Techniques of machine learning (ML) find a rapidly increasing range of applications touching upon social, economic, and technological aspects of everyday life. They are also being used with great enthusiasm to fill in gaps in our scientific knowledge by data-based modelling approaches. I have followed these developments for a while with interest, concern, and mounting disappointment. When these technologies are employed to take over decisive functionality in safety-critical applications, we would like to exactly know how to guarantee their compliance with pre-defined guardrails and limitations. Moreover, when they are utilized as building blocks in scientific research, it would violate scientific standards -in my opinion- if these building blocks were used without a thorough understanding of their functionality, including inaccuracies, uncertainties, and other pitfalls.

In this context, I will juxtapose (a subset of) deep neural network methods with the family of entropy-optimal Sparse Probabilistic Approximation (eSPA/eSPA+) techniques developed recently by Illia Horenko (RPTU Kaiserslautern-Landau) and colleagues. If time permits I will also address their most recent extension of eSPA, called "Entropy-Optimal Networks" (EON).