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Neuroinformatics is a broad and rapidly evolving discipline concerned with applying information technology and computer science to solve challenges and answer pressing questions in the field of neuroscience. An emerging field of neuroinformatics attempts to model the cellular structures, properties, and functions of neural networks and their applic...
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... It is recognized that Cognitive Informatics (CI) [1]. [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19] is a transdisciplinary field that studies the internal information processing mechanisms of the brain [20], [21], [22], the underlying abstract intelligence (I) theories [23], [24], [25], [26], intelligent mathematics (IM) [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], and their engineering applications in cognitive computing, computational intelligence, and cognitive systems [56] [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [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], [101]. Cognitive Computing (CC) [3], [4], [15], [16], [27], [28], [50] is cutting-edge paradigms of intelligent computing methodologies based on CI, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. ...
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.]
... Cognitive Informatics (CI) [1]. [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19] is a transdisciplinary field that studies the internal information processing mechanisms of the brain [20], [21], [22], the underlying abstract intelligence (σI) theories [23], [24], [25], [26], intelligent mathematics (IM) [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], and their engineering applications in cognitive computing, computational intelligence, and cognitive systems [56] [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [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], [101], [174], [175], [176], [177], [178], [179], [180], [181], [182], [183], [184], [185]. ...
Basic research in Cognitive Informatics (CI) and Cognitive Computing (CC) provides fundamental theories of intelligence science for Autonomous AI (AAI) and cognitive systems. The field of CI and CC has led to general AI technologies triggered by the transdisciplinary advances in brain, intelligence, computer, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary report of the plenary panel (Part II) on "Recent Advances in Cognitive Informatics and Cognitive Computing towards Autonomous Systems" in the 21st IEEE International CI/CC Conference (ICCI*CC'22). Strategic CI/CC applications are presented in cognitive systems, AAI, cognitive robots, intelligent vehicles, AI knowledge learning, autonomous intelligence generation, cognitive digital twins, and safety-and-mission-critical systems.
... A pinnacle of IM is the recent proofs of the world top-ten hardest problems known as the Goldbach conjecture [39], [40], [41] and the Twin-Prime conjecture [42], [43] by the author. IM has led to unprecedented theories and general methodologies for implementing κC towards autonomous intelligence generation by autonomous inferencing machines [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62]. ...
... It is recognized that Cognitive Informatics (CI) [1]. [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19] is a transdisciplinary field that studies the internal information processing mechanisms of the brain [20], [21], [22], the underlying abstract intelligence (I) theories [23], [24], [25], [26], intelligent mathematics (IM) [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], and their engineering applications in cognitive computing, computational intelligence, and cognitive systems [56] [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [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], [101]. Cognitive Computing (CC) [3], [4], [15], [16], [27], [28], [50] is cutting-edge paradigms of intelligent computing methodologies based on CI, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. ...
A presentation of the front-matters and keynotes of 2022 IEEE 21st International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC 2022).
... It is recognized that Cognitive Informatics (CI) [1]. [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19] is a transdisciplinary field that studies the internal information processing mechanisms of the brain [20], [21], [22], the underlying abstract intelligence (I) theories [23], [24], [25], [26], intelligent mathematics (IM) [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], and their engineering applications in cognitive computing, computational intelligence, and cognitive systems [56] [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [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], [101]. Cognitive Computing (CC) [3], [4], [15], [16], [27], [28], [50] is cutting-edge paradigms of intelligent computing methodologies based on CI, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. ...
Basic research in Cognitive Informatics (CI) and Cognitive Computing (CC) provides fundamental theories of intelligence science for Autonomous AI (AAI) and cognitive systems. The field of CI and CC has led to general AI technologies triggered by the transdisciplinary advances in brain, intelligence, computer, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part I) on the "Recent Breakthroughs in Cognitive Informatics and Cognitive Computing towards AAI" in the 21st IEEE International ICCI*CC Conference (ICCI*CC'22). Strategic CI/CC applications are presented in cognitive systems, AAI, cognitive robots, intelligent vehicles, AI knowledge learning, autonomous intelligence generation, and safety-and-mission-critical systems.
