On the philosophical, cognitive and mathematical foundations of symbiotic autonomous systems

Wang, Yingxu; Karray, Fakhri; Plataniotis, Konstantinos N.; Leung, Henry; Hou, Ming; Tunstel, Edward; Rudas, Imre J.; Trajković, Ljiljana; Kaynak, Okyay; Kacprzyk, Janusz; Zhou, MengChu; Smith, Michael H.; Chen, Philip; Patel, Shushma

doi:10.1098/rsta.2020.0362

Cited by 24 publications

(17 citation statements)

References 47 publications

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“…An autonomous system is a group of components forming a unified whole with the power of acting independently from the external environment [77,78]. Today, autonomous vehicles and autonomous drones have become typical applications of such.…”

Section: Autonomous Systemsmentioning

confidence: 99%

Quo vadis artificial intelligence?

Jiang

Luo

et al. 2022

Discov Artif Intell

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235

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The study of artificial intelligence (AI) has been a continuous endeavor of scientists and engineers for over 65 years. The simple contention is that human-created machines can do more than just labor-intensive work; they can develop human-like intelligence. Being aware or not, AI has penetrated into our daily lives, playing novel roles in industry, healthcare, transportation, education, and many more areas that are close to the general public. AI is believed to be one of the major drives to change socio-economical lives. In another aspect, AI contributes to the advancement of state-of-the-art technologies in many fields of study, as helpful tools for groundbreaking research. However, the prosperity of AI as we witness today was not established smoothly. During the past decades, AI has struggled through historical stages with several winters. Therefore, at this juncture, to enlighten future development, it is time to discuss the past, present, and have an outlook on AI. In this article, we will discuss from a historical perspective how challenges were faced on the path of revolution of both the AI tools and the AI systems. Especially, in addition to the technical development of AI in the short to mid-term, thoughts and insights are also presented regarding the symbiotic relationship of AI and humans in the long run.

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Section: Autonomous Systemsmentioning

confidence: 99%

Quo vadis artificial intelligence?

Jiang

Luo

et al. 2022

Discov Artif Intell

Self Cite

235

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“…The fundamental theories of both AE and HP reveal the nature of COVID-19 and a rigorous approach to explain various practices in COVID-19 epidemiology and image diagnosis [6], [7]. They introduce mathematical rigor into COVID-19 pandemic analytics, which provides an indispensable framework for developing SP/ML models and algorithms for the manipulation of complex COVID-19 epidemiology and image processing.…”

Section: Definition 10mentioning

confidence: 99%

“…Recent studies [3]- [5] have shown that chest computed tomography (CT) scans and chest radiography (CXR) reveal informative features of COVID-19 that can assist in the monitoring, severity assessment, and treatment of COVID-19 [6], [7]. According to the guidelines provided by the WHO, the use of chest imaging as a complementary source of data is recommended in different scenarios and stages of COVID-19 to help radiologists and physicians detect and evaluate the disease more accurately.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis/Prognosis of COVID-19 Chest Images via Machine Learning and Hypersignal Processing: Challenges, opportunities, and applications

Mohammadi

Wang

Enshaei

et al. 2021

IEEE Signal Process. Mag.

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T he novel coronavirus disease, COVID-19, has rapidly and abruptly changed the world as we knew it in 2020. It has become the most unprecedented challenge to analytic epidemiology (AE) in general and signal processing (SP) theories specifically. In this regard, medical imaging plays an important role for the management of COVID-19. SP and deep learning (DL) models can assist in the development of robust radiomics solutions for the diagnosis/prognosis, severity assessment, treatment response, and monitoring of COVID-19 patients.We intend to present not only an overview of the current state, challenges, and opportunities of developing SP/ DL-empowered models for the diagnosis/prognosis of COVID-19 but also the latest developments in the theoretical framework of AE and hypersignal processing (HP) for COVID-19 from the points of view of both SP and medical/pandemic control professionals. The imaging modalities and radiological characteristics of COVID-19 are then discussed. SL/DL-based radiomic models specific to the analysis of COVID-19 infection are described covering four domains, which encompass the segmentation of COVID-19 lesions, models for outcome prediction, severity assessment, and diagnosis/classification models. This work leads to the identification of significant open problems and opportunities

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“…In the second article by Wang et al [2], the authors argue that SAS are advanced intelligent and cognitive systems that exhibit autonomous collective intelligence enabled by coherent symbiosis of human-machine interactions in hybrid societies. Basic research and engineering demands on SAS embody a contemporary philosophy of intelligence and knowledge sciences in the era from information to intelligence revolution, which have triggered advanced general-AI technologies, cognitive systems, deep knowledge learning underpinned by intelligent mathematics (IM).…”

Section: Introductionmentioning

confidence: 99%