Intelligent Computing: The Latest Advances, Challenges and Future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners

Intelligent computing : the latest advances, challenges and future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing

EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications.

Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact with the environment. Recent advancements in technology and machine learning algorithms have increased interest in electroencephalographic (EEG)-based BCI applications. EEG-based intelligent BCI systems can facilitate continuous monitoring of fluctuations in human cognitive states under monotonous tasks, which is both beneficial for people in need of healthcare support and general researchers in different domain areas. In this review, we survey the recent literature on EEG signal sensing technologies and computational intelligence approaches in BCI applications, compensating for the gaps in the systematic summary of the past five years. Specifically, we first review the current status of BCI and signal sensing technologies for collecting reliable EEG signals. Then, we demonstrate state-of-the-art computational intelligence techniques, including fuzzy models and transfer learning in machine learning and deep learning algorithms, to detect, monitor, and maintain human cognitive states and task performance in prevalent applications. Finally, we present a couple of innovative BCI-inspired healthcare applications and discuss future research directions in EEG-based BCI research

A Biosymtic (Biosymbiotic Robotic) Approach to Human Development and Evolution. The Echo of the Universe.

In the present work we demonstrate that the current Child-Computer Interaction paradigm is not potentiating human development to its fullest – it is associated with several physical and mental health problems and appears not to be maximizing children’s cognitive performance and cognitive development. In order to potentiate children’s physical and mental health (including cognitive performance and cognitive development) we have developed a new approach to human development and evolution. This approach proposes a particular synergy between the developing human body, computing machines and natural environments. It emphasizes that children should be encouraged to interact with challenging physical environments offering multiple possibilities for sensory stimulation and increasing physical and mental stress to the organism. We created and tested a new set of computing devices in order to operationalize our approach – Biosymtic (Biosymbiotic Robotic) devices: “Albert” and “Cratus”. In two initial studies we were able to observe that the main goal of our approach is being achieved. We observed that, interaction with the Biosymtic device “Albert”, in a natural environment, managed to trigger a different neurophysiological response (increases in sustained attention levels) and tended to optimize episodic memory performance in children, compared to interaction with a sedentary screen-based computing device, in an artificially controlled environment (indoors) - thus a promising solution to promote cognitive performance/development; and that interaction with the Biosymtic device “Cratus”, in a natural environment, instilled vigorous physical activity levels in children - thus a promising solution to promote physical and mental health

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Brain Waves, A Cultural History: Oscillations of Neuroscience, Technology, Telepathy, and Transcendence

This project proceeds from a narrow question: What, if anything, is a brain wave? Beguiling in its simplicity, this question prompts a cultural-historical investigation that spans over 150 years of science, technology, and society. Proposed in 1869, the original theory of brain waves cites etheric undulations to explain reports of apparent thought transference. Though most modern thinkers no longer believe in outright telepathy, I argue that dreams of thought transmission and other mental miracles subtly persist—not in obscure and occult circles, but at the forefront of technoscience. A hybrid of science and fiction, brain waves represent an ideal subject through which to explore the ways in which technical language shrouds spiritual dreams. Today, the phrase “brain waves” often function as shorthand for electrical changes in the brain, particularly in the context of technologies that purport to “read” some aspect of mental function, or to transmit neural data to a digital device. While such technologies appear uniquely modern, the history of brain waves reveals that they are merely the millennial incarnation of a much older hope—a hope for transmission and transcendence via the brain’s emanations

Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Cooperative Human-Machine Interaction in Industrial Environments

Until the present days, there has been little advances in the relation between the shop-floor operator in an industrial environment and the machines execution the manufacturing processes. Normally, the semi-automatic processes for collaborative assembly in industry are composed of a human and non-human elements. In the human perspective, one or more persons can be working in the same cell directly or indirectly with a non-human entity. In a cell can exist several machines, normally robotic arms that perform very specific collaborative tasks with the operators. However, the latest advances are mostly related with security issues and regulations, like immediately stopping the machine if a human touches it, and not much related with operative issues like adjusting the process velocity (within a certain window of cycle time) or give preference to some tasks over another in the beginning of the shift, to benefit the operator's working conditions. Therefore, a step forward to a more advanced interaction between machine and operator should be taken, towards a more adaptive and rich symbiosis. The main goal of the present Dissertation is to explore the relation between the shop-floor operator and the machine in a cyber physical system. For that purpose, biometric sensors will be used (ECG, EMG, EDA, PZT, wearables and others) to monitor the operators physiology during the operative times, and based on that, explore how a collaborative process can be adapted to minimize the operator's stress and fatigue. First, the correct set of sensors should be explored to understand how stress and fatigue metrics can be calculated. Secondly, optimization techniques need to be studied in order to, e.g. finds the correct machine's process parameterization that, on one hand, minimizes the operator's fatigue and stress, and on the other, do not jeopardizes the process requirements in terms of timing and quality. Therefore, this can be stated as a multivariate optimization problem

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 362)

This bibliography lists 357 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during May 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance