The current state of neural networks is somewhat analogous to how cathedrals were built in the medieval era. Master architects had a good understanding of then

The current state of neural networks is somewhat analogous to how cathedrals were built in the medieval era. Master architects had a good understanding of then-ancient geometry and applied that knowledge plus knowledge of arches from Roman times to create the great cathedrals found throughout Europe. The architects didn't have a deep understanding of material science (stone) and other forces that are taken into account in the modern era, but they had enough knowledge from experience and experimentation combined with relatively simple geometry to create strong structures that have stood the test of centuries. The analogy between that and modern practical AI/neural network implementations is that experts in the field have good basic knowledge of how to construct neural networks but there isn't yet an advanced theoretical knowledge of the science that allows for precise predictions and modeling. i.e. Experts are still at the level of experimentation and noting the results and getting useful results without a super-deep understanding of how neural networks produce useful results. Hypotheses have been/are being proposed and tested right now. Expect that as a deeper level of experimental results are achieved we'll soon have a sound theoretical model that will lead to profound advances in the field.