“In computability theory, the halting problem is the problem of determining, from a description of an arbitrary computer program and an input, whether the program will finish running (i.e., halt) or continue to run forever.” – wikipedia

I think this is possible and quite important. If we rewrite the halting problem to a practical, still useful problem. Then “from a description” becomes from a small number of initial steps.

Then the problem could be fed to another machine learning model(). With indata like the model parameters, data from inital step calculations. Like the input, output and error.

Then the idea is that the practical halting model() will output a number indicating the probability of success.

If this works. The lessons is that even if something seems impossible you can rewrite the rules a bit but still get something useful back. Do we need to change the approach of the model().