I know that a programming language is a tool, but the libraries of a language are very effective in doing a project. Programming languages like Lisp and Prolog were intended for artificial intelligence, but with the emergence of languages like Python, they have faded and even expired. In the AI and ML fields, is Rust-Lang useful?
Check out https://www.arewelearningyet.com/. It tries to capture the state of machine learning libraries written in and for the Rust ecosystem. They sum it up pretty well, I think:
It's ripe for experimentation, but the ecosystem isn't very complete yet.
The same topic occurs once a week on reddit these days
The TL;DR is that you can write robust and fast machine learning code (which is important for both training and inference) in Rust if you know what you are doing. I have done it multiple times, and it worked out just fine.
Lisp is still going strong.
Rust itself is great for AI and ML - but the ecosystem is a lot less mature.
Faster to pick up python because there is matplotlib, tensorflow and pytorch.
Thank you so much for your reply.
Are you meaning Common Lisp? If yes, then this language is for 29 years ago and how strong is it?
Any hope to see something like the Matplotlib, Tensorflow and Pytorch for Rust-Lang?
That's a popular Lisp - and similar age as python.
Clojure is also Lisp - a more recent offshoot (Java platform).
Would also look at Haskell if you are interested in functional languages - does have tensorflow/pytorch bindings.
Prolog was never a general purpose language - but it is also still used. However - that kind of logic inference is not what is behind the recent wave of AI products (ChatGPT etc).
I can't find the exact quote, but I think Jeff Bezanson once said that "Julia is a LISP". Anyway, Julia is littered with great ideas from the LISP world, most notably its metaprogramming capabilities. But also multiple dispatch which gives Julia and its standard library quite a unique feel (if you are not native to all the LISP dialects).
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