Why is Lisp AI suitable for each other?
I've heard before from computer scientists and from researchers in the area of AI that Lisp is a good language for research and development in artificial intelligence. Does this still apply, with the proliferation of neural networks and deep learning? What was their reasoning for this? What languages are current deep-learning systems currently built in?
David Nolen (contributor of Clojure and ClojureScript; creator of Core Logic a port of miniKanren) in a talk called LISP as too powerful stated that back in his days LISP was decades ahead of other programming languages. There are a number of reasons why the language wasn't able to maintain its initial reputation.
- This article highlights some key points why LISP AI is good for each other
- Easy to define a new language and manipulate complex information.
- Full flexibility in defining and manipulating programs as well as data.
- Fast, as the program is concise along with low-level detail.
- Good programming environment (debugging, incremental compilers, editors).
Most of my friends in this field usually use Matlab for Artificial Neural Networks and Machine Learning. It hides the low-level details though. If you are only looking for results and not how you get there, then Matlab will be good. But if you want to learn even low-level detailed stuff, then I will suggest you go through LISP at-least once. The language might not be that important if you have an understanding of various AI algorithms and techniques. I will suggest you read "Artificial Intelligence: A Modern Approach'' (by Stuart J. Russell and Peter Norvig". I am currently reading this book, and it's a very good book.