in the Workplace. Doing the same thing over and over again might take a toll on your workers' productivity. Automating and optimising these procedures will save you money. Employees will be able to concentrate on multiple areas at once, resulting in increased production. As AI processes data more quickly than humans, this leads to increased productivity. Originality/Value: It is essential to have a deeper comprehension of the differences between AI and human intelligence if we are to be adequately prepared for a society in which AI will play a much more pervasive role in our everyday lives in the foreseeable future. The process of reproduction is intricately connected to both biological and human intelligence. The advancement of artificial intelligence (AI) is significant for the reason that it paves the way for software to perform human activities and cost-effective than it was previously possible. Paper Type: Company Analysis.

Augmented intelligence (AuI) integrates human intelligence (HI) and artificial intelligence (AI) to harness their strengths and mitigate their weaknesses. The combination of HI and AI has seen to improve both human and machine capabilities, and achieve a better performance compared to separate HI and AI approaches. In this paper, we present a survey of literature to understand how AuI has been applied in the literature, including the roles of HI and AI, AI approaches, features, and applications. Due to the limited literature related to this topic, we also present a survey of expert opinion to answer four main questions to understand the experts’ implications of AuI, including: a) the definition of AuI and the significance of HI in AuI; b) the roles of HI in AuI; c) the current and future applications of AuI in research, industry, and public, as well as the advantages and shortcomings of AuI; and d) end users’ view of the application of AuI. We also present recommendations to improve AuI, and provide a comparison between the findings from the surveys of both literature and expert opinion. The discussion of this paper shows the promising potential of AuI compared to separate HI and AI approaches.

The relevance of research is due to the rapid development of artificial intelligence. It is an important technology that supports everyday social, technical, and economic activities. Artificial intelligence allows computers to learn from their own experience, adapt to set parameters, and perform tasks that were previously only possible for humans. In this regard, this article is aimed at identifying trends and prospects for the development of artificial intelligence. Another considerable task is to highlight the principles of building artificial intelligence systems. Developing an artificial intelligence system differs from building a conventional system as it requires a systematic approach, big data analysis, and model training. Building an artificial intelligence system − is a detailed process of reverse engineering human traits, capabilities of a machine, and using its computational power to surpass humans' skills. The leading approach to the study of this issue is literature analysis, which makes it possible to comprehensively consider artificial intelligence development. This article includes the modern foundations of artificial intelligence and various representative applications. In the context of the modern digital world, artificial intelligence is the property of machines, computer programmes and systems to perform intellectual and creative human functions, independently find ways to solve issues, be able to draw conclusions and make decisions. The research materials are of practical value for a critical analysis of current artificial intelligence capabilities, reasons why it still cannot achieve human intelligence, and the challenges it faces when achieving and surpassing the level of human intelligence

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.

Artificial intelligence, due to being heavily researched and funded, reaches new peaks of performance by the hour. In his new book, Gigerenzer (2022) addresses the predominantly positive perspective on AI with an advocacy for the uniqueness of the human intellect. He outlines strengths of human intelligence and the failures as well as dangers of AI. While the book presents an enlightening case for human intelligence, the author misses out on exploring a more productive approach: The synthesis of human intelligence and AI. In the present review, I introduce strengths and weaknesses of both types of intelligence and focus on the potential of synthetic cooperation between them. I support my plea for cooperation with two recent research ventures, namely, the regulation of digital social media platforms and predicting the societal effects of emerging innovations.

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.

Artificial Intelligence (AI) is a branch of computer science that seeks to simulate certain aspects of human intelligence [1]. Specifically, it aims to enable computers and software to impersonate human cognitive functions such as thinking, planning, learning, communicating, perceiving the environment, and moving and manipulating objects [2-3]. Such activities are generally considered to require intelligence when performed by humans or other animals. Although AI has achieved significant success in many areas, it still has some limitations. The development of AI can be broadly categorized into three groups: Narrow AI, Artificial General Intelligence, and Artificial Superintelligence. Narrow AI can perform certain specific tasks at a narrow level of intelligence. General AI can perform any intellectual task in various domains that humans are capable of. Superintelligent AI can perform intellectual tasks surpassing human intelligence [2,4]. As the definition of intelligence is subjective and no clear consensus exists, these categorizations are based on possible distinctions rather than standards. Regardless of these limitations and classifications, current advances in AI have led to widespread usage in various sectors, including e-commerce, education, research, and service industries.