Cognitive computers (κ C ) are intelligent processors advanced from data and information processing to autonomous knowledge learning and intelligence generation. This work presents a retrospective and prospective review of the odyssey toward κ C empowered by transdisciplinary basic research and engineering advances. A wide range of fundamental theories and innovative technologies for κ C is explored, and a set of underpinning intelligent mathematics (IM) is created. The architectures of κ C for cognitive computing and Autonomous Intelligence Generation (AIG) are designed as a brain-inspired cognitive engine. Applications of κ C in autonomous AI (AAI) are demonstrated by pilot projects. This work reveals that AIG will no longer be a privilege restricted only to humans via the odyssey to κ C toward training-free and self-inferencing computers.

Hebbian learning is widely accepted in the fields of psychology, neurology, and neurobiology. It is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world's most widely used adaptive algorithm, fundamental in the fields of signal processing, control systems, pattern recognition, and artificial neural networks. These are very different learning paradigms. Hebbian learning is unsupervised. LMS learning is supervised. However, a form of LMS can be constructed to perform unsupervised learning and, as such, LMS can be used in a natural way to implement Hebbian learning. Combining the two paradigms creates a new unsupervised learning algorithm that has practical engineering applications and provides insight into learning in living neural networks. A fundamental question is, how does learning take place in living neural networks? "Nature's little secret," the learning algorithm practiced by nature at the neuron and synapse level, may well be the Hebbian-LMS algorithm.

Norbert Wiener invented the word cybernetics and wrote a book by that name. The subject was feedback and control in the human body. The new book, “Cybernetics 2.0: a General Theory of Adaptivity and Homeostasis in the Brain and in the Body” follows in Wiener's footsteps. It is different as it introduces learning algorithms to Wiener's subject. Learning algorithms did not exist in Wiener's day. In the synapse, information is carried by neurotransmitter which in turn binds to neuroreceptors. The synapse is the coupling device from to neuron. The strength of the coupling, the “weight”, is proportional to the number of receptors. Their numbers can increase or decrease, as upregulation or downregulation. A mystery in neuroscience is, what is nature's algorithm for controlling upregulation and downregulation? Start with Hebbian learning and generalize it to cover downregulation as well as upregulation, and inhibitory as well as excitatory synapses. What results is a surprise ! We have an unsupervised form of the LMS(least mean square) algorithm of Widrow and Hoff. The Hebbian-LMS algorithm encompasses Hebb's learning rule (fire together, wire together), and introduces homeostasis into the equation. A neuron's “normal” firing rate is set by homeostasis. Physical evidence supports Hebbian-LMS as being nature's learning rule. LMS binds nature's learning to learning in artificial neural networks. The same Hebbian-LMS algorithm is key to the control of all the organs of the body, where hormones bind to hormonereceptors and there is upregulation and downregulation in the control process.

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.

Hebbian learning is widely accepted in the fields of psychology, neurology, and neurobiology. It is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world’s most widely used adaptive algorithm, fundamental in the fields of signal processing, control systems, communication systems, pattern recognition, and artificial neural networks. These learning paradigms are very different. Hebbian learning is unsupervised. LMS learning is supervised. However, a form of LMS can be constructed to perform unsupervised learning and, as such, LMS can be used in a natural way to implement Hebbian learning. Combining the two paradigms creates a new unsupervised learning algorithm, Hebbian-LMS. This algorithm has practical engineering applications and provides insight into learning in living neural networks. A fundamental question is, how does learning take place in living neural networks? "Nature’s little secret," the learning algorithm practiced by nature at the neuron and synapse level, may well be the Hebbian-LMS algorithm.

Cognitive Informatics (CI) and Cognitive Computing (CC) are fundamental intelligence theories and general AI technologies triggered by the transdisciplinary advances in intelligence, computer, brain, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part I) on the theoretical foundations of CI/CC as well recent breakthroughs in AI engineering reported in the 20th IEEE International ICCI*CC Conference (ICCI*CC'21). The latest advances in CI and CC towards general AI are presented by twenty-two distinguished panelists. Strategic AI engineering applications in CI, CC, and cognitive systems are elaborated for abstract intelligence, general AI, cognitive robots, autonomous systems, intelligent vehicles, and safety-and-mission-critical systems.