... It is discovered that human and machine intelligence is dually aggregated from (sensory | data), (neural signaling | information), (semantic networks | knowledge), and (autonomous behaviors | intelligence) in a recursive framework, where the former are embodied in neural structures of the brain [9], [62], [63], [64], [65], [66], [67], [68], while the latter are represented in abstract (mathematical) forms in autonomous AI [14], [69]. Therefore, natural intelligence (NI) and AI are equivalent counterparts in philosophy and mathematics [10], [70] sharing a unified theoretical foundation of intelligence science represented by the Hierarchical Intelligence Model (HIM) [6], [20], which are compatible to those of human brain as revealed by the Layered Reference Model of the Brain [13]. ...
The theoretical bottlenecks and technical challenges towards fully intelligent and autonomous AI are fundamentally rooted in current technologies of pretrained AI and preprogramed computing. Basic studies in contemporary Intelligence Science have revealed that the natural intelligence of the brain is not directly generated by data nor implemented by predetermined behaviors. This leads to contemporary transdisciplinary investigations into the nature of general intelligence shared by human and machines, their theoretical foundations, and novel mathematical means for expressing and manipulating the unprecedented intelligent entities across the real and abstract worlds.
It is discovered that human and machine intelligence is dually aggregated from (sensory | data), (neural signaling | information), (semantic networks | knowledge), and (autonomous behaviors | intelligence) in a recursive framework, where the former are embodied in neural structures of the brain, while the latter are represented in abstract (mathematical) forms in autonomous AI. Therefore, natural intelligence (NI) and AI are equivalent counterparts in philosophy and mathematics sharing a unified theoretical foundation of intelligence science represented by the Hierarchical Intelligence Model (HIM), which are compatible to those of human brain as revealed by the Layered Reference Model of the Brain.
The unified theoretical framework of intelligence science based on HIM and LRMB has triggered Cognitive AI (CAI) and symbiotic human-machine intelligence underpinned by Intelligent Mathematics (IM). IM has shed light to the solving of challenging NI/AI problems by a rigorous and generic methodology. A pinnacle of IM is the recent proofs of the world top-ten hardest problems: the Goldbach conjecture and the Twin-Prime conjecture. Paradigms of IM encompass inference algebra, concept algebra, semantic algebra, real-time process algebra (RTPA), system algebra, causal probability algebra, big data algebra, image frame algebra, relation algebra, etc. IM leads to unprecedented theories and methodologies for implementing cognitive computers and cognitive robots towards a symbiotic NI and AI coherently sharable by human brain and intelligent machines in order to achieving the ultimate aims of intelligence science and autonomous computing.
... This leads to the latest discoveries on that the basic unit of human knowledge as a hyperstructure is a binary relation (bir). Therefore, a new framework of Intelligence Mathematics (IM) [22], [23], [24], [25], [26], [27], [28], [29], [30] has been created for rigorously manipulating the cognitive entities in the brain and cognitive robots spanning from formal concepts, semantics, knowledge, causalities, inferences, and consciousness by contemporary mathematical means for advancing both AI [31], [32], [33] and SE [34], [35], [36], [37]. ...
Breakthroughs of basic research in Intelligence Science (IS) and Cognitive Computing (CC) have triggered the emergence of Cognitive Robots (CR) and AI Programming (AIP) technologies towards autonomous software engineering. The latest advances enable the synergy of classical pre-programmed AI and pre-trained AI to an unprecedented platform for machine intelligence generation mimicking human brains and brain-inspired cognitive systems. This keynote lecture presents fundamental theories and groundbreaking technologies for designing and implementing cognitive robots based on AIP and autonomous intelligence generation methodologies 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. The keynote will demonstrate disruptive technologies encompassing autonomous systems, cognitive robots, real-time autonomous learning and trustworthy systems for symbiotic human-machine applications.
