A Historical Review and Philosophical Examination of the two Paradigms in Artificial Intelligence Research

Youheng, Zhang

doi:10.24018/ejai.2023.2.2.23

Cited by 6 publications

(2 citation statements)

References 43 publications

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“…The advent of deep learning techniques allowed connectionist models to make more precise predictions through increased exposure to vast amounts of data the way that the human brain does (e.g., artificial neural networks). 8 Now, we can build larger, general models that learn context and meaning from neural networks (e.g., transformers). An example is Med‐PaLM, a large language model that can accurately answer medical questions by training on a massive dataset of medical text and code.…”

Section: Defining Ai‐generated Dts In Healthcarementioning

confidence: 99%

Increasing acceptance of AI‐generated digital twins through clinical trial applications

Vidovszky,

Fisher,

Loukianov

et al. 2024

Clinical Translational Sci

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Today's approach to medicine requires extensive trial and error to determine the proper treatment path for each patient. While many fields have benefited from technological breakthroughs in computer science, such as artificial intelligence (AI), the task of developing effective treatments is actually getting slower and more costly. With the increased availability of rich historical datasets from previous clinical trials and real‐world data sources, one can leverage AI models to create holistic forecasts of future health outcomes for an individual patient in the form of an AI‐generated digital twin. This could support the rapid evaluation of intervention strategies in silico and could eventually be implemented in clinical practice to make personalized medicine a reality. In this work, we focus on uses for AI‐generated digital twins of clinical trial participants and contend that the regulatory outlook for this technology within drug development makes it an ideal setting for the safe application of AI‐generated digital twins in healthcare. With continued research and growing regulatory acceptance, this path will serve to increase trust in this technology and provide momentum for the widespread adoption of AI‐generated digital twins in clinical practice.

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Section: Defining Ai‐generated Dts In Healthcarementioning

confidence: 99%

Increasing acceptance of AI‐generated digital twins through clinical trial applications

Vidovszky,

Fisher,

Loukianov

et al. 2024

Clinical Translational Sci

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“…As the learning is a capacity that could be used to show intelligence, the learning process of artificial intelligence differs from how humans learn (Youheng, 2023). This fact outlines a number of ways how artificial intelligence can be used in education and particularly during the scientific and pedagogical workers training.…”

Section: Dialecticismmentioning

confidence: 99%

Philosophy of the Future in the Context of Scientific and Pedagogical Workers Training and Artificial Intelligence Application

2023

Futurity Philosophy

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The article is aimed to introduce a conceptual and categorical framework of the future, to analyse the philosophical fundamentals of scientific and pedagogical workers training and artificial intelligence application, and to reveal applicable scenarios of establishment of efficient training in future in the context of application of artificial intelligence. Methodology: To conduct the research thoroughly we used mixed methodology. A scientific literature review was used in order to establish the theoretical basics of the investigation, to clarify the relevant ideas, and to develop the research methodology. During the investigation the most recent 41 papers devoted to the philosophy of future-focused education and application of artificial intelligence were selected to describe the problem accurately. To collect data the questionnaires, surveys and online data acquisition were applied. The research included three stages and lasted during September, 2021 – May, 2023. The research involved 119 scientific and pedagogical workers from institutions of higher education including instructors, post-graduate students and undergraduates. Results: The research interpreted the category artificial intelligence from the philosophical perspective, described the applications of artificial intelligence in the process of training of scientific and pedagogical workers at the institutions of higher education, and elaborated the model of artificial intelligence application while training scientific and pedagogical workers. Also, four future-focused scenarios to implement the model were outlined. Scientific Novelty: The study initially represents a unique perspective of philosophical interpretation of the future. Also, it brings original ideas to design the model of artificial intelligence application while training scientific and pedagogical workers that has not been done before. Conclusion: The model can be used for the improvement of scientific and pedagogical workers training at the institutions of higher education and prepare them to future transformation within the educational environment.

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Neuro-symbolic artificial intelligence: a survey

Bhuyan,

Ramdane-Cherif,

Tomar

et al. 2024

Neural Comput & Applic

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A Historical Review and Philosophical Examination of the two Paradigms in Artificial Intelligence Research

Cited by 6 publications

References 43 publications

Increasing acceptance of AI‐generated digital twins through clinical trial applications

Increasing acceptance of AI‐generated digital twins through clinical trial applications

Philosophy of the Future in the Context of Scientific and Pedagogical Workers Training and Artificial Intelligence Application

Neuro-symbolic artificial intelligence: a survey

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