12 Best Artificial Intelligence Programming Language in 2023

Python also has a large variety of libraries for natural language processing, computer vision, and other specific AI tasks. Python is a high-level, interpreted programming language that is widely used for developing AI and machine learning applications. It is known for its simple and easy-to-read syntax, as well as its large and active community that provides a wealth of pre-built libraries and modules.

community of developers

R’s interactive environment is also ideal for rapid prototyping and experimentation with new problems. Although R is not a go-to programming language for deploying machine learning models in production, R is a top tool for exploratory work in the process of model selection. Haskell is a functional programming language based on the semantics of the Miranda programming language. Above all, Haskell delivers safety and speed in machine learning contexts. So those are the 8 most popular AI programming languages developers can learn and work with in 2022. C++ comes with limited but highly effective machine learning and deep learning libraries written in C++.

Top 5 most popular languages for Artificial Intelligence (AI) programming

Its notable feature is concise code, more readable and easier to write than other languages ​​like Java. It is valued among AI developers for its speed and efficiency. The language integrates with Java and JavaScript and provides error-free coding with easy debugging, ensuring a fast and convenient development process.

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Some languages are best for handling large amounts of data and complicated calculations, while others are strong in natural language planning. We, in APRO, use several modern languages and frameworks for our projects, choosing the most suitable options without limitations. We like to think that there is some sort of magic inside AI models.

How Much Programming Knowledge is Required to Learn ML?

If you’re stumped by a programming problem, a fellow Python developer will likely be eager to help. As a programmer, you should get to know the best languages for developing AI. Below are 10 options to consider and how they can benefit your smart projects. Used for automated planning, theorem proving, expert and type systems, Prolog still has limited usage. However, it is used to build some high-end NLP applications and by giants like IBM Watson. Just like Java, JavaScript is also an ideal match for AI development.

C++ is a general-purpose, cross-platform programming language created as an extension of C by Bjarne Stroustrup of Bell Laboratories in the early 1980s. C++ is also used for AI, although it is not the language of the first choice. C++ is an advantageous language as it translates user code into computer-readable machine code. You can use frameworks like TensorFlow and Caffe are written in C++ to build AI projects. Applications such as driverless cars, computer vision, and natural language processing have been able to develop using AI in recent years.

R programming language

Another task Prolog is particularly adept at is list-handling. Prolog’s built-in list handling is recursive, allowing for problem solving, analytics and overall improved application performance. Deploying one of the languages above in your tech stack is only a minor part of building competent AI software. Altogether, the theme of Haskell’s attractiveness for AI developers is that the language is efficient. Add in memory management, debugging, and metaprogramming to the mix, and you’ll soon understand what all the hype’s about. Yet, in practice,C++’s capacity for low-level programming makes it perfect for handling AI models in production.


C# was developed by Microsoft as part of the .NET framework and is primarily used for developing Windows desktop and web applications. C++ is a high-performance, general-purpose programming language that is widely used for developing a wide range of applications, including AI and machine learning. C++ is an extension of the C programming language, and it provides additional features such as classes, templates, and exception handling. The adoption of artificial intelligence and machine learning is growing at a fast pace. There are several programming languages used in AI and ML development.

Some general thoughts about programming languages

These sub-fields are based on technical considerations, such as particular goals, application domains , and/or particular implementation approaches . That’s why we give you the option to donate to us, and we will switch ads off for you. According to Payscale, the average salary for a Machine Learning Engineer with Python Skills was $112,178 as of 2022. Not only are AI-related jobs growing in leaps and bounds, but many technical jobs now request AI knowledge as well.


The language is being used by Google, meta, Dropbox, Walt Disney, and other household names. Its popular library of code and package services put it far above its current competition. To become a machine learning engineer, you need to know more than the basics of machine learning. Saying that, there is no need to learn all, or even a handful, of the existing programming languages. Most engineers in the field of machine learning focus on developing programming skills in one or maybe two programming languages and become experts in those. No-code language or base-level code software applications have become extremely popular over the past decade or so.

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Though it may come as a surprise, software engineering and writing go hand-in-hand. Cloud technologymakes it so chatbots have a whole store of data to access new and old information, meaning chatbots are worlds more intelligent than in the time of Prolog. You can chalk its innocent fame up to its dynamic interface and arresting graphics for data visualization.

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Knowing all of the components ahead of time will help you choose the best language. Making a sensible choice ahead of time will save you from having to patch things together later and spend more time maintaining, scaling, and securing the project later down the line. These languages, though not as commonly used, can provide great advantages depending on the nature of the projects you have in mind. C++ will be good in high-load services, where you need to process a lot of data, images, and videos very fast. You can find C++ inside stock exchange AI software and financial or bank services. It can be represented as a separate file that contains a structure of interconnections, coefficients, and ratios.

Why is Python Preferred for Artificial Intelligence Programming?

Python is preferred for AI programming because it is easy to learn and has a large community of developers. Quite a few AI platforms have been developed in Python—and it’s easier for non-programmers and scientists to understand.

Scala took the Java Virtual Machine environment and developed a better solution for programming intelligent software. It’s compatible with Java and JavaScript, while making the coding process easier, faster, and more productive. With Haskell, code reusability comes in handy for the developers along with other features like type system and memory management.

  • If you want to have control over runtime and performance, C++ is obviously a good choice here.
  • One of the biggest strengths is its interactive environment that facilitates rapid prototyping and exploratory model sections.
  • There are many popular AI programming languages, including Python, Java, Julia, Haskell, and Lisp.
  • This is the most popular programming language for AI, which can be read more about here, and it is always the top choice for developers.
  • To demonstrate solutions to customers quickly, you must know how to work with prototypes.
  • Few codebases and integrations are available for C++ because developers don’t use C++ as frequently as Python for AI development.

Thus, we want to tell you more about AI and machine learning. The functions work on many types of data, including numerical, categorical, time series, textual, and image. Smalltalk has been used extensively for simulations, neural networks, machine learning and genetic algorithms. It implements the purest and most elegant form of object-oriented programming using message passing. One of the main advantages of Java is its platform independence, meaning that Java code can run on a variety of different operating systems without modification. This makes it a good choice for developing AI applications that need to run on multiple platforms.

Which Language is Best for AI?

Python is the best language for AI. It is easy to learn, has a large community of developers, and has an extensive collection of frameworks, libraries, and codebases. However, Python has some criticisms—it can be slow, and its loose syntax may teach programmers bad habits.

Python is an independent and flexible language compatible with multiple platforms with minimum tweaks. The origin of Artificial Intelligence dates way back in time. So, it is safe to say that it is not an innovation in the year 2022.

  • They’re all widely used in the AI community, so you’ll be able to find plenty of resources and help online.
  • While the list goes on, there seems to be a unanimous agreement between firms, developers, and businesses that Python is the best language for AI development.
  • Python is an interpreted, high-level, and general-purpose programming language.
  • CLML , mgl, Antik and LLA are the popular AI and ML libraries offered by Lisp.
  • He created the first checkers program that could learn independently.
  • Julia is designed to deal with high-performance numerical analysis and computational science without the typical requirement of separate compilation.

In AI best languages for ai, data is crucial, so if you want to analyze and represent data accurately, things are going to get a bit mathematical. Its key feature is that you can use Java almost anywhere, on any platform, through its virtual machine. 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.