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Brain-Inspired Systems (BIS) are an emerging field of brain and intelligence sciences that studies natural intelligence models of AI and cognitive systems in one direction, and the formal models of the brain simulated by computational intelligence in another direction. A typical BIS is the cognitive robots that mimic and implement the brain through...
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... brain as the most matured intelligent organ is an ideal reference model for revealing the theoretical foundations of AI and what AI may do or not do constrained by the nature and the expressive power of current mathematical means for computational implementations. A Layered Reference Model of the Brain (LRMB) [15] is introduced as shown in Figure 1 as an overarching logical architecture of the mechanisms of the brain. The four lower layers of LRMB such as those of sensation, action, memory and perception are classified as subconscious mental functions of the brain equivalent to a Brain Operating System (BOS). ...
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Brain-Inspired Systems (BIS') are an emerging field of brain and intelligence sciences that studies natural intelligence models of AI and cognitive systems in one direction, and the formal models of the brain simulated by computational intelligence in another direction. A typical BIS is the cognitive robots that mimic and implement the brain throug...
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... The human brain provides an ideal reference model for the foundations of artificial intelligence, an indispensable IT tool that processes large data sets based on numerical algorithms to obtain solutions to problems in focus (Wang, Sun & Chen, 2023). The study of the foundation of natural intelligence may contribute to both the understanding of the general mechanics of intelligence toward pervasive brain-inspired systems and the formulation of intelligent entities (Wang et al., 2018). While the development of AI, including deep learning where algorithms are becoming more precise, faster, and able to reproduce the processes taking place in the human brain more faithfully, is rapidly permeating virtually all aspects of everyday life, necessary human dimensions such as ethical behavior and learning practice need to be accounted for and integrated. ...
Artificial Intelligence (AI) has an increasing role to play in medical education and has great potential to revolutionize health professional education systems overall. However, this is accompanied by substantial questions concerning technical and ethical risks which are of particular importance because the quality of medical education has a direct effect on physical and psychological health and wellbeing. This article establishes an overarching distinction of AI across two typological dimensions, functional and humanistic. As indispensable foundations, these are then related to medical practice overall, and forms of implementation with examples are described in both general and medical education. Increasingly, the conditions for successful medical education will depend on an understanding of AI and the ethical issues surrounding its implementation, as well as the formulation of appropriate guidelines by regulatory and other authorities. Within that discussion, the limits of both narrow or Routine AI (RAI) and artificial general intelligence or Decision AI (DAI) are examined particularly in view of the ethical need for Trustworthy AI (TAI) as part of the humanistic dimension. All stakeholders, from patients to medical practitioners, managers, and institutions, need to be able to trust AI, and loss of confidence could be catastrophic in some cases.
... Despite many advances, we are unsure of human brain complexity. Thus, the study of the foundations of natural intelligence may contribute to both the understanding of the general mechanics of intelligence regarding pervasive brain-inspired systems [5,6], and the formulation of intelligent entities. At the moment, artificial intelligence is far from replicating the entire human brain. ...
Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies, as well as increasing the effectiveness of the training process. In this context, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in medical image scan segmentation and intelligent visual content generation, i.e., computer-generated three-dimensional (3D) images in the context of extended reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. It concludes with possible developments and open challenges in AI applications in extended reality-based solutions. Finally, future lines of research and development directions of artificial intelligence applications, both in medical image segmentation and extended reality-based medical solutions, are discussed.
... Cognitive framework of the brain (CFB). (Birattari and Kacprzyk, 2001;Kacprzyk and Yager, 2001;Pedrycz and Gomide, 2007;Rudas and Fodor, 2008;Siddiqi and Pizer, 2008;Wang et al., 2009Wang et al., , 2016bWang et al., , 2018Cios et al., 2012;Wang, 2012aWang, , 2016aWang, , 2020Wang, , 2022cBerwick et al., 2013;Plataniotis, 2022;Huang et al., 2023) theories and technologies beyond classical pretrained AI based on data convolution and preprogrammed computing underpinned by pre-deterministic run-time behaviors. ...