International Journal of Neural SystemsVol. 31, No. 12, 2103009 (2021) EditorialFree AccessEditorial: Celebrating 30 Years of IJNS and the Next Industrial RevolutionBernard WidrowBernard WidrowDepartment of Electrical Engineering, Stanford University, Stanford, California, Member of the U.S. National Academy of Engineering, Co-Inventor of the Widrow-Hoff least mean squares filter (LMS) adaptive algorithm, USAhttps://doi.org/10.1142/S012906572103009XCited by:0 Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail References 1. H. Adeli and S. L. Hung, A concurrent adaptive conjugate gradient learning algorithm on MIMD machines, J. Supercomput. Appl. 7(2) (1993) 155–166. Crossref, ISI, Google Scholar2. S. L. Hung and H. Adeli, A parallel genetic/neural network learning algorithm for MIMD shared memory machines, IEEE Trans. Neural Netw. 5(6) (1994) 900–909. Crossref, Medline, ISI, Google Scholar3. H. Adeli and S. L. Hung, An adaptive conjugate gradient learning algorithm for effective training of multilayer neural networks, Appl. Math. Comput. 62(1) (1994) 81–102. Crossref, ISI, Google Scholar4. S. Ghosh-Dastidar and H. Adeli, Improved spiking neural networks for EEG classification and Epilepsy and seizure detection, Integr. Comput.-Aided Eng. 14(3) (2007) 187–212. Crossref, ISI, Google Scholar5. S. Ghosh-Dastidar and H. Adeli, A new supervised learning algorithm for multiple spiking neural networks with application in Epilepsy and seizure detection, Neural Netw. 22(10) (2009) 1419–1431. Crossref, Medline, ISI, Google Scholar6. M. Ahmadlou and H. Adeli, Enhanced probabilistic neural network with local decision circles: A robust classifier, Integr. Comput.-Aided Eng. 17(3) (2010) 197–210. Crossref, ISI, Google Scholar7. M. H. Rafiei and H. Adeli, A new neural dynamic classification algorithm, IEEE Trans. Neural Netw. Learn. Syst. 28(12) (2017) 3074–3083. Crossref, Medline, ISI, Google Scholar8. A. Hassanpour, M. Moradikia, H. Adeli, S. R. Khayami and P. Shamsinejad, A novel end-to-end deep learning scheme for classifying multiclass motor imagery EEG signals, Expert Syst. 36 (2019) 6. Crossref, ISI, Google Scholar9. D. R. Pereira, M. A. Piteri, A. N. Souza, J. Papa and H. Adeli, FEMa: A finite element machine for fast learning, Neural Comput. Appl. 32(10) (2020) 6393–6404. Crossref, ISI, Google Scholar10. K. M. R. Alam, N. Siddique and H. Adeli, A dynamic ensemble learning algorithm for neural networks, Neural Comput. Appl. 32(10) (2020) 8675–8690. Crossref, ISI, Google Scholar11. H. Adeli and S. L. Hung, Machine Learning: Neural Networks, Genetic Algorithms, and Fuzzy Systems (John Wiley & Sons, New York, 1995). Google Scholar12. H. Adeli and H. S. Park, Neurocomputing for Design Automation (CRC Press, Boca Raton, Florida, 1998). Crossref, Google Scholar13. H. Adeli and X. Jiang, Intelligent Infrastructure: Neural Networks, Wavelets, and Chaos Theory for Intelligent Transportation Systems and Smart Structures (CRC Press, Taylor & Francis, Boca Raton, Florida, 2009). Google Scholar14. N. Siddique and H. Adeli, Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing (Wiley, West Sussex, UK, 2013). Crossref, Google Scholar15. N. Siddique and H. Adeli, Nature Inspired Computing: Physics and Chemistry-based Algorithms (CRC Press, Taylor & Francis, Boca Raton, Florida, 2017). Crossref, Google Scholar Remember to check out the Most Cited Articles! Check out our titles in neural networks today! FiguresReferencesRelatedDetails Recommended Vol. 31, No. 12 Metrics History Published: 8 October 2021 PDF download

Hebbian learning is widely accepted in the fields of psychology, neurology, and neurobiology. It is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world's most widely used adaptive algorithm, fundamental in the fields of signal processing, control systems, communication systems, pattern recognition, and artificial neural networks. These learning paradigms are very different. Hebbian learning is unsupervised. LMS learning is supervised. However, a form of LMS can be constructed to perform unsupervised learning and, as such, LMS can be used in a natural way to implement Hebbian learning. Combining the two paradigms creates a new unsupervised learning algorithm, Hebbian-LMS. This algorithm has practical engineering applications and provides insight into learning in living neural networks. A fundamental question is how does learning take place in living neural networks? "Nature's little secret," the learning algorithm practiced by nature at the neuron and synapse level, may well be the Hebbian-LMS algorithm.

Hebbian learning is widely accepted in the fields of psychology, neurology, and neurobiology. It is one of the fundamental premises of neuroscience. The LMS (least mean square) algorithm of Widrow and Hoff is the world's most widely used adaptive algorithm, fundamental in the fields of signal processing, control systems, communication systems, pattern recognition, and artificial neural networks. These learning paradigms are very different. Hebbian learning is unsupervised. LMS learning is supervised. However, a form of LMS can be constructed to perform unsupervised learning and, as such, LMS can be used in a natural way to implement Hebbian learning. Combining the two paradigms creates a new unsupervised learning algorithm, Hebbian-LMS. This algorithm has practical engineering applications and provides insight into learning in living neural networks. A fundamental question is, how does learning take place in living neural networks? “Nature's little secret,” the learning algorithm practiced by nature at the neuron and synapse level, may well be the Hebbian-LMS algorithm.

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