Top programming languages for building AI

programing languages

Artificial intelligence (AI) is rapidly transforming our world, driving innovation across industries. But the brilliance behind these intelligent systems lies in the code that brings them to life. Choosing the right programming language is crucial for AI development, impacting everything from development speed to efficiency. This article explores the strengths and weaknesses of some of the most popular languages for building AI applications.

General-purpose languages might seem sufficient for any programming task. However, AI projects often involve complex mathematical operations, large datasets, and intricate algorithms. Here’s where specialized languages shine:

  • Rich Libraries and Frameworks: Many AI-focused languages boast extensive libraries and frameworks pre-equipped with functions for machine learning, natural language processing, and deep learning. These tools streamline development, saving time and resources.
  • Performance and Efficiency: AI applications often deal with massive data sets and require high-performance computing. Certain languages excel in speed and memory optimization, crucial for real-time AI tasks.
  • Readability and Maintainability: AI projects are iterative, and code clarity is paramount. Languages with clean syntax promote better understanding and easier collaboration among developers.

Now, let’s delve into the specifics of some of the leading languages used in AI development:

1. Lisp

Lisp, one of the oldest languages in AI, is renowned for its versatility in handling symbolic AI tasks. Its multi-paradigm nature, encompassing both functional and procedural programming, provides flexibility in expressing complex AI algorithms. Lisp’s homoiconic nature makes it particularly adept at dealing with large datasets, while its rich heritage in symbolic AI, with resources like Eurisko or CYC, continues to influence AI research and development.


  • Versatility in handling symbolic AI tasks.
  • Homoiconic nature facilitates manipulation of large datasets.
  • Rich heritage in symbolic AI, offering access to resources like Eurisko or CYC.


  • Unconventional syntax may pose challenges for newcomers.
  • Relatively limited adoption compared to mainstream languages.

2. C++

C++ stands out for its efficiency and speed, making it ideal for AI algorithms demanding computational power. With its procedural paradigm influenced by C and Algol 68, C++ offers fast execution times and compatibility with libraries tailored for AI, such as Alchemy for Markov logic and Mlpack for general machine learning (ML).


  • Efficiency and speed, suitable for computationally intensive AI tasks.
  • Compatibility with AI-specific libraries like Alchemy and Mlpack.


  • Steep learning curve may deter beginners.
  • Verbose syntax compared to higher-level languages.

3. C#

C# is a versatile language with a multi-paradigm approach, blending functional and procedural programming. Its ease of prototyping and well-elaborated environment make it a preferred choice for AI development. C# finds significant use in AI applications for gaming, thanks to its compatibility with popular game engines like Unity.


  • Versatility with a multi-paradigm approach.
  • Easy prototyping and well-elaborated environment.
  • Compatibility with popular game engines for AI development.


  • Microsoft-centric ecosystem may limit adoption in certain domains.

4. Clojure

Clojure, a functional language, offers a unique blend of features from Lisp, Erlang, and Prolog. Its easy design and cloud infrastructure, working seamlessly atop the JVM, make it well-suited for AI development. Rapid interactive development and libraries like alter-ego for behavior tree development enhance its appeal.


  • Easy design and cloud infrastructure on top of the JVM.
  • Rapid interactive development.
  • Libraries like alter-ego for behavior tree development.


  • Relatively new compared to mainstream languages, may lack extensive community support.

5. Erlang

Erlang shines in handling concurrency and elastic clouds, making it an excellent choice for scalable AI applications. Drawing inspiration from Lisp and Prolog, Erlang offers robust support for logic programming, with libraries like erlog enhancing its capabilities.


  • Robust framework for concurrency and elastic clouds.
  • Libraries like erlog for logic programming.


  • Syntax and learning curve may pose challenges for newcomers.
  • Relatively niche compared to mainstream languages.

6. Go

Go’s simplicity and built-in support for concurrency and asynchronous patterns make it well-suited for AI development. Its efficiency and runtime speed, along with libraries like Golearn for machine learning, contribute to its appeal in building AI applications.


  • Simplicity and ease of use.
  • Built-in support for concurrency.
  • Efficiency and runtime speed.


  • Relatively limited ecosystem for AI compared to more established languages.

7. Haskell

Haskell’s functional programming paradigm and ease of parallelization make it attractive for AI tasks. With utilities for implementing neural networks and general ML, Haskell offers a pragmatic approach to AI development.


