AI programming top languages need to learn

In the context of Artificial Intelligence, there are multiple languages you need to develop artificial applications. The below is the list of languages that are most popular in AI community.

Expert System

POP-11, Prolog, LISP


Dynamic Applications

LISP, Scheme, Dylan

Computer Vision


Natural Language Systems

Prolog, Oz

Evolutionary Systems


Artificial Intelligence data flow diagram
Artificial Intelligence data flow diagram

The definition of Expert system is In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert.[1]Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code.[2]

Dynamic applications – The applications which are really good and help to take business decisions based on the result provided by AI applications are called Dynamic applications.

Few real-life Dynamic applications in the context of AI

  • Finance professionals nimbly negotiate best supplier terms, while optimizing cash flow needs and balancing costs—especially during critical financial events such as the end of a quarter or during a high volume of payables.
  • Human Resources recruiters automatically identify the best candidates in the shortest time and help HR managers create job descriptions that will help candidates more efficiently find the best and most well-suited positions.
  • Supply Chain managers automatically find the best options to distribute goods around the world, while optimizing costs and price for both the buyers and the transporters to provide the best freight and transportation options for enterprise shippers.

Computer Vision-The applications that work based on Visual applications such as Image application and Video application:

Computer vision is an interdisciplinary field that deals with how computers can be made for gaining high-level understanding from digital images or videos. From the perspective of engineering, it seeks to automate tasks that the human visual system can do.

The Natural Language system – The applications, which are followed NLP called Natural language Systems. NLP is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way. By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.

Evolutionary Systems – per wiki In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms.

The best example for Evolutionary system is an application being researched for machine learning is optimal execution. When large trades are executed in the market, it could potentially push prices in an unfavourable direction, so it makes sense that traders are keen on optimising this cost. So far, most of the interest in applying machine learning technology to reduce trading costs has been from the buy side.

Final point is the languages that are most popular in the development of Artificial intelligence applications are LISP, Scheme, Prolog, Erlang, POP-11, Smalltalk. A good book read to understand better on AI applications Artificial Intelligence: A Systems Approach


Author: Srini

Experienced software developer. Skills in Development, Coding, Testing and Debugging. Good Data analytic skills (Data Warehousing and BI). Also skills in Mainframe.