Artificial intelligence is one of the most fascinating and rapidly growing fields in computer science. Not only are AI-related jobs growing in leaps and bounds, but many technical jobs now request AI knowledge as well. Emma White is a Business Development Manager at BairesDev with a background in tech company expansion through client base growth. White helps to expand BairesDev’s business at a global scale while managing new market research, overseeing growth projects, and generating leads.

Only then can you move on to considering which AI programming language offers the tools and libraries you need to meet your project’s goals. I respect no list of programming languages that doesn’t include F#. With the exception of C++ , F# blows away all of these languages, for AI and pretty much anything else. Also, Lisp consists of a macro system, a well-developed compiler that can produce efficient code, and a library of collection types, including hashtables and dynamic-size lists. It is another easy-to-learn language used for analysis, Big Data modeling, visualization, and forecasting.

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Starting with Python is easy because codes are more legible, concise, and straightforward. Python also has a large supportive community, with many users, collaborators and fans. AI is a high-tech field where the maximum level of knowledge is required.

Which is better for AI Java or Python?

AI developers prefer Python over Java because of its ease of use, accessibility and simplicity. Java has a better performance than Python but Python requires lesser code and can compile even when there are bugs in your code. On the other hand, Java handles concurrency better than Python.

But Python has become the lingua franca of data scientists and researchers for performing experiments in deep learning. If you’re just getting started with AI, I highly recommend learning Python. If you’ve done machine learning or data analysis projects, you’ve probably heard of the R programming language. R was originally developed by Ross Ihaka and Robert Gentleman in 1993 to implement the S programming language, which focuses on statistical computation and graphical modeling.

Other Popular Programming Languages for AI Programming

The Java community is rich and active, allowing developers to learn from each other and think up new AI solutions faster. The famous FaceApp and Google Assistant are both built with Java. Some libraries that can be used when developing an AI program with Java are Deep Java Library, KubeFlow, OpenNLP, Java Machine Learning Library, and Neuroph. Java is also a good choice, but it’s more challenging to learn. Other popular AI programming languages include Julia, Haskell, Lisp, R, JavaScript, C++, Prolog, and Scala.

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Below are eight programming languages well-suited for AI development. Deploying a powerful AI requires an extraordinary team of engineers. At Dirox, our developers are highly skilled in using different languages to build AI programs that will be most suitable for you. Aside from the proficient technical skills, developers at Dirox are also great communicators who prioritize collaboration to achieve better productivity and results.

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CLML , mgl, Antik and LLA are the popular AI and ML libraries offered by Lisp. The Servo parallel browser engine developed by Mozilla in collaboration with Samsung is written in Rust. The locus of Prolog’s expression lies in relationships between facts—otherwise known as rules. To solve a problem, the Prolog interpreter queries the facts and rules represented in a database. In other words, once the user provides the rules and a desired solution, Prolog solves for the rest. Those who have experience with Java or Python will quickly adapt to R’s basic syntax.

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Prolog’s built-in list handling is recursive, allowing for problem solving, analytics and overall improved application performance. Harikrishna Kundariya is a marketer, developer, IoT, ChatBot & Blockchain savvy designer, co-founder, and Director of eSparkBiz Technologies. His 10+ years of experience enables him to provide digital solutions to new start-ups based on IoT and ChatBot. With JavaScript, you can ensure security, high performance, and less development time. There are options like Scripting, OOPs approach, and IDE that allows fast development with diverse algorithms. Deploying one of the languages above in your tech stack is only a minor part of building competent AI software.


LISP works perfectly for artificial intelligence projects that are heavy on ML because of its rapid prototyping capability and its automatic garbage collection feature. Another advantage of the R programming language is its cross-platform compatibility. Even when R is performing tasks in its own language, ML engineers can mix it with other tools to optimize outcomes. Last but not least, R is a fairly easy programming language to learn. Whenever your application requires statistical analysis, such as processing biomedical statistics, R truly comes into its own. The language’s capability to apply the principles of statistics to big data makes it an excellent choice for training ML algorithms to predict future events.

Why Python is the best programming language for machine learning?

Python is a programming language that distinguishes itself from other programming languages by its flexibility, simplicity, and reliable tools required to create modern software. Python is consistent and is anchored on simplicity, which makes it most appropriate for machine learning.

Scaladex, a database of all Scala libraries, including those for artificial intelligence, will be available once you have learned Scala for AI. And we do it all with an eye towards optimizing development and reducing overhead costs. Let’s explore the advantages and diverse applications of the most popular AI programming languages. Developed in the early 1990s, Python has become one of the fastest-growing programming languages because of its scalability, adaptability, and ease of learning. The language overrides the complexities of 3D games, optimizing resource management and facilitating multiplayer with networking.

Which Programming Language Should I Learn First for Artificial Intelligence?

In AI development, data is crucial, so if you want to analyze and represent data accurately, things are going to get a bit mathematical. By 1962 and with the aid of creator John McCarthy, the language worked its way up to being capable of addressing problems of artificial intelligence. NLP is what smart assistants applications like Google and Alexa use to understand what you’re saying and respond appropriately.

If you are working on complex projects for big data applications, R should be on your list of top programming languages to learn for faster development. LISP or Lisp has a long history—it’s the second-oldest high-level programming language. It dates back to the 1950s when AI pioneer John McCarthy created it for AI programs like ELIZA, an early natural language processing computer program. Thanks to its adaptability, quick prototyping abilities, and capacity for dynamic object creation, it’s typically used in machine learning projects.


AI will not be replacing best ai language or programmers anytime soon but might perform coding and developing tasks in the future. Researchers and AI scientists believe that it will take time for AI to be able to create actual production-worthy and usable code that spans more than a few lines. You should know at least one of these programming languages if you want to build a career in AI. Industry experts believe that JS has several critical advantages over Python and R.

  • Deeplearning4j is a deep learning library that allows developers to build and train deep learning models in Java, which can be used in Scala as well.
  • Similarly, libraries like Keras, Caffe, and TensorFlow handle deep learning with finesse, keeping AI development with Python perfectly streamlined and easy.
  • That’s right, R was created by statisticians just for performing computations and crunch massive data sets with ease in a matter of seconds.
  • Java is the high-level programming language of choice for many and has been around since 1995.
  • It is not as popular for apps that are being developed entirely based on ML.
  • It is a wonderful option for creating machine learning algorithms since it can store and access data efficiently.