Cognitive computers (κC) are intelligent processors advanced from data and information processing to autonomous knowledge learning and intelligence generation. This study presents a retrospective and prospective review of the odyssey toward κC empowered by transdisciplinary basic research and engineering advances. A wide range of fundamental theories and innovative technologies for κC is explored, and a set of underpinning intelligent mathematics (IM) is created. The architectures of κC for cognitive computing and Autonomous Intelligence Generation (AIG) are designed as a brain-inspired cognitive engine. Applications of κC in autonomous AI (AAI) are demonstrated by pilot projects. This study reveals that AIG will no longer be a privilege restricted only to humans via the odyssey to κC toward training-free and self-inferencing computers.
... Cognitive framework of the brain (CFB). (Birattari and Kacprzyk, 2001;Kacprzyk and Yager, 2001;Pedrycz and Gomide, 2007;Rudas and Fodor, 2008;Siddiqi and Pizer, 2008;Wang et al., 2009Wang et al., , 2016bWang et al., , 2018Cios et al., 2012;Wang, 2012aWang, , 2016aWang, , 2020Wang, , 2022cBerwick et al., 2013;Plataniotis, 2022;Huang et al., 2023) theories and technologies beyond classical pretrained AI based on data convolution and preprogrammed computing underpinned by pre-deterministic run-time behaviors. ...
IEEE ICCI*CC'23: Stanford University, August 19-21, 2023.
Themes: Fundamental Challenges to Basic Research on the Theories and Methodologies of AI and Cognitive/Intelligent Computing.
• What is the next generation of intelligent computers underpinned by discoveries in intelligence science: General cognitive computers vs. pretrained AI systems? [Ref. 1: The Odyssey to Cognitive Computers.]
• May the classic preprogrammed (von Neumann) computers deal with infinitive and unpredictable real-world problems at run-time? [Ref. 2: Cognitive Computing and Autonomous AI (AAI).]
• Will the pretrained AI systems (such as Chat-GPT) be able to exhaustively cover the entire state space of machine reasoning beyond the coverage of their training models: Enumerational knowledge (empirical) vs. human knowledge (Induction/deduction-based)? [Ref. 3: What can’t Chat-GPT do?]
• Will intelligence science enable AI systems to learn human inference mechanisms rather than let them to collect infinitive factors and unstructured knowledge by so called large-language models? How are the essential forms of human intelligence and wisdom generated beyond big data and empirical learning? [Ref. 4: Intelligent Mathematics.]
... But we can adopt a simpler approach by simulating and modeling sub-organizational levels of the brain or modeling a simple nervous system like C. elegans [6][7][8] organism to understand complex systems. Instead of accurately creating the brain, we can adopt brain dynamics principles to create a braininspired AI [9][10][11][12] model to represent complex Spatio-temporal patterns. ...
Neuronal population activity in the brain is the combined response of information in the spatial domain and dynamics in the temporal domain. Modeling such Spatio-temporal mechanisms is a complex process because of the complexity of the brain and the limitations of the hardware. In this paper, we demonstrate how information processing principles adapted from the brain can be used to create a brain-inspired artificial intelligence (AI) model and represent Spatio-temporal patterns. The same is demonstrated by designing the tiny brain using spiking neural networks, where activated neuronal populations represent information in the spatial domain and transmitting signals represent dynamics in the temporal domain. Spatially located sensory neurons excited by input visual stimuli further activate motor neurons to trigger a motor response that causes behavior modification of the robotic agent. Initially, an isolated brain network is simulated to understand the excitation part from sensory to motor neurons while plotting waveform between membrane potential and time. The response of the network to stimulate robot body movements is also plotted to demonstrate representation. The simulation shows how the response of particular visual stimuli modifies behavior and helps us understand the body and brain synchronization. The perceived environment and resultant behavior response allow us to study body interaction with the environment.
... Fulfilling almost all higher cognitive processes (e.g., comprehension, learning, problem solving, and long-term memory establishment) relies on the support of conscious and subconscious processes. Abstract Intelligence (αI) is defined as a general mathematical model for intelligence and it is an important theory in intelligence and brain science [11]. ...
... The brain-inspired system (BIS) is a relatively new field of brain and intelligence science that deals with natural intelligence models in cognitive and AI systems as well as formal models of the human brain that is analyzed and simulated using computational methods. BIS is an advanced and promising area of cognitive systems underpinned by transdisciplinary theories related to brain, cybernetic and cognitive science, denotational mathematics, knowledge, intelligence, and system [11]. ...
