Continual Learning (CL) is a novel AI paradigm in which tasks and data are made available over time; thus, the trained model is computed on the basis of a stream of data. CL-based approaches are able to learn new skills and knowledge without forgetting the previous ones, with no guaranteed access to previously encountered data, and mitigating the so-called “catastrophic forgetting” phenomenon. Interestingly, by making AI systems able to learn and improve over time without the need for large amounts of new data or computational resources, CL can help at reducing the impact of computationally-expensive and energy-intensive activities; hence, CL can play a key role in the path towards more green AIs, enabling more efficient and sustainable uses of resources. In this work, we describe different methods proposed in the literature to solve CL tasks; we survey different applications, highlighting strengths and weaknesses, with a particular focus on the biomedical context. Furthermore, we discuss how to make the methods more robust and suitable for a wider range of applications.

Artificial Intelligence plays a main role in supporting and improving smart manufacturing and Industry 4.0, by enabling the automation of different types of tasks manually performed by domain experts. In particular, assessing the compliance of a product with the relative schematic is a time-consuming and prone-to-error process. In this paper, we address this problem in a specific industrial scenario. In particular, we define a Neuro-Symbolic approach for automating the compliance verification of the electrical control panels. Our approach is based on the combination of Deep Learning techniques with Answer Set Programming (ASP), and allows for identifying possible anomalies and errors in the final product even when a very limited amount of training data is available. The experiments conducted on a real test case provided by an Italian Company operating in electrical control panel production demonstrate the effectiveness of the proposed approach.

Artificial Intelligence plays a main role in supporting and improving smart manufacturing and Industry 4.0, by enabling the automation of different types of tasks manually performed by domain experts. In particular, assessing the compliance of a product with the relative schematic is a time-consuming and prone-to-error process. In this paper, we address this problem in a specific industrial scenario. In particular, we define a Neuro-Symbolic approach for automating the compliance verification of the electrical control panels. Our approach is based on the combination of Deep Learning techniques with Answer Set Programming (ASP), and allows for identifying possible anomalies and errors in the final product even when a very limited amount of training data is available. The experiments conducted on a real test case provided by an Italian Company operating in electrical control panel production demonstrate the effectiveness of the proposed approach.