... This keynote lecture presents the theoretical framework of IS and the emerging technology for autonomous machine intelligence generation underpinned by contemporary IS [7], Intelligent Mathematics (IM) [8], [13], [14], [15], [16], [17], [18], [19], [20], and Brain-Inspired Systems (BIS) [21], [22], [23], [24], [25], [26]. A set of disruptive technologies in IS triggered by the AIG theory and IM will be presented encompassing autonomous systems [27], [28], [29], [30], [31], cognitive robots [32], [33], real-time autonomous learning [34], [35], and trustworthy systems for human-machine symbiotic applications [37], [38], [39]. ...
Latest advances in Cognitive Computing (CC) [1], [2] and Cognitive Informatics (CI) [3], [4], [5], [6] have let to the emergence of Intelligence Science (IS) [7] towards General AI (GAI) [36] and Autonomous Systems (AS) [2], [7], [8]. Intelligence science paves a way for Autonomous Intelligence Generation (AIG) beyond classic pre-trained Neural-Network-Regression (NNR) or pre-Stored-Program-Controlled (SPC) technologies for AI. The AIG theory enables the implementation of brain-inspired cognitive intelligence from machine learning to machine thinking and inferences [9], [10], [11], [12] in order to address the constraints and challenges to classical imperative SPC intelligence and reflexive NNR intelligence towards human-like autonomous intelligence.
This keynote lecture presents the theoretical framework of IS and the emerging technology for autonomous machine intelligence generation underpinned by contemporary IS [7], Intelligent Mathematics (IM) [8], [13], [14], [15], [16], [17], [18], [19], [20], and Brain-Inspired Systems (BIS) [21], [22], [23], [24], [25], [26]. A set of disruptive technologies in IS triggered by the AIG theory and IM will be presented encompassing autonomous systems [27], [28], [29], [30], [31], cognitive robots [32], [33], real-time autonomous learning [34], [35], and trustworthy systems for human-machine symbiotic applications [37], [38], [39], [40].
... Thinking and inference are key essences of human intelligence as René Descartes stated that "I think therefore I am (1637)." The ultimate goal of General AI (GAI) [1], [2], [3], [4] is to enable machine thinking and inference [5], [6], [7], [8], [9] beyond data-based learning [10], [11] towards run-time intelligence generation [9], [12] driven by Autonomous Systems (AS) [13], [14] and cognitive robots [15]. Basic research on machine thinking is powered by Abstract Intelligence theories [16], [17], [18], [19], [20], [21] and Intelligent Mathematics (IM) [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32] towards enabling symbiotic and collective intelligence [13], [33] and cognitive systems [34], [35], [36], [37], [38], [39]. ...
... The ultimate goal of General AI (GAI) [1], [2], [3], [4] is to enable machine thinking and inference [5], [6], [7], [8], [9] beyond data-based learning [10], [11] towards run-time intelligence generation [9], [12] driven by Autonomous Systems (AS) [13], [14] and cognitive robots [15]. Basic research on machine thinking is powered by Abstract Intelligence theories [16], [17], [18], [19], [20], [21] and Intelligent Mathematics (IM) [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32] towards enabling symbiotic and collective intelligence [13], [33] and cognitive systems [34], [35], [36], [37], [38], [39]. ...
... Cognitive Informatics (CI) [1]− [13] is a transdisciplinary field that studies the internal information processing mechanisms of the brain, the underlying abstract intelligence (I) theories [23]− [28], intelligent mathematics (IM) [29]− [50], and their engineering applications in cognitive computing, computational intelligence, and cognitive systems. Cognitive Computing (CC) [14]− [22] is cutting-edge paradigms of intelligent computing methodologies based on CI, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. ...
