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AI Programming Languages for Seamless Digital Transformation


With multiple use-cases and high scope, Artificial Intelligence has become one of the technologies to transform business operations. According to Statista, the AI software market is expected to reach a revenue of 126 billion US dollars by 2025 from 34.84 billion US dollars in 2021. With each passing day, we see increasing competition over AI that makes it important for industries to come up with better strategies and technology stacks. To excel in the field, companies are tightening the reins over the latest and flexible programming languages that fit best for their AI-based software project.

Below is the list of the best AI programming languages. Every language has its own matrix of capabilities and shortcomings, therefore, choose the one that meets the requirements of your AI system.

1. Python

Python is a simple and ideal programming language used for Natural Language Processing (NLP), machine learning, and implementation of neural networks. When it comes to starting with Artificial Intelligence programming for beginners, Python is considered the most suitable language due to the following reasons:

  • Simple syntax & minimal code
  • Built-in core structure
  • Rapid development
  • Ready-to-use libraries (SciPy, nltk, Pandas, etc.)
  • Object-oriented programming
  • Straightforward integration
  • Custom AI development
  • Scalable open course support

Python language and IDEs are highly supportive of the development of high-level AI-based projects. It provides a high processing speed of complex code and faster testing algorithms. Some of its built-in libraries are helpful in the scientific computation of scalable projects. Moreover, with advanced use-cases and evolving requirements, python provides full support as it is open-source and available for any modifications by developers that seem necessary for an AI environment.


LISP is one of the oldest and first programming languages developed for AI systems. Leading companies and enterprises employ LISP for the development of complex AI systems for a very long time. Due to its precision and efficiency, even today LISP serves as the foundation of AI logic. LISP provides support for various programs for general problem solving which include:

  • Object-oriented approach
  • Schemas and frames
  • Data types
  • Lists
  • Adjustable arrays
  • Vectors
  • hash-tables
  • Association
  • Arguments
  • Cooperative multitasking
  • High-level debugging
  • Dynamic memory allocation
  • Generators and streams
  • Recursion

Due to its capability of processing symbolic information, LISP is considered one of the prominent languages among the AI programming languages list.


Artificial Intelligence Markup Language or AIML is an XML-based language that helps create AI applications. It is one of the top AI programming languages to create your own chatbots. AIML helps create:

  • Human interfaces
  • Easy-to-understand systems
  • High maintainable and scalable projects

4. R

R is one of those AI computer programming languages that provide support for the implementation of efficient statistical processes. The fusion of built-in packages and libraries in R produces state-of-the-art AI projects. For instance, there are some packages that fully support Machine Learning processes such as RODBC, Gmodels, Tm, OneR, etc. that when coupled with the R libraries such as Keras, MXNet, or TensorFlow, help solve complex real-world problems. Its implementation support is available for:

  • Advanced AI systems
  • Numerical analysis
  • Statistical computations
  • Neural networks
  • Vectorial computation
  • Bayesian inference
  • Functional programming

5. C++

C++ is one AI programming language used to develop high-speed AI systems. Its support for OpenCV for computer vision and building neural networks makes it a prominent language to use at an industrial level, however, some companies find alternatives as C++ has comparatively complex syntax. C++ helps develop AI applications that are

  • Cost-efficient
  • Consume low memory
  • High-speed
  • Asset-compelled

6. Java

Due to complete support of Java Virtual Machine, Java streamlines the implementation process that helps save energy and time of compiling the software program repeatedly. Java is considered for developing AI systems due to following reasons:

  • Flexible language
  • Scalability
  • Easy debugging
  • User-friendly language
  • Graphical analysis
  • Support for manifold libraries

7. Javascript

Javascript leaves behind Java when it comes to versatility. Javascript is used as one of the best AI programming languages to develop deep learning models and dynamic software systems. Its AI capabilities allow programmers to work efficiently with HTML and CSS and handle the functionality of both front-end and back-end. Other features of Javascript include:

  • High interactivity
  • Ease of use
  • Stability
  • Built-in libraries

8. Prolog

Prolog refers to “Programming in logic.” It is an exceptional way of approaching AI systems that undergo three factors: rules, sides, and facts to obtain the desired outcomes. All the requirements of an AI project are gathered against these three factors and the connection between them is identified to design a tailored AI solution. Prolog supports data structuring and backtracking. Prolog is specifically considered to develop:

  • Voice assistants
  • Chatbots
  • Unification and pattern matching modules
  • Graphical user interfaces (GUI)

9. Haskell

Haskell is one of the AI computer programming languages that is used to build AI systems with an advanced approach. It is considered one of the default programming languages for data science. It supports the development of high-scale neural networks due to the built-in memory that helps process various AI modules. It helps resolve errors at the time of compilation. Its features include:

  • Built-in memory support
  • High abstraction
  • High performance and scalability
  • Code reusability

10. Julia

Julia is an AI programming language suitable for the development of machine learning projects. With the support of several tools for graphical analysis and dynamic interface, Julia helps develop efficient AI systems with flexible debugging and metaprogramming approaches. It also supports the development of probabilistic programming models and algorithms.

11. Smalltalk

Smalltalk is an object-oriented AI programming language that makes computation easy in AI systems. Smalltalk is extensively used to build:

  • Genetic algorithms
  • Machine learning models
  • Neural networks

12. Planner

Planner is an AI programming language that provides support for the development of AI-based robotic systems. It is a hybrid of logical and procedural languages that help implement complex systems to train and test robots. Planner helps develop;

  • Logical sentences
  • Manipulating robotic models
  • Theorems
  • Hierarchical control structure

13. Scala

Scala supports the Java Virtual Machine (JVM) environment. It is considered one of the top AI programming languages that provide efficient solutions for intelligence systems. Its compatibility with Java and Javascript makes programming faster and easier. The features of Scala include:

  • Flexible interfaces
  • Browser tools
  • Built-in libraries and functions
  • Pattern matching

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