Lisp had an advantage when we believed AI was symbol manipulation and things like Ontologies.
Prolog had an advantage when we believed AI as logic, and Unification was the tricky operation.
But neither of these provide any advantage for any of the current contenders for "AI":
Statistical AI is about sparse arrays.
Neural networks of all kinds, including deep learning, is about oceans of nodes connected with links.
Model Free Methods (many kinds of machine learning, evolutionary methods, etc) are also very simple. The complexity is emergent, so you don't have to worry about it. Write a simple base that can learn what it needs to learn.
In either of these cases, any general purpose language will do. Arguments can even be made that most Neural Network approaches are so simple that C++ would be overkill.
Use the language that allows you to most easily hire the best programmers for the task.