The 20th IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC 2021), a flagship conference in this field, has been held in Banff, Canada and online worldwide during Oct. 29-31, 2021. Cognitive Informatics (CI) is a transdisciplinary field that studies the internal information processing mechanisms of the brain, the underlying abstract intelligence (aI) theories and intelligent mathematics, and their engineering applications in cognitive computing, computational intelligence, and cognitive systems. Cognitive Computing (CC) is a cutting-edge paradigm of intelligent computing methodologies and systems based on cognitive informatics, which implements computational intelligence by autonomous inferences and perceptions mimicking the mechanisms of the brain. CI and CC not only synergize theories of modern information science, computer science, communication theories, AI, cybernetics, computational intelligence, cognitive science, intelligence science, neuropsychology, brain science, systems science, software science, knowledge science, cognitive robots, cognitive linguistics, and life science, but also promote novel applications in cognitive computers, cognitive communications, computational intelligence, cognitive robots, cognitive systems, and the AI, IT, and software industries.
... This keynote lecture will present basic research advances and their theoretical foundations for dealing with the aforementioned challenges. It explains how AI [22], [23], [24], [25], [26], [27], [28] and big data [29], [30], [31], [32], [33] engineering may learn from intelligence science underpinned by contemporary Intelligent Mathematics (IM) [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44] such as inference algebra (IA) [15] and big-data algebra (BDA) [29]. Applications of the findings and fundamental theories will be elaborated towards the development of unprecedentedly nonpretrained and non-preprogrammed autonomous AI [5]. ...
The emergence of Abstract Sciences (AS) [1], [2], [3], [4], [5], [6], [7], [8] has triggered the synergy of contemporary disciplines of data, information, knowledge, and intelligence sciences in a coherent and hierarchical framework. Two of the fundamental queries in AS are: a) If human or machine intelligence may merely be generated from big data? and b) How may data-regression-based machine intelligence produce explainable, distinguishable, and casual wisdom? These questions have led to a series of basic research [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21] on the essences of intelligence and its aggregation from data, information, and knowledge to derivable intelligent behaviors. They also reveal the differences between data-driven and inference-driven intelligence where the former is a one-off reflexive regression of collective factors, but the latter is rigorously constrained by both sufficient and necessary conditions for causal inference based on rigorous rules [9, 15, 29, 37] and cumulatively acquired knowledge [10], [12].
For instances: a) Given a logic AND-gate with 10,000 input-pins, a training based on the first 2^9,999 sets of big data in the given space will lead to a false learning result that would recognize the AND-gate as a dummy device because its output is always zero no matter what the inputs would be; and b) All big data would indicate that humans get older along the counter-clockwise rotation of the earth. Therefore, data-driven learning may result in a false conclusion that the aging process may be reversed if a person moves to another planet that rotates otherwise. Both failed learning cases indicate a vital risk of the data-driven mechanism of learning in AI, because it dissatisfies the necessary and sufficient inferencing causality for rigorous machine learning towards generating trustworthy machine intelligent.
This keynote lecture will present basic research advances and their theoretical foundations for dealing with the aforementioned challenges. It explains how AI [22], [23], [24], [25], [26], [27], [28] and big data [29], [30], [31], [32], [33] engineering may learn from intelligence science underpinned by contemporary Intelligent Mathematics (IM) [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44] such as inference algebra (IA) [15] and big-data algebra (BDA) [29]. Applications of the findings and fundamental theories will be elaborated towards the development of unprecedentedly non-pretrained and non-preprogrammed autonomous AI [5].
... The neurophysiological foundation for neurosignal transmission in the nervous system is embodied by uniformed neurosignals (spikes), neural clusters (circuits), and neural networks at different levels [6], [7], [14]− [16], [22], [24], [25]. The spikes, a form of electrochemical impulses, are information carrier uniformed across neural networks in the brain. ...
Neurorehabilitation is an emerging and transdisciplinary field that is not only highly demanded in medical science and healthcare, but also a challenging problem in cognitive neurology, neurosignaling theory, neuroinformatics, and brain science. This paper presents findings in basic research on neuroinformatics towards neurorehabilitation by exploring its cognitive and mathematical foundations. It introduces novel theories for neurorehabilitation encompassing neural spike frequency modulation, basic neural circuits, and neurosignaling pathways. It develops diagnostic and assessment technologies for clinic neurorehabilitation such as consciousness status and cognitive memory recovery.