... An abstract intelligence model of the human brain is a top-level system model that reduces the intricate complexity of functional and structural models of the brain to lower-level details. The brain's system model helps BIS to mimic human's thinking, perception, inference and learning in many applications, including cognitive learning engines, cognitive control systems, cognitive robots, cognitive automobiles, cognitive translators, cognitive IoT, inter alia, unmanned systems as paradigms of brain-inspired systems [11]. ...
Legal prediction presents one of the most significant challenges when applying artificial intelligence (AI) to the legal field. The legal system is a complex adaptive system characterized by the ambiguity of legal language and the diversity of value functions. The imprecision and procedural knowledge inherent in law makes judicial issues difficult to be expressed in a computer symbol system. Current semantic processing and machine learning technologies cannot fully capture the complex nature of legal relations, thereby raising doubts about the accuracy of legal predictions and reliability of judicial models. Cognitive computing, designed to emulate human brain functions and aid in enhancing decision-making processes, offers a better understanding of legal data and the processes of legal reasoning. This paper discusses the advancements made in cognitive methods applied to legal concept learning, semantic extraction, judicial data processing, legal reasoning, understanding of judicial bias, and the interpretability of judicial models. The integration of cognitive neuroscience with law has facilitated several constructive attempts, indicating that the evolution of cognitive law could be the next frontier in the intersection of AI and legal practice.
Breakthroughs of basic research in Intelligence Science (IS) [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], Cognitive Computing (CC) [14], [15], [16], [17], [18], [19], [20], [21], [22], [22], [23], [24], [25], [26], and Cognitive Informatics (CI) [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], have triggered the emergence of Cognitive Robots (CR) [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49] towards software science (SS) [50], [51], [52], [53], [54], [55] and autonomous software engineering (SE) [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66]. These latest advances have enabled the synergy of classical pre-programmed software engineering and pre-trained AI to an unprecedented platform of training-free machine intelligence generation mimicking human brains and Brain-Inspired Systems (BIS) [67], [68], [69], [70], [71], [72], [73], [74]. It is discovered in IS and CC in general, as well as in CR and SE in particular, that the target entities across these contemporary fields had already out of the denotational power of the classic mathematical domains of real (R) and binary (B) numbers [76]. This leads to the latest discovery that the basic unit of human knowledge as a hyperstructure [50] is a binary relation (bir) [74]. Therefore, a new framework of Intelligence Mathematics (IM) [75], [76], [77], [78], [79], [80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90], [91], [92], [93], [94], [95], [96], [97], [98], [99], [100] has been created for rigorously manipulating the cognitive entities in the brain and CRs spanning from formal concepts, semantics, knowledge, causalities, inferences, and consciousness by contemporary IMs for advancing both AI [1], [2], [3] and SE [56], [58].
This keynote lecture presents fundamental theories and ground-breaking technologies for designing and implementing cognitive robots based on AI Programming (AIP) [101], [102], [103], [104], and Autonomous Intelligence Generation (AIG) methodologies [105], [106] beyond classic reflexive neural networks and imperative programming platforms. The latest advances have paved a new way for cognitive computing and autonomous software generation, which put the house before the cart for the software and AI industries [51], [56]. The keynote will demonstrate disruptive technologies encompassing autonomous systems, cognitive robots, real-time autonomous learning, and trustworthy systems for symbiotic human-machine applications [107], [108], [109], [110], [111], [112], [113], [114], [115], [116], [117], [118], [119], [120], [121], [122], [123], [124], [125], [126].
The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately, many application domains do not have access to big data because acquiring data involves a process that is expensive or time-consuming. This has triggered a serious debate in both the industrial and academic communities calling for more data-efficient models that harness the power of artificial learners while achieving good results with less training data and in particular less human supervision. In light of this debate, this work investigates the issue of algorithms’ data hungriness. First, it surveys the issue from different perspectives. Then, it presents a comprehensive review of existing data-efficient methods and systematizes them into four categories. Specifically, the survey covers solution strategies that handle data-efficiency by (i) using non-supervised algorithms that are, by nature, more data-efficient, by (ii) creating artificially more data, by (iii) transferring knowledge from rich-data domains into poor-data domains, or by (iv) altering data-hungry algorithms to reduce their dependency upon the amount of samples, in a way they can perform well in small samples regime. Each strategy is extensively reviewed and discussed. In addition, the emphasis is put on how the four strategies interplay with each other in order to motivate exploration of more robust and data-efficient algorithms. Finally, the survey delineates the limitations, discusses research challenges, and suggests future opportunities to advance the research on data-efficiency in machine learning.
