Editor’s Note.

Artificial Intelligence (AI) is a scientific discipline that aims to drive disruptive scenarios for science-based technical developments that solve complex problems. The IJIMAI journal’s scope is precisely to demonstrate how the combination of two factors — technical foundations and sought-after applications — must guide future AI developments to find solutions to complex real-world problems. This IJIMAI publication opens with an article that considers the current framework for AI fundamentals: how can we improve AI technology to find solutions to real-unsolved problems? The initial answer seems to be related with a desired self-consistent procedure: let machines learn from our experience. In the article by Alotaibi et al., the analysis of neural networks in terms of the parameters used, how they work, and how do they respond to the problem itself led the authors to a rationale for decision-making regarding the performance of different neural models. The immediate question that arises is whether there are any universal and fundamental criteria that can be used to define the models that guide AI methods. Apparently, there are not such universal methods, and we are faced with a challenging open problem. Subsequent manuscripts will provide readers with more in-depth insights into this issue.