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Artificial intelligence based on neural networks has made significantprogress. However, there are concerns about the reliability and security ofthis approach due to its lack of transparency. This is the black box problem ofAI. Here we show how this problem can be solved using symbolic AI, which has atransparent white box nature. The widespread use of symbolic AI is hindered bythe opacity of mathematical models and natural language terms, the lack of aunified ontology, and the combinatorial explosion of search options. To solvethe AI black box problem and to implement general-purpose symbolic AI, wepropose to use deterministic logic cellular automata with rules based on firstprinciples of the general theory of the relevant domain. In this case, thegeneral theory of the relevant domain plays the role of a knowledge base forthe cellular automaton inference. A cellular automaton implements automaticparallel logical inference at three levels of organization of a complex system.Our verification of several ecological hypotheses provides a successfulprecedent for the implementation of white-box AI. Finally, we discuss a programfor creating a general-purpose symbolic AI capable of processing knowledge andensuring the reliability and safety of automated decisions.

A white box solution to the black box problem of AI