I'd say it depends on what you mean by AI. Machine learning in general has seen some rapid evolution of tooling, so a number of algorithms for classification, clustering, and other forms of supervised and unsupervised learning, especially with probabilistic graphical models, have been implemented in Python, C#, Ruby, OCaml, and Java, just to name a few.
If you're doing large scale manipulation of data for building things like recommendation engines, collaborative filtering, or other types of unsupervised or supervised learning problems, you may want to take a look at Mahout. It's not really a "programming language" per se, but it's a set of tools for this kind of problem. You can write model code in Java, or other JVM languages like groovy (a dynamic, reasonably expressive language) or clojure (lisp-like).
I'm not sure why you'd consider Lisp dated; it's where most of the "new" language features in other languages (closures, etc.) originated from.
Of course, machine learning techniques have generally been moving toward probabilistic models than on the binary logic, decision-tree style approach that most early AI efforts started with, so it's possible to argue that machine learning is a branch or a diversion from the big tent of AI.