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What is Meta-Learning (ML)

Artificial Intelligence Paradigms for Smart Cyber-Physical Systems
Meta-learning is a subfield of machine learning, where automatic learning algorithms are applied to metadata related to machine learning experiments to learn (induce) the learning algorithm itself, how automatic learning can become flexible in solving learning problems.
Published in Chapter:
Explainable Artificial Intelligence (xAI) Approaches and Deep Meta-Learning Models for Cyber-Physical Systems
Evren Daglarli (Istanbul Technical University, Turkey)
Copyright: © 2021 |Pages: 26
DOI: 10.4018/978-1-7998-5101-1.ch003
Abstract
Today, the effects of promising technologies such as explainable artificial intelligence (xAI) and meta-learning (ML) on the internet of things (IoT) and the cyber-physical systems (CPS), which are important components of Industry 4.0, are increasingly intensified. However, there are important shortcomings that current deep learning models are currently inadequate. These artificial neural network based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable. For these reasons, it is necessary to make serious efforts on the explanability and interpretability of black box models. In the near future, the integration of explainable artificial intelligence and meta-learning approaches to cyber-physical systems will have effects on a high level of virtualization and simulation infrastructure, real-time supply chain, cyber factories with smart machines communicating over the internet, maximizing production efficiency, analysis of service quality and competition level.
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