  • Ease of parallelization.
  • Utilities for implementing neural networks and ML.


  • Abstract nature and academic focus may deter industry practitioners.
  • Relatively niche compared to mainstream languages.

8. Java

Java’s procedural concurrent paradigm, influenced by C++, Ada 83, among others, makes it a robust choice for AI development. With efficient maintainability, portability, and a plethora of AI libraries like Tweety and ML, Java remains a popular language in the AI community.


  • Efficient maintainability and portability.
  • Rich ecosystem of AI libraries like Tweety and ML.


  • Verbosity and ceremony may lead to more verbose code.
  • Relatively slower compared to lower-level languages.

9. Julia

Julia, with its multi-paradigm approach and seamless integration with C and Fortran, offers a scientific-oriented language for AI development. Its extensive ML packages like Mocha or MLBase enhance its suitability for AI tasks.


  • Seamless integration with C and Fortran.
  • Extensive ML packages.


  • Relatively young ecosystem may lack maturity compared to more established languages.

10. Lua

Lua’s versatility and lightweight nature make it a popular choice for AI development, particularly in frameworks like Torch for machine and deep learning. With influences from C++ and Scheme, Lua offers a flexible environment for AI experimentation.


  • Versatility and lightweight nature.
  • Widely used in frameworks like Torch for AI development.


  • Relatively limited scope compared to more feature-rich languages.

11. Matlab

Matlab’s multi-paradigm approach and focus on matrix and linear algebra make it suitable for AI tasks requiring numerical computation. With a solid integrated environment and a selection of toolboxes for machine learning, Matlab remains a preferred choice in academic and research settings.


  • Solid integrated environment.
  • Focus on matrix and linear algebra.
  • Selection of toolboxes for machine learning.


  • Licensing costs may be prohibitive for some users.
  • Limited scalability compared to open-source alternatives.

12. Prolog

Prolog’s unique approach to logic programming and symbolic computation makes it invaluable for certain AI tasks. With a rich set of utilities and internal functionalities for expressing relationships between objects, Prolog remains a niche but powerful language in the AI landscape.


  • Unique approach to logic programming.
  • Rich set of utilities for symbolic computation.


  • Limited applicability to certain AI tasks.
  • Relatively steep learning curve compared to more traditional languages.

13. Python

Python’s versatility, extensive libraries, and ease of use make it the go-to language for AI development. With a highly useful standard library and a plethora of frameworks for AI, ML, deep learning, and scientific computing, Python continues to dominate the AI landscape across industries.


  • Highly versatile and flexible.
  • Extensive libraries and frameworks for AI development.


  • Global interpreter lock (GIL) may hinder performance in multithreaded applications.
  • Slower execution speed compared to lower-level languages.

14. R

R stands out as a declarative language with a focus on statistical analysis and data visualization, making it a powerhouse for AI tasks involving data manipulation and modeling. With one of the most comprehensive sets of statistical analysis functions and packages, R enjoys widespread adoption in academia and industries reliant on data-driven decision-making.


  • Comprehensive sets of statistical analysis functions and packages.
  • Rich community of tools for AI or ML provided freely through the CRAN repository.


  • Performance may lag behind other languages for certain compute-intensive tasks.
  • Syntax and semantics may differ significantly from traditional programming languages, posing a learning curve for newcomers.

15. Scala

Scala, a multiparadigm language running on top of the JVM, combines elements of procedural and functional programming. Its fast runtime, almost comparable to C, along with robust support for distributed systems, makes it an attractive choice for AI tasks requiring scalability and performance.


  • Fast runtime, comparable to lower-level languages.
  • Excellent support for distributed systems.


  • Complexity may be higher compared to more straightforward languages.
  • Scala’s learning curve may be steep for newcomers, particularly those unfamiliar with functional programming concepts.


In the dynamic landscape of AI programming, the choice of language plays a crucial role in determining the success and efficiency of projects. Each language offers its unique blend of features, strengths, and challenges, catering to different aspects of AI development.

From the versatility of Python to the efficiency of C++, and the concurrency capabilities of Erlang, developers have a plethora of options to choose from based on project requirements, familiarity, and performance considerations. It’s essential to weigh the pros and cons of each language carefully, considering factors such as community support, ecosystem maturity, and alignment with project objectives.

Ultimately, the best language for AI development is one that aligns closely with the goals and constraints of the project, empowering developers to unleash the full potential of artificial intelligence and drive innovation in this rapidly evolving field.