Ten years ago Rudolf Winestock wrote The Lisp Curse, an essay that "attempt[ed] to reconcile the power of the Lisp programming language with the inability of the Lisp community to reproduce their pre- AI Winter achievements."
His conclusion? The power and expressiveness of Lisp have conspired to keep its developers individually productive, but collectively unable to organize their work into complete, standardized, well-documented, ‑tested, and ‑maintained packages that they could coalesce into interoperable and widely-adopted solutions. Everything from object systems to types to asynchronous non-blocking programming and concurrency is up for grabs and has multiple competing implementations.
5 Things to Know Before Starting an AI Project
Suppose you have an opportunity to create a project on AI. Consider these five stages before starting. These five are learning, programming language, knowledge representation, problem solving, and hardware.
1. Learning Process
Learning means adding new knowledge to the knowledge base and improving or refining previous knowledge.
Why I Picked Clojure
Clojure is a functional programming language that runs on the JVM (Java Virtual Machine), CLR (Common Language Runtime), and JavaScript platforms.
I was considering learning Lisp for a long time. The reason for this is that my lecturer, Chris Stephenson, had strong discourses on functional programming and was using Racket (a Lisp dialect) in some of his